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https://github.com/cwinfo/yggdrasil-go.git
synced 2024-11-09 16:20:26 +00:00
update python sims
This commit is contained in:
parent
40ef1d7125
commit
015078a239
@ -1,5 +1,6 @@
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import glob
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inputDirPath = "fc00"
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import sys
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inputDirPath = sys.argv[1]
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inputFilePaths = glob.glob(inputDirPath+"/*")
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inputFilePaths.sort()
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@ -174,7 +174,7 @@ class Node:
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self.info.coords = self.root.path
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return changed
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def lookup_old(self, dest):
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def lookup(self, dest):
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# Note: Can loop in an unconverged network
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# The person looking up the route is responsible for checking for loops
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best = None
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@ -220,7 +220,7 @@ class Node:
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current = next
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return None
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def lookup(self, dest):
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def lookup_new(self, dest):
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# Use pre-computed lookup table to look up next hop for dest coords
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assert self.table
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if len(self.info.coords) >= 2: parent = self.info.coords[-2]
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@ -895,7 +895,8 @@ if __name__ == "__main__":
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if len(args) == 2:
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job_number = int(sys.argv[1])
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#rootNodeASTest("fc00-2017-08-12.txt", "fc00", None, job_number)
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rootNodeASTest("skitter", "out-skitter", None, job_number)
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#rootNodeASTest("skitter", "out-skitter", None, job_number)
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rootNodeASTest("walk-1517414401.txt.map", "out-walk", None, job_number)
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else:
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print "Usage: {} job_number".format(args[0])
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print "job_number = which job set to run on this node (1-indexed)"
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907
misc/sim/treesim-source-degree.py
Normal file
907
misc/sim/treesim-source-degree.py
Normal file
@ -0,0 +1,907 @@
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# Tree routing scheme (named Yggdrasil, after the world tree from Norse mythology)
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# Steps:
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# 1: Pick any node, here I'm using highest nodeID
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# 2: Build spanning tree, each node stores path back to root
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# Optionally with weights for each hop
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# Ties broken by preferring a parent with higher degree
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# 3: Distance metric: self->peer + (via tree) peer->dest
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# 4: Perform (modified) greedy lookup via this metric for each direction (A->B and B->A)
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# 5: Source-route traffic using the better of those two paths
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# Note: This makes no attempt to simulate a dynamic network
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# E.g. A node's peers cannot be disconnected
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# TODO:
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# Make better use of drop?
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# In particular, we should be ignoring *all* recently dropped *paths* to the root
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# To minimize route flapping
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# Not really an issue in the sim, but probably needed for a real network
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import array
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import gc
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import glob
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import gzip
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import heapq
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import os
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import random
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import time
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#############
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# Constants #
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#############
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# Reminder of where link cost comes in
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LINK_COST = 1
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# Timeout before dropping something, in simulated seconds
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TIMEOUT = 60
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###########
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# Classes #
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###########
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class PathInfo:
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def __init__(self, nodeID):
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self.nodeID = nodeID # e.g. IP
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self.coords = [] # Position in tree
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self.tstamp = 0 # Timestamp from sender, to keep track of old vs new info
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self.degree = 0 # Number of peers the sender has, used to break ties
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# The above should be signed
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self.path = [nodeID] # Path to node (in path-vector route)
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self.time = 0 # Time info was updated, to keep track of e.g. timeouts
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self.treeID = nodeID # Hack, let tree use different ID than IP, used so we can dijkstra once and test many roots
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def clone(self):
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# Return a deep-enough copy of the path
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clone = PathInfo(None)
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clone.nodeID = self.nodeID
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clone.coords = self.coords[:]
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clone.tstamp = self.tstamp
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clone.degree = self.degree
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clone.path = self.path[:]
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clone.time = self.time
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clone.treeID = self.treeID
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return clone
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# End class PathInfo
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class Node:
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def __init__(self, nodeID):
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self.info = PathInfo(nodeID) # Self NodeInfo
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self.root = None # PathInfo to node at root of tree
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self.drop = dict() # PathInfo to nodes from clus that have timed out
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self.peers = dict() # PathInfo to peers
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self.links = dict() # Links to peers (to pass messages)
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self.msgs = [] # Said messages
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self.table = dict() # Pre-computed lookup table of peer info
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def tick(self):
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# Do periodic maintenance stuff, including push updates
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self.info.time += 1
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if self.info.time > self.info.tstamp + TIMEOUT/4:
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# Update timestamp at least once every 1/4 timeout period
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# This should probably be randomized in a real implementation
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self.info.tstamp = self.info.time
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self.info.degree = len(self.peers)
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#self.info.degree = 0# TODO decide if degree should be used
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changed = False # Used to track when the network has converged
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changed |= self.cleanRoot()
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self.cleanDropped()
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# Should probably send messages infrequently if there's nothing new to report
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if self.info.tstamp == self.info.time:
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msg = self.createMessage()
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self.sendMessage(msg)
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return changed
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def cleanRoot(self):
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changed = False
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if self.root and self.info.time - self.root.time > TIMEOUT:
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print "DEBUG: clean root,", self.root.path
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self.drop[self.root.treeID] = self.root
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self.root = None
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changed = True
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if not self.root or self.root.treeID < self.info.treeID:
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# No need to drop someone who'se worse than us
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self.info.coords = [self.info.nodeID]
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self.root = self.info.clone()
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changed = True
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elif self.root.treeID == self.info.treeID:
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self.root = self.info.clone()
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return changed
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def cleanDropped(self):
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# May actually be a treeID... better to iterate over keys explicitly
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nodeIDs = sorted(self.drop.keys())
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for nodeID in nodeIDs:
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node = self.drop[nodeID]
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if self.info.time - node.time > 4*TIMEOUT:
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del self.drop[nodeID]
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return None
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def createMessage(self):
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# Message is just a tuple
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# First element is the sender
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# Second element is the root
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# We will .clone() everything during the send operation
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msg = (self.info, self.root)
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return msg
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def sendMessage(self, msg):
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for link in self.links.values():
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newMsg = (msg[0].clone(), msg[1].clone())
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link.msgs.append(newMsg)
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return None
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def handleMessages(self):
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changed = False
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while self.msgs:
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changed |= self.handleMessage(self.msgs.pop())
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return changed
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def handleMessage(self, msg):
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changed = False
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for node in msg:
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# Update the path and timestamp for the sender and root info
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node.path.append(self.info.nodeID)
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node.time = self.info.time
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# Update the sender's info in our list of peers
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sender = msg[0]
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self.peers[sender.nodeID] = sender
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# Decide if we want to update the root
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root = msg[1]
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updateRoot = False
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isSameParent = False
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isBetterParent = False
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if len(self.root.path) > 1 and len(root.path) > 1:
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parent = self.peers[self.root.path[-2]]
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if parent.nodeID == sender.nodeID: isSameParent = True
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if sender.degree > parent.degree:
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# This would also be where you check path uptime/reliability/whatever
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# All else being equal, we prefer parents with high degree
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# We are trusting peers to report degree correctly in this case
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# So expect some performance reduction if your peers aren't trustworthy
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# (Lies can increase average stretch by a few %)
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isBetterParent = True
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if self.info.nodeID in root.path[:-1]: pass # No loopy routes allowed
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elif root.treeID in self.drop and self.drop[root.treeID].tstamp >= root.tstamp: pass
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elif not self.root: updateRoot = True
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elif self.root.treeID < root.treeID: updateRoot = True
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elif self.root.treeID != root.treeID: pass
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elif self.root.tstamp > root.tstamp: pass
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elif len(root.path) < len(self.root.path): updateRoot = True
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elif isBetterParent and len(root.path) == len(self.root.path): updateRoot = True
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elif isSameParent and self.root.tstamp < root.tstamp: updateRoot = True
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if updateRoot:
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if not self.root or self.root.path != root.path: changed = True
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self.root = root
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self.info.coords = self.root.path
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return changed
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def lookup(self, dest):
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# Note: Can loop in an unconverged network
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# The person looking up the route is responsible for checking for loops
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best = None
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bestDist = 0
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bestDeg = 0
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for node in self.peers.itervalues():
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# dist = distance to node + dist (on tree) from node to dest
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dist = len(node.path)-1 + treeDist(node.coords, dest.coords)
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deg = node.degree
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if not best or dist < bestDist or (best == bestDist and deg > bestDeg):
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best = node
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bestDist = dist
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bestDeg = deg
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if best:
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next = best.path[-2]
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assert next in self.peers
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return next
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else:
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# We failed to look something up
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# TODO some way to signal this which doesn't crash
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assert False
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def initTable(self):
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# Pre-computes a lookup table for destination coords
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# Insert parent first so you prefer them as a next-hop
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self.table.clear()
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parent = self.info.nodeID
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if len(self.info.coords) >= 2: parent = self.info.coords[-2]
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for peer in self.peers.itervalues():
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current = self.table
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for coord in peer.coords:
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if coord not in current: current[coord] = (peer.nodeID, dict())
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old = current[coord]
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next = old[1]
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oldPeer = self.peers[old[0]]
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oldDist = len(oldPeer.coords)
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oldDeg = oldPeer.degree
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newDist = len(peer.coords)
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newDeg = peer.degree
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# Prefer parent
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# Else prefer short distance from root
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# If equal distance, prefer high degree
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if peer.nodeID == parent: current[coord] = (peer.nodeID, next)
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elif newDist < oldDist: current[coord] = (peer.nodeID, next)
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elif newDist == oldDist and newDeg > oldDeg: current[coord] = (peer.nodeID, next)
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current = next
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return None
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def lookup_new(self, dest):
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# Use pre-computed lookup table to look up next hop for dest coords
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assert self.table
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if len(self.info.coords) >= 2: parent = self.info.coords[-2]
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else: parent = None
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current = (parent, self.table)
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c = None
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for coord in dest.coords:
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c = coord
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if coord not in current[1]: break
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current = current[1][coord]
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next = current[0]
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if c in self.peers: next = c
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if next not in self.peers:
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assert next == None
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# You're the root of a different connected component
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# You'd drop the packet in this case
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# To make the path cache not die, need to return a valid next hop...
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# Returning self for that reason
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next = self.info.nodeID
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return next
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# End class Node
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####################
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# Helper Functions #
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####################
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def getIndexOfLCA(source, dest):
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# Return index of last common ancestor in source/dest coords
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# -1 if no common ancestor (e.g. different roots)
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lcaIdx = -1
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minLen = min(len(source), len(dest))
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for idx in xrange(minLen):
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if source[idx] == dest[idx]: lcaIdx = idx
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else: break
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return lcaIdx
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def treePath(source, dest):
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# Return path with source at head and dest at tail
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lastMatch = getIndexOfLCA(source, dest)
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path = dest[-1:lastMatch:-1] + source[lastMatch:]
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assert path[0] == dest[-1]
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assert path[-1] == source[-1]
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return path
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def treeDist(source, dest):
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dist = len(source) + len(dest)
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lcaIdx = getIndexOfLCA(source, dest)
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dist -= 2*(lcaIdx+1)
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return dist
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def dijkstra(nodestore, startingNodeID):
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# Idea to use heapq and basic implementation taken from stackexchange post
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# http://codereview.stackexchange.com/questions/79025/dijkstras-algorithm-in-python
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nodeIDs = sorted(nodestore.keys())
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nNodes = len(nodeIDs)
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idxs = dict()
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for nodeIdx in xrange(nNodes):
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nodeID = nodeIDs[nodeIdx]
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idxs[nodeID] = nodeIdx
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dists = array.array("H", [0]*nNodes)
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queue = [(0, startingNodeID)]
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while queue:
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dist, nodeID = heapq.heappop(queue)
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idx = idxs[nodeID]
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if not dists[idx]: # Unvisited, otherwise we skip it
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dists[idx] = dist
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for peer in nodestore[nodeID].links:
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if not dists[idxs[peer]]:
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# Peer is also unvisited, so add to queue
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heapq.heappush(queue, (dist+LINK_COST, peer))
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return dists
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def dijkstrall(nodestore):
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# Idea to use heapq and basic implementation taken from stackexchange post
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# http://codereview.stackexchange.com/questions/79025/dijkstras-algorithm-in-python
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nodeIDs = sorted(nodestore.keys())
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nNodes = len(nodeIDs)
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idxs = dict()
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for nodeIdx in xrange(nNodes):
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nodeID = nodeIDs[nodeIdx]
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idxs[nodeID] = nodeIdx
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dists = array.array("H", [0]*nNodes*nNodes) # use GetCacheIndex(nNodes, start, end)
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for sourceIdx in xrange(nNodes):
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print "Finding shortest paths for node {} / {} ({})".format(sourceIdx+1, nNodes, nodeIDs[sourceIdx])
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queue = [(0, sourceIdx)]
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while queue:
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dist, nodeIdx = heapq.heappop(queue)
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distIdx = getCacheIndex(nNodes, sourceIdx, nodeIdx)
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if not dists[distIdx]: # Unvisited, otherwise we skip it
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dists[distIdx] = dist
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for peer in nodestore[nodeIDs[nodeIdx]].links:
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pIdx = idxs[peer]
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pdIdx = getCacheIndex(nNodes, sourceIdx, pIdx)
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if not dists[pdIdx]:
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# Peer is also unvisited, so add to queue
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heapq.heappush(queue, (dist+LINK_COST, pIdx))
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return dists
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def linkNodes(node1, node2):
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node1.links[node2.info.nodeID] = node2
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node2.links[node1.info.nodeID] = node1
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############################
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# Store topology functions #
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############################
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def makeStoreSquareGrid(sideLength, randomize=True):
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# Simple grid in a sideLength*sideLength square
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# Just used to validate that the code runs
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store = dict()
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nodeIDs = list(range(sideLength*sideLength))
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if randomize: random.shuffle(nodeIDs)
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for nodeID in nodeIDs:
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store[nodeID] = Node(nodeID)
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for index in xrange(len(nodeIDs)):
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if (index % sideLength != 0): linkNodes(store[nodeIDs[index]], store[nodeIDs[index-1]])
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if (index >= sideLength): linkNodes(store[nodeIDs[index]], store[nodeIDs[index-sideLength]])
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print "Grid store created, size {}".format(len(store))
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return store
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def makeStoreASRelGraph(pathToGraph):
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#Existing network graphs, in caida.org's asrel format (ASx|ASy|z per line, z denotes relationship type)
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with open(pathToGraph, "r") as f:
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inData = f.readlines()
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store = dict()
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for line in inData:
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if line.strip()[0] == "#": continue # Skip comment lines
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line = line.replace('|'," ")
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nodes = map(int, line.split()[0:2])
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if nodes[0] not in store: store[nodes[0]] = Node(nodes[0])
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if nodes[1] not in store: store[nodes[1]] = Node(nodes[1])
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linkNodes(store[nodes[0]], store[nodes[1]])
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print "CAIDA AS-relation graph successfully imported, size {}".format(len(store))
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return store
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def makeStoreASRelGraphMaxDeg(pathToGraph, degIdx=0):
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with open(pathToGraph, "r") as f:
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inData = f.readlines()
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store = dict()
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nodeDeg = dict()
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for line in inData:
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if line.strip()[0] == "#": continue # Skip comment lines
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line = line.replace('|'," ")
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nodes = map(int, line.split()[0:2])
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if nodes[0] not in nodeDeg: nodeDeg[nodes[0]] = 0
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if nodes[1] not in nodeDeg: nodeDeg[nodes[1]] = 0
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nodeDeg[nodes[0]] += 1
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nodeDeg[nodes[1]] += 1
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sortedNodes = sorted(nodeDeg.keys(), \
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key=lambda x: (nodeDeg[x], x), \
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reverse=True)
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maxDegNodeID = sortedNodes[degIdx]
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return makeStoreASRelGraphFixedRoot(pathToGraph, maxDegNodeID)
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def makeStoreASRelGraphFixedRoot(pathToGraph, rootNodeID):
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with open(pathToGraph, "r") as f:
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inData = f.readlines()
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store = dict()
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for line in inData:
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if line.strip()[0] == "#": continue # Skip comment lines
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line = line.replace('|'," ")
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nodes = map(int, line.split()[0:2])
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if nodes[0] not in store:
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store[nodes[0]] = Node(nodes[0])
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if nodes[0] == rootNodeID: store[nodes[0]].info.treeID += 1000000000
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if nodes[1] not in store:
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store[nodes[1]] = Node(nodes[1])
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if nodes[1] == rootNodeID: store[nodes[1]].info.treeID += 1000000000
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linkNodes(store[nodes[0]], store[nodes[1]])
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||||
print "CAIDA AS-relation graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
def makeStoreDimesEdges(pathToGraph, rootNodeID=None):
|
||||
# Read from a DIMES csv-formatted graph from a gzip file
|
||||
store = dict()
|
||||
with gzip.open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
size = len(inData)
|
||||
index = 0
|
||||
for edge in inData:
|
||||
if not index % 1000:
|
||||
pct = 100.0*index/size
|
||||
print "Processing edge {}, {:.2f}%".format(index, pct)
|
||||
index += 1
|
||||
dat = edge.rstrip().split(',')
|
||||
node1 = "N" + str(dat[0].strip())
|
||||
node2 = "N" + str(dat[1].strip())
|
||||
if '?' in node1 or '?' in node2: continue #Unknown node
|
||||
if node1 == rootNodeID: node1 = "R" + str(dat[0].strip())
|
||||
if node2 == rootNodeID: node2 = "R" + str(dat[1].strip())
|
||||
if node1 not in store: store[node1] = Node(node1)
|
||||
if node2 not in store: store[node2] = Node(node2)
|
||||
if node1 != node2: linkNodes(store[node1], store[node2])
|
||||
print "DIMES graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
def makeStoreGeneratedGraph(pathToGraph, root=None):
|
||||
with open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
store = dict()
|
||||
for line in inData:
|
||||
if line.strip()[0] == "#": continue # Skip comment lines
|
||||
nodes = map(int, line.strip().split(' ')[0:2])
|
||||
node1 = nodes[0]
|
||||
node2 = nodes[1]
|
||||
if node1 == root: node1 += 1000000
|
||||
if node2 == root: node2 += 1000000
|
||||
if node1 not in store: store[node1] = Node(node1)
|
||||
if node2 not in store: store[node2] = Node(node2)
|
||||
linkNodes(store[node1], store[node2])
|
||||
print "Generated graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
|
||||
############################################
|
||||
# Functions used as parts of network tests #
|
||||
############################################
|
||||
|
||||
def idleUntilConverged(store):
|
||||
nodeIDs = sorted(store.keys())
|
||||
timeOfLastChange = 0
|
||||
step = 0
|
||||
# Idle until the network has converged
|
||||
while step - timeOfLastChange < 4*TIMEOUT:
|
||||
step += 1
|
||||
print "Step: {}, last change: {}".format(step, timeOfLastChange)
|
||||
changed = False
|
||||
for nodeID in nodeIDs:
|
||||
# Update node status, send messages
|
||||
changed |= store[nodeID].tick()
|
||||
for nodeID in nodeIDs:
|
||||
# Process messages
|
||||
changed |= store[nodeID].handleMessages()
|
||||
if changed: timeOfLastChange = step
|
||||
initTables(store)
|
||||
return store
|
||||
|
||||
def getCacheIndex(nodes, sourceIndex, destIndex):
|
||||
return sourceIndex*nodes + destIndex
|
||||
|
||||
def initTables(store):
|
||||
nodeIDs = sorted(store.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
print "Initializing routing tables for {} nodes".format(nNodes)
|
||||
for idx in xrange(nNodes):
|
||||
nodeID = nodeIDs[idx]
|
||||
store[nodeID].initTable()
|
||||
print "Routing tables initialized"
|
||||
return None
|
||||
|
||||
def getCache(store):
|
||||
nodeIDs = sorted(store.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
nodeIdxs = dict()
|
||||
for nodeIdx in xrange(nNodes):
|
||||
nodeIdxs[nodeIDs[nodeIdx]] = nodeIdx
|
||||
cache = array.array("H", [0]*nNodes*nNodes)
|
||||
for sourceIdx in xrange(nNodes):
|
||||
sourceID = nodeIDs[sourceIdx]
|
||||
print "Building fast lookup table for node {} / {} ({})".format(sourceIdx+1, nNodes, sourceID)
|
||||
for destIdx in xrange(nNodes):
|
||||
destID = nodeIDs[destIdx]
|
||||
if sourceID == destID: nextHop = destID # lookup would fail
|
||||
else: nextHop = store[sourceID].lookup(store[destID].info)
|
||||
nextHopIdx = nodeIdxs[nextHop]
|
||||
cache[getCacheIndex(nNodes, sourceIdx, destIdx)] = nextHopIdx
|
||||
return cache
|
||||
|
||||
def testPaths(store, dists):
|
||||
cache = getCache(store)
|
||||
nodeIDs = sorted(store.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
idxs = dict()
|
||||
for nodeIdx in xrange(nNodes):
|
||||
nodeID = nodeIDs[nodeIdx]
|
||||
idxs[nodeID] = nodeIdx
|
||||
results = dict()
|
||||
for sourceIdx in xrange(nNodes):
|
||||
sourceID = nodeIDs[sourceIdx]
|
||||
print "Testing paths from node {} / {} ({})".format(sourceIdx+1, len(nodeIDs), sourceID)
|
||||
#dists = dijkstra(store, sourceID)
|
||||
for destIdx in xrange(nNodes):
|
||||
destID = nodeIDs[destIdx]
|
||||
if destID == sourceID: continue # Skip self
|
||||
distIdx = getCacheIndex(nNodes, sourceIdx, destIdx)
|
||||
eHops = dists[distIdx]
|
||||
if not eHops: continue # The network is split, no path exists
|
||||
hops = 0
|
||||
for pair in ((sourceIdx, destIdx), (destIdx, sourceIdx)): # Either direction because source routing
|
||||
nHops = 0
|
||||
locIdx = pair[0]
|
||||
dIdx = pair[1]
|
||||
while locIdx != dIdx:
|
||||
locIdx = cache[getCacheIndex(nNodes, locIdx, dIdx)]
|
||||
nHops += 1
|
||||
if not hops or nHops < hops: hops = nHops
|
||||
if eHops not in results: results[eHops] = dict()
|
||||
if hops not in results[eHops]: results[eHops][hops] = 0
|
||||
results[eHops][hops] += 1
|
||||
return results
|
||||
|
||||
def getAvgStretch(pathMatrix):
|
||||
avgStretch = 0.
|
||||
checked = 0.
|
||||
for eHops in sorted(pathMatrix.keys()):
|
||||
for nHops in sorted(pathMatrix[eHops].keys()):
|
||||
count = pathMatrix[eHops][nHops]
|
||||
stretch = float(nHops)/float(max(1, eHops))
|
||||
avgStretch += stretch*count
|
||||
checked += count
|
||||
avgStretch /= max(1, checked)
|
||||
return avgStretch
|
||||
|
||||
def getMaxStretch(pathMatrix):
|
||||
maxStretch = 0.
|
||||
for eHops in sorted(pathMatrix.keys()):
|
||||
for nHops in sorted(pathMatrix[eHops].keys()):
|
||||
stretch = float(nHops)/float(max(1, eHops))
|
||||
maxStretch = max(maxStretch, stretch)
|
||||
return maxStretch
|
||||
|
||||
def getCertSizes(store):
|
||||
# Returns nCerts frequency distribution
|
||||
# De-duplicates common certs (for shared prefixes in the path)
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
certs = set()
|
||||
for peer in node.peers.values():
|
||||
pCerts = set()
|
||||
assert len(peer.path) == 2
|
||||
assert peer.coords[-1] == peer.path[0]
|
||||
hops = peer.coords + peer.path[1:]
|
||||
for hopIdx in xrange(len(hops)-1):
|
||||
send = hops[hopIdx]
|
||||
if send == node.info.nodeID: continue # We created it, already have it
|
||||
path = hops[0:hopIdx+2]
|
||||
# Each cert is signed by the sender
|
||||
# Includes information about the path from the sender to the next hop
|
||||
# Next hop is at hopIdx+1, so the path to next hop is hops[0:hopIdx+2]
|
||||
cert = "{}:{}".format(send, path)
|
||||
certs.add(cert)
|
||||
size = len(certs)
|
||||
if size not in sizes: sizes[size] = 0
|
||||
sizes[size] += 1
|
||||
return sizes
|
||||
|
||||
def getMinLinkCertSizes(store):
|
||||
# Returns nCerts frequency distribution
|
||||
# De-duplicates common certs (for shared prefixes in the path)
|
||||
# Based on the minimum number of certs that must be traded through a particular link
|
||||
# Handled per link
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
peerCerts = dict()
|
||||
for peer in node.peers.values():
|
||||
pCerts = set()
|
||||
assert len(peer.path) == 2
|
||||
assert peer.coords[-1] == peer.path[0]
|
||||
hops = peer.coords + peer.path[1:]
|
||||
for hopIdx in xrange(len(hops)-1):
|
||||
send = hops[hopIdx]
|
||||
if send == node.info.nodeID: continue # We created it, already have it
|
||||
path = hops[0:hopIdx+2]
|
||||
# Each cert is signed by the sender
|
||||
# Includes information about the path from the sender to the next hop
|
||||
# Next hop is at hopIdx+1, so the path to next hop is hops[0:hopIdx+2]
|
||||
cert = "{}:{}".format(send, path)
|
||||
pCerts.add(cert)
|
||||
peerCerts[peer.nodeID] = pCerts
|
||||
for peer in peerCerts:
|
||||
size = 0
|
||||
pCerts = peerCerts[peer]
|
||||
for cert in pCerts:
|
||||
required = True
|
||||
for p2 in peerCerts:
|
||||
if p2 == peer: continue
|
||||
p2Certs = peerCerts[p2]
|
||||
if cert in p2Certs: required = False
|
||||
if required: size += 1
|
||||
if size not in sizes: sizes[size] = 0
|
||||
sizes[size] += 1
|
||||
return sizes
|
||||
|
||||
def getPathSizes(store):
|
||||
# Returns frequency distribution of the total number of hops the routing table
|
||||
# I.e. a node with 3 peers, each with 5 hop coord+path, would count as 3x5=15
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
size = 0
|
||||
for peer in node.peers.values():
|
||||
assert len(peer.path) == 2
|
||||
assert peer.coords[-1] == peer.path[0]
|
||||
peerSize = len(peer.coords) + len(peer.path) - 1 # double-counts peer, -1
|
||||
size += peerSize
|
||||
if size not in sizes: sizes[size] = 0
|
||||
sizes[size] += 1
|
||||
return sizes
|
||||
|
||||
def getPeerSizes(store):
|
||||
# Returns frequency distribution of the number of peers each node has
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
nPeers = len(node.peers)
|
||||
if nPeers not in sizes: sizes[nPeers] = 0
|
||||
sizes[nPeers] += 1
|
||||
return sizes
|
||||
|
||||
def getAvgSize(sizes):
|
||||
sumSizes = 0
|
||||
nNodes = 0
|
||||
for size in sizes:
|
||||
count = sizes[size]
|
||||
sumSizes += size*count
|
||||
nNodes += count
|
||||
avgSize = float(sumSizes)/max(1, nNodes)
|
||||
return avgSize
|
||||
|
||||
def getMaxSize(sizes):
|
||||
return max(sizes.keys())
|
||||
|
||||
def getMinSize(sizes):
|
||||
return min(sizes.keys())
|
||||
|
||||
def getResults(pathMatrix):
|
||||
results = []
|
||||
for eHops in sorted(pathMatrix.keys()):
|
||||
for nHops in sorted(pathMatrix[eHops].keys()):
|
||||
count = pathMatrix[eHops][nHops]
|
||||
results.append("{} {} {}".format(eHops, nHops, count))
|
||||
return '\n'.join(results)
|
||||
|
||||
####################################
|
||||
# Functions to run different tests #
|
||||
####################################
|
||||
|
||||
def runTest(store):
|
||||
# Runs the usual set of tests on the store
|
||||
# Does not save results, so only meant for quick tests
|
||||
# To e.g. check the code works, maybe warm up the pypy jit
|
||||
for node in store.values():
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Begin testing network"
|
||||
dists = None
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
peers = getPeerSizes(store)
|
||||
certs = getCertSizes(store)
|
||||
paths = getPathSizes(store)
|
||||
linkCerts = getMinLinkCertSizes(store)
|
||||
avgPeerSize = getAvgSize(peers)
|
||||
maxPeerSize = getMaxSize(peers)
|
||||
avgCertSize = getAvgSize(certs)
|
||||
maxCertSize = getMaxSize(certs)
|
||||
avgPathSize = getAvgSize(paths)
|
||||
maxPathSize = getMaxSize(paths)
|
||||
avgLinkCert = getAvgSize(linkCerts)
|
||||
maxLinkCert = getMaxSize(linkCerts)
|
||||
totalCerts = sum(map(lambda x: x*certs[x], certs.keys()))
|
||||
totalLinks = sum(map(lambda x: x*peers[x], peers.keys())) # one-way links
|
||||
avgCertsPerLink = float(totalCerts)/max(1, totalLinks)
|
||||
print "Finished testing network"
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
print "Avg / Max nPeers size: {} / {}".format(avgPeerSize, maxPeerSize)
|
||||
print "Avg / Max nCerts size: {} / {}".format(avgCertSize, maxCertSize)
|
||||
print "Avg / Max total hops in any node's routing table: {} / {}".format(avgPathSize, maxPathSize)
|
||||
print "Avg / Max lower bound cert requests per link (one-way): {} / {}".format(avgLinkCert, maxLinkCert)
|
||||
print "Avg certs per link (one-way): {}".format(avgCertsPerLink)
|
||||
return # End of function
|
||||
|
||||
def rootNodeASTest(path, outDir="output-treesim-AS", dists=None, proc = 1):
|
||||
# Checks performance for every possible choice of root node
|
||||
# Saves output for each root node to a separate file on disk
|
||||
# path = input path to some caida.org formatted AS-relationship graph
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
store = makeStoreASRelGraph(path)
|
||||
nodes = sorted(store.keys())
|
||||
for nodeIdx in xrange(len(nodes)):
|
||||
if nodeIdx % proc != 0: continue # Work belongs to someone else
|
||||
rootNodeID = nodes[nodeIdx]
|
||||
outpath = outDir+"/{}".format(rootNodeID)
|
||||
if os.path.exists(outpath):
|
||||
print "Skipping {}, already processed".format(rootNodeID)
|
||||
continue
|
||||
store = makeStoreASRelGraphFixedRoot(path, rootNodeID)
|
||||
for node in store.values():
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(nodeIdx, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished test for root AS {} ({} / {})".format(rootNodeID, nodeIdx+1, len(store))
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
#break # Stop after 1, because they can take forever
|
||||
return # End of function
|
||||
|
||||
def timelineASTest():
|
||||
# Meant to study the performance of the network as a function of network size
|
||||
# Loops over a set of AS-relationship graphs
|
||||
# Runs a test on each graph, selecting highest-degree node as the root
|
||||
# Saves results for each graph to a separate file on disk
|
||||
outDir = "output-treesim-timeline-AS"
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
paths = sorted(glob.glob("asrel/datasets/*"))
|
||||
for path in paths:
|
||||
date = os.path.basename(path).split(".")[0]
|
||||
outpath = outDir+"/{}".format(date)
|
||||
if os.path.exists(outpath):
|
||||
print "Skipping {}, already processed".format(date)
|
||||
continue
|
||||
store = makeStoreASRelGraphMaxDeg(path)
|
||||
dists = None
|
||||
for node in store.values():
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(date, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished {} with {} nodes".format(date, len(store))
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
#break # Stop after 1, because they can take forever
|
||||
return # End of function
|
||||
|
||||
def timelineDimesTest():
|
||||
# Meant to study the performance of the network as a function of network size
|
||||
# Loops over a set of AS-relationship graphs
|
||||
# Runs a test on each graph, selecting highest-degree node as the root
|
||||
# Saves results for each graph to a separate file on disk
|
||||
outDir = "output-treesim-timeline-dimes"
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
# Input files are named ASEdgesX_Y where X = month (no leading 0), Y = year
|
||||
paths = sorted(glob.glob("DIMES/ASEdges/*.gz"))
|
||||
exists = set(glob.glob(outDir+"/*"))
|
||||
for path in paths:
|
||||
date = os.path.basename(path).split(".")[0]
|
||||
outpath = outDir+"/{}".format(date)
|
||||
if outpath in exists:
|
||||
print "Skipping {}, already processed".format(date)
|
||||
continue
|
||||
store = makeStoreDimesEdges(path)
|
||||
# Get the highest degree node and make it root
|
||||
# Sorted by nodeID just to make it stable in the event of a tie
|
||||
nodeIDs = sorted(store.keys())
|
||||
bestRoot = ""
|
||||
bestDeg = 0
|
||||
for nodeID in nodeIDs:
|
||||
node = store[nodeID]
|
||||
if len(node.links) > bestDeg:
|
||||
bestRoot = nodeID
|
||||
bestDeg = len(node.links)
|
||||
assert bestRoot
|
||||
store = makeStoreDimesEdges(path, bestRoot)
|
||||
rootID = "R" + bestRoot[1:]
|
||||
assert rootID in store
|
||||
# Don't forget to set random seed before setitng times
|
||||
# To make results reproducible
|
||||
nodeIDs = sorted(store.keys())
|
||||
random.seed(12345)
|
||||
for nodeID in nodeIDs:
|
||||
node = store[nodeID]
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(date, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished {} with {} nodes".format(date, len(store))
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
break # Stop after 1, because they can take forever
|
||||
return # End of function
|
||||
|
||||
def scalingTest(maxTests=None, inputDir="graphs"):
|
||||
# Meant to study the performance of the network as a function of network size
|
||||
# Loops over a set of nodes in a previously generated graph
|
||||
# Runs a test on each graph, testing each node as the root
|
||||
# if maxTests is set, tests only that number of roots (highest degree first)
|
||||
# Saves results for each graph to a separate file on disk
|
||||
outDir = "output-treesim-{}".format(inputDir)
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
paths = sorted(glob.glob("{}/*".format(inputDir)))
|
||||
exists = set(glob.glob(outDir+"/*"))
|
||||
for path in paths:
|
||||
gc.collect() # pypy waits for gc to close files
|
||||
graph = os.path.basename(path).split(".")[0]
|
||||
store = makeStoreGeneratedGraph(path)
|
||||
# Get the highest degree node and make it root
|
||||
# Sorted by nodeID just to make it stable in the event of a tie
|
||||
nodeIDs = sorted(store.keys(), key=lambda x: len(store[x].links), reverse=True)
|
||||
dists = None
|
||||
if maxTests: nodeIDs = nodeIDs[:maxTests]
|
||||
for nodeID in nodeIDs:
|
||||
nodeIDStr = str(nodeID).zfill(len(str(len(store)-1)))
|
||||
outpath = outDir+"/{}-{}".format(graph, nodeIDStr)
|
||||
if outpath in exists:
|
||||
print "Skipping {}-{}, already processed".format(graph, nodeIDStr)
|
||||
continue
|
||||
store = makeStoreGeneratedGraph(path, nodeID)
|
||||
# Don't forget to set random seed before setting times
|
||||
random.seed(12345) # To make results reproducible
|
||||
nIDs = sorted(store.keys())
|
||||
for nID in nIDs:
|
||||
node = store[nID]
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(graph, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished {} with {} nodes for root {}".format(graph, len(store), nodeID)
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
return # End of function
|
||||
|
||||
##################
|
||||
# Main Execution #
|
||||
##################
|
||||
|
||||
if __name__ == "__main__":
|
||||
if True: # Run a quick test
|
||||
random.seed(12345) # DEBUG
|
||||
store = makeStoreSquareGrid(4)
|
||||
runTest(store) # Quick test
|
||||
store = None
|
||||
# Do some real work
|
||||
#runTest(makeStoreDimesEdges("DIMES/ASEdges/ASEdges1_2007.csv.gz"))
|
||||
#timelineDimesTest()
|
||||
#rootNodeASTest("asrel/datasets/19980101.as-rel.txt")
|
||||
#timelineASTest()
|
||||
#rootNodeASTest("hype-2016-09-19.list", "output-treesim-hype")
|
||||
#scalingTest(None, "graphs-20") # First argument 1 to only test 1 root per graph
|
||||
#store = makeStoreGeneratedGraph("bgp_tables")
|
||||
#store = makeStoreGeneratedGraph("skitter")
|
||||
#store = makeStoreASRelGraphMaxDeg("hype-2016-09-19.list") #http://hia.cjdns.ca/watchlist/c/walk.peers.20160919
|
||||
#store = makeStoreGeneratedGraph("fc00-2017-08-12.txt")
|
||||
if store: runTest(store)
|
||||
#rootNodeASTest("skitter", "output-treesim-skitter", None, 0, 1)
|
||||
#scalingTest(1, "graphs-20") # First argument 1 to only test 1 root per graph
|
||||
#scalingTest(1, "graphs-21") # First argument 1 to only test 1 root per graph
|
||||
#scalingTest(1, "graphs-22") # First argument 1 to only test 1 root per graph
|
||||
#scalingTest(1, "graphs-23") # First argument 1 to only test 1 root per graph
|
||||
if not store:
|
||||
import sys
|
||||
args = sys.argv
|
||||
if len(args) == 2:
|
||||
job_number = int(sys.argv[1])
|
||||
#rootNodeASTest("fc00-2017-08-12.txt", "fc00", None, job_number)
|
||||
#rootNodeASTest("skitter", "out-skitter", None, job_number)
|
||||
rootNodeASTest("walk-1517414401.txt.map", "out-walk", None, job_number)
|
||||
else:
|
||||
print "Usage: {} job_number".format(args[0])
|
||||
print "job_number = which job set to run on this node (1-indexed)"
|
||||
|
907
misc/sim/treesim-source.py
Normal file
907
misc/sim/treesim-source.py
Normal file
@ -0,0 +1,907 @@
|
||||
# Tree routing scheme (named Yggdrasil, after the world tree from Norse mythology)
|
||||
# Steps:
|
||||
# 1: Pick any node, here I'm using highest nodeID
|
||||
# 2: Build spanning tree, each node stores path back to root
|
||||
# Optionally with weights for each hop
|
||||
# Ties broken by preferring a parent with higher degree
|
||||
# 3: Distance metric: self->peer + (via tree) peer->dest
|
||||
# 4: Perform (modified) greedy lookup via this metric for each direction (A->B and B->A)
|
||||
# 5: Source-route traffic using the better of those two paths
|
||||
|
||||
# Note: This makes no attempt to simulate a dynamic network
|
||||
# E.g. A node's peers cannot be disconnected
|
||||
|
||||
# TODO:
|
||||
# Make better use of drop?
|
||||
# In particular, we should be ignoring *all* recently dropped *paths* to the root
|
||||
# To minimize route flapping
|
||||
# Not really an issue in the sim, but probably needed for a real network
|
||||
|
||||
import array
|
||||
import gc
|
||||
import glob
|
||||
import gzip
|
||||
import heapq
|
||||
import os
|
||||
import random
|
||||
import time
|
||||
|
||||
#############
|
||||
# Constants #
|
||||
#############
|
||||
|
||||
# Reminder of where link cost comes in
|
||||
LINK_COST = 1
|
||||
|
||||
# Timeout before dropping something, in simulated seconds
|
||||
TIMEOUT = 60
|
||||
|
||||
###########
|
||||
# Classes #
|
||||
###########
|
||||
|
||||
class PathInfo:
|
||||
def __init__(self, nodeID):
|
||||
self.nodeID = nodeID # e.g. IP
|
||||
self.coords = [] # Position in tree
|
||||
self.tstamp = 0 # Timestamp from sender, to keep track of old vs new info
|
||||
self.degree = 0 # Number of peers the sender has, used to break ties
|
||||
# The above should be signed
|
||||
self.path = [nodeID] # Path to node (in path-vector route)
|
||||
self.time = 0 # Time info was updated, to keep track of e.g. timeouts
|
||||
self.treeID = nodeID # Hack, let tree use different ID than IP, used so we can dijkstra once and test many roots
|
||||
def clone(self):
|
||||
# Return a deep-enough copy of the path
|
||||
clone = PathInfo(None)
|
||||
clone.nodeID = self.nodeID
|
||||
clone.coords = self.coords[:]
|
||||
clone.tstamp = self.tstamp
|
||||
clone.degree = self.degree
|
||||
clone.path = self.path[:]
|
||||
clone.time = self.time
|
||||
clone.treeID = self.treeID
|
||||
return clone
|
||||
# End class PathInfo
|
||||
|
||||
class Node:
|
||||
def __init__(self, nodeID):
|
||||
self.info = PathInfo(nodeID) # Self NodeInfo
|
||||
self.root = None # PathInfo to node at root of tree
|
||||
self.drop = dict() # PathInfo to nodes from clus that have timed out
|
||||
self.peers = dict() # PathInfo to peers
|
||||
self.links = dict() # Links to peers (to pass messages)
|
||||
self.msgs = [] # Said messages
|
||||
self.table = dict() # Pre-computed lookup table of peer info
|
||||
|
||||
def tick(self):
|
||||
# Do periodic maintenance stuff, including push updates
|
||||
self.info.time += 1
|
||||
if self.info.time > self.info.tstamp + TIMEOUT/4:
|
||||
# Update timestamp at least once every 1/4 timeout period
|
||||
# This should probably be randomized in a real implementation
|
||||
self.info.tstamp = self.info.time
|
||||
self.info.degree = len(self.peers)
|
||||
self.info.degree = 0# TODO decide if degree should be used
|
||||
changed = False # Used to track when the network has converged
|
||||
changed |= self.cleanRoot()
|
||||
self.cleanDropped()
|
||||
# Should probably send messages infrequently if there's nothing new to report
|
||||
if self.info.tstamp == self.info.time:
|
||||
msg = self.createMessage()
|
||||
self.sendMessage(msg)
|
||||
return changed
|
||||
|
||||
def cleanRoot(self):
|
||||
changed = False
|
||||
if self.root and self.info.time - self.root.time > TIMEOUT:
|
||||
print "DEBUG: clean root,", self.root.path
|
||||
self.drop[self.root.treeID] = self.root
|
||||
self.root = None
|
||||
changed = True
|
||||
if not self.root or self.root.treeID < self.info.treeID:
|
||||
# No need to drop someone who'se worse than us
|
||||
self.info.coords = [self.info.nodeID]
|
||||
self.root = self.info.clone()
|
||||
changed = True
|
||||
elif self.root.treeID == self.info.treeID:
|
||||
self.root = self.info.clone()
|
||||
return changed
|
||||
|
||||
def cleanDropped(self):
|
||||
# May actually be a treeID... better to iterate over keys explicitly
|
||||
nodeIDs = sorted(self.drop.keys())
|
||||
for nodeID in nodeIDs:
|
||||
node = self.drop[nodeID]
|
||||
if self.info.time - node.time > 4*TIMEOUT:
|
||||
del self.drop[nodeID]
|
||||
return None
|
||||
|
||||
def createMessage(self):
|
||||
# Message is just a tuple
|
||||
# First element is the sender
|
||||
# Second element is the root
|
||||
# We will .clone() everything during the send operation
|
||||
msg = (self.info, self.root)
|
||||
return msg
|
||||
|
||||
def sendMessage(self, msg):
|
||||
for link in self.links.values():
|
||||
newMsg = (msg[0].clone(), msg[1].clone())
|
||||
link.msgs.append(newMsg)
|
||||
return None
|
||||
|
||||
def handleMessages(self):
|
||||
changed = False
|
||||
while self.msgs:
|
||||
changed |= self.handleMessage(self.msgs.pop())
|
||||
return changed
|
||||
|
||||
def handleMessage(self, msg):
|
||||
changed = False
|
||||
for node in msg:
|
||||
# Update the path and timestamp for the sender and root info
|
||||
node.path.append(self.info.nodeID)
|
||||
node.time = self.info.time
|
||||
# Update the sender's info in our list of peers
|
||||
sender = msg[0]
|
||||
self.peers[sender.nodeID] = sender
|
||||
# Decide if we want to update the root
|
||||
root = msg[1]
|
||||
updateRoot = False
|
||||
isSameParent = False
|
||||
isBetterParent = False
|
||||
if len(self.root.path) > 1 and len(root.path) > 1:
|
||||
parent = self.peers[self.root.path[-2]]
|
||||
if parent.nodeID == sender.nodeID: isSameParent = True
|
||||
if sender.degree > parent.degree:
|
||||
# This would also be where you check path uptime/reliability/whatever
|
||||
# All else being equal, we prefer parents with high degree
|
||||
# We are trusting peers to report degree correctly in this case
|
||||
# So expect some performance reduction if your peers aren't trustworthy
|
||||
# (Lies can increase average stretch by a few %)
|
||||
isBetterParent = True
|
||||
if self.info.nodeID in root.path[:-1]: pass # No loopy routes allowed
|
||||
elif root.treeID in self.drop and self.drop[root.treeID].tstamp >= root.tstamp: pass
|
||||
elif not self.root: updateRoot = True
|
||||
elif self.root.treeID < root.treeID: updateRoot = True
|
||||
elif self.root.treeID != root.treeID: pass
|
||||
elif self.root.tstamp > root.tstamp: pass
|
||||
elif len(root.path) < len(self.root.path): updateRoot = True
|
||||
elif isBetterParent and len(root.path) == len(self.root.path): updateRoot = True
|
||||
elif isSameParent and self.root.tstamp < root.tstamp: updateRoot = True
|
||||
if updateRoot:
|
||||
if not self.root or self.root.path != root.path: changed = True
|
||||
self.root = root
|
||||
self.info.coords = self.root.path
|
||||
return changed
|
||||
|
||||
def lookup(self, dest):
|
||||
# Note: Can loop in an unconverged network
|
||||
# The person looking up the route is responsible for checking for loops
|
||||
best = None
|
||||
bestDist = 0
|
||||
bestDeg = 0
|
||||
for node in self.peers.itervalues():
|
||||
# dist = distance to node + dist (on tree) from node to dest
|
||||
dist = len(node.path)-1 + treeDist(node.coords, dest.coords)
|
||||
deg = node.degree
|
||||
if not best or dist < bestDist or (best == bestDist and deg > bestDeg):
|
||||
best = node
|
||||
bestDist = dist
|
||||
bestDeg = deg
|
||||
if best:
|
||||
next = best.path[-2]
|
||||
assert next in self.peers
|
||||
return next
|
||||
else:
|
||||
# We failed to look something up
|
||||
# TODO some way to signal this which doesn't crash
|
||||
assert False
|
||||
|
||||
def initTable(self):
|
||||
# Pre-computes a lookup table for destination coords
|
||||
# Insert parent first so you prefer them as a next-hop
|
||||
self.table.clear()
|
||||
parent = self.info.nodeID
|
||||
if len(self.info.coords) >= 2: parent = self.info.coords[-2]
|
||||
for peer in self.peers.itervalues():
|
||||
current = self.table
|
||||
for coord in peer.coords:
|
||||
if coord not in current: current[coord] = (peer.nodeID, dict())
|
||||
old = current[coord]
|
||||
next = old[1]
|
||||
oldPeer = self.peers[old[0]]
|
||||
oldDist = len(oldPeer.coords)
|
||||
oldDeg = oldPeer.degree
|
||||
newDist = len(peer.coords)
|
||||
newDeg = peer.degree
|
||||
# Prefer parent
|
||||
# Else prefer short distance from root
|
||||
# If equal distance, prefer high degree
|
||||
if peer.nodeID == parent: current[coord] = (peer.nodeID, next)
|
||||
elif newDist < oldDist: current[coord] = (peer.nodeID, next)
|
||||
elif newDist == oldDist and newDeg > oldDeg: current[coord] = (peer.nodeID, next)
|
||||
current = next
|
||||
return None
|
||||
|
||||
def lookup_new(self, dest):
|
||||
# Use pre-computed lookup table to look up next hop for dest coords
|
||||
assert self.table
|
||||
if len(self.info.coords) >= 2: parent = self.info.coords[-2]
|
||||
else: parent = None
|
||||
current = (parent, self.table)
|
||||
c = None
|
||||
for coord in dest.coords:
|
||||
c = coord
|
||||
if coord not in current[1]: break
|
||||
current = current[1][coord]
|
||||
next = current[0]
|
||||
if c in self.peers: next = c
|
||||
if next not in self.peers:
|
||||
assert next == None
|
||||
# You're the root of a different connected component
|
||||
# You'd drop the packet in this case
|
||||
# To make the path cache not die, need to return a valid next hop...
|
||||
# Returning self for that reason
|
||||
next = self.info.nodeID
|
||||
return next
|
||||
# End class Node
|
||||
|
||||
####################
|
||||
# Helper Functions #
|
||||
####################
|
||||
|
||||
def getIndexOfLCA(source, dest):
|
||||
# Return index of last common ancestor in source/dest coords
|
||||
# -1 if no common ancestor (e.g. different roots)
|
||||
lcaIdx = -1
|
||||
minLen = min(len(source), len(dest))
|
||||
for idx in xrange(minLen):
|
||||
if source[idx] == dest[idx]: lcaIdx = idx
|
||||
else: break
|
||||
return lcaIdx
|
||||
|
||||
def treePath(source, dest):
|
||||
# Return path with source at head and dest at tail
|
||||
lastMatch = getIndexOfLCA(source, dest)
|
||||
path = dest[-1:lastMatch:-1] + source[lastMatch:]
|
||||
assert path[0] == dest[-1]
|
||||
assert path[-1] == source[-1]
|
||||
return path
|
||||
|
||||
def treeDist(source, dest):
|
||||
dist = len(source) + len(dest)
|
||||
lcaIdx = getIndexOfLCA(source, dest)
|
||||
dist -= 2*(lcaIdx+1)
|
||||
return dist
|
||||
|
||||
def dijkstra(nodestore, startingNodeID):
|
||||
# Idea to use heapq and basic implementation taken from stackexchange post
|
||||
# http://codereview.stackexchange.com/questions/79025/dijkstras-algorithm-in-python
|
||||
nodeIDs = sorted(nodestore.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
idxs = dict()
|
||||
for nodeIdx in xrange(nNodes):
|
||||
nodeID = nodeIDs[nodeIdx]
|
||||
idxs[nodeID] = nodeIdx
|
||||
dists = array.array("H", [0]*nNodes)
|
||||
queue = [(0, startingNodeID)]
|
||||
while queue:
|
||||
dist, nodeID = heapq.heappop(queue)
|
||||
idx = idxs[nodeID]
|
||||
if not dists[idx]: # Unvisited, otherwise we skip it
|
||||
dists[idx] = dist
|
||||
for peer in nodestore[nodeID].links:
|
||||
if not dists[idxs[peer]]:
|
||||
# Peer is also unvisited, so add to queue
|
||||
heapq.heappush(queue, (dist+LINK_COST, peer))
|
||||
return dists
|
||||
|
||||
def dijkstrall(nodestore):
|
||||
# Idea to use heapq and basic implementation taken from stackexchange post
|
||||
# http://codereview.stackexchange.com/questions/79025/dijkstras-algorithm-in-python
|
||||
nodeIDs = sorted(nodestore.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
idxs = dict()
|
||||
for nodeIdx in xrange(nNodes):
|
||||
nodeID = nodeIDs[nodeIdx]
|
||||
idxs[nodeID] = nodeIdx
|
||||
dists = array.array("H", [0]*nNodes*nNodes) # use GetCacheIndex(nNodes, start, end)
|
||||
for sourceIdx in xrange(nNodes):
|
||||
print "Finding shortest paths for node {} / {} ({})".format(sourceIdx+1, nNodes, nodeIDs[sourceIdx])
|
||||
queue = [(0, sourceIdx)]
|
||||
while queue:
|
||||
dist, nodeIdx = heapq.heappop(queue)
|
||||
distIdx = getCacheIndex(nNodes, sourceIdx, nodeIdx)
|
||||
if not dists[distIdx]: # Unvisited, otherwise we skip it
|
||||
dists[distIdx] = dist
|
||||
for peer in nodestore[nodeIDs[nodeIdx]].links:
|
||||
pIdx = idxs[peer]
|
||||
pdIdx = getCacheIndex(nNodes, sourceIdx, pIdx)
|
||||
if not dists[pdIdx]:
|
||||
# Peer is also unvisited, so add to queue
|
||||
heapq.heappush(queue, (dist+LINK_COST, pIdx))
|
||||
return dists
|
||||
|
||||
def linkNodes(node1, node2):
|
||||
node1.links[node2.info.nodeID] = node2
|
||||
node2.links[node1.info.nodeID] = node1
|
||||
|
||||
############################
|
||||
# Store topology functions #
|
||||
############################
|
||||
|
||||
def makeStoreSquareGrid(sideLength, randomize=True):
|
||||
# Simple grid in a sideLength*sideLength square
|
||||
# Just used to validate that the code runs
|
||||
store = dict()
|
||||
nodeIDs = list(range(sideLength*sideLength))
|
||||
if randomize: random.shuffle(nodeIDs)
|
||||
for nodeID in nodeIDs:
|
||||
store[nodeID] = Node(nodeID)
|
||||
for index in xrange(len(nodeIDs)):
|
||||
if (index % sideLength != 0): linkNodes(store[nodeIDs[index]], store[nodeIDs[index-1]])
|
||||
if (index >= sideLength): linkNodes(store[nodeIDs[index]], store[nodeIDs[index-sideLength]])
|
||||
print "Grid store created, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
def makeStoreASRelGraph(pathToGraph):
|
||||
#Existing network graphs, in caida.org's asrel format (ASx|ASy|z per line, z denotes relationship type)
|
||||
with open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
store = dict()
|
||||
for line in inData:
|
||||
if line.strip()[0] == "#": continue # Skip comment lines
|
||||
line = line.replace('|'," ")
|
||||
nodes = map(int, line.split()[0:2])
|
||||
if nodes[0] not in store: store[nodes[0]] = Node(nodes[0])
|
||||
if nodes[1] not in store: store[nodes[1]] = Node(nodes[1])
|
||||
linkNodes(store[nodes[0]], store[nodes[1]])
|
||||
print "CAIDA AS-relation graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
def makeStoreASRelGraphMaxDeg(pathToGraph, degIdx=0):
|
||||
with open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
store = dict()
|
||||
nodeDeg = dict()
|
||||
for line in inData:
|
||||
if line.strip()[0] == "#": continue # Skip comment lines
|
||||
line = line.replace('|'," ")
|
||||
nodes = map(int, line.split()[0:2])
|
||||
if nodes[0] not in nodeDeg: nodeDeg[nodes[0]] = 0
|
||||
if nodes[1] not in nodeDeg: nodeDeg[nodes[1]] = 0
|
||||
nodeDeg[nodes[0]] += 1
|
||||
nodeDeg[nodes[1]] += 1
|
||||
sortedNodes = sorted(nodeDeg.keys(), \
|
||||
key=lambda x: (nodeDeg[x], x), \
|
||||
reverse=True)
|
||||
maxDegNodeID = sortedNodes[degIdx]
|
||||
return makeStoreASRelGraphFixedRoot(pathToGraph, maxDegNodeID)
|
||||
|
||||
def makeStoreASRelGraphFixedRoot(pathToGraph, rootNodeID):
|
||||
with open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
store = dict()
|
||||
for line in inData:
|
||||
if line.strip()[0] == "#": continue # Skip comment lines
|
||||
line = line.replace('|'," ")
|
||||
nodes = map(int, line.split()[0:2])
|
||||
if nodes[0] not in store:
|
||||
store[nodes[0]] = Node(nodes[0])
|
||||
if nodes[0] == rootNodeID: store[nodes[0]].info.treeID += 1000000000
|
||||
if nodes[1] not in store:
|
||||
store[nodes[1]] = Node(nodes[1])
|
||||
if nodes[1] == rootNodeID: store[nodes[1]].info.treeID += 1000000000
|
||||
linkNodes(store[nodes[0]], store[nodes[1]])
|
||||
print "CAIDA AS-relation graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
def makeStoreDimesEdges(pathToGraph, rootNodeID=None):
|
||||
# Read from a DIMES csv-formatted graph from a gzip file
|
||||
store = dict()
|
||||
with gzip.open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
size = len(inData)
|
||||
index = 0
|
||||
for edge in inData:
|
||||
if not index % 1000:
|
||||
pct = 100.0*index/size
|
||||
print "Processing edge {}, {:.2f}%".format(index, pct)
|
||||
index += 1
|
||||
dat = edge.rstrip().split(',')
|
||||
node1 = "N" + str(dat[0].strip())
|
||||
node2 = "N" + str(dat[1].strip())
|
||||
if '?' in node1 or '?' in node2: continue #Unknown node
|
||||
if node1 == rootNodeID: node1 = "R" + str(dat[0].strip())
|
||||
if node2 == rootNodeID: node2 = "R" + str(dat[1].strip())
|
||||
if node1 not in store: store[node1] = Node(node1)
|
||||
if node2 not in store: store[node2] = Node(node2)
|
||||
if node1 != node2: linkNodes(store[node1], store[node2])
|
||||
print "DIMES graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
def makeStoreGeneratedGraph(pathToGraph, root=None):
|
||||
with open(pathToGraph, "r") as f:
|
||||
inData = f.readlines()
|
||||
store = dict()
|
||||
for line in inData:
|
||||
if line.strip()[0] == "#": continue # Skip comment lines
|
||||
nodes = map(int, line.strip().split(' ')[0:2])
|
||||
node1 = nodes[0]
|
||||
node2 = nodes[1]
|
||||
if node1 == root: node1 += 1000000
|
||||
if node2 == root: node2 += 1000000
|
||||
if node1 not in store: store[node1] = Node(node1)
|
||||
if node2 not in store: store[node2] = Node(node2)
|
||||
linkNodes(store[node1], store[node2])
|
||||
print "Generated graph successfully imported, size {}".format(len(store))
|
||||
return store
|
||||
|
||||
|
||||
############################################
|
||||
# Functions used as parts of network tests #
|
||||
############################################
|
||||
|
||||
def idleUntilConverged(store):
|
||||
nodeIDs = sorted(store.keys())
|
||||
timeOfLastChange = 0
|
||||
step = 0
|
||||
# Idle until the network has converged
|
||||
while step - timeOfLastChange < 4*TIMEOUT:
|
||||
step += 1
|
||||
print "Step: {}, last change: {}".format(step, timeOfLastChange)
|
||||
changed = False
|
||||
for nodeID in nodeIDs:
|
||||
# Update node status, send messages
|
||||
changed |= store[nodeID].tick()
|
||||
for nodeID in nodeIDs:
|
||||
# Process messages
|
||||
changed |= store[nodeID].handleMessages()
|
||||
if changed: timeOfLastChange = step
|
||||
initTables(store)
|
||||
return store
|
||||
|
||||
def getCacheIndex(nodes, sourceIndex, destIndex):
|
||||
return sourceIndex*nodes + destIndex
|
||||
|
||||
def initTables(store):
|
||||
nodeIDs = sorted(store.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
print "Initializing routing tables for {} nodes".format(nNodes)
|
||||
for idx in xrange(nNodes):
|
||||
nodeID = nodeIDs[idx]
|
||||
store[nodeID].initTable()
|
||||
print "Routing tables initialized"
|
||||
return None
|
||||
|
||||
def getCache(store):
|
||||
nodeIDs = sorted(store.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
nodeIdxs = dict()
|
||||
for nodeIdx in xrange(nNodes):
|
||||
nodeIdxs[nodeIDs[nodeIdx]] = nodeIdx
|
||||
cache = array.array("H", [0]*nNodes*nNodes)
|
||||
for sourceIdx in xrange(nNodes):
|
||||
sourceID = nodeIDs[sourceIdx]
|
||||
print "Building fast lookup table for node {} / {} ({})".format(sourceIdx+1, nNodes, sourceID)
|
||||
for destIdx in xrange(nNodes):
|
||||
destID = nodeIDs[destIdx]
|
||||
if sourceID == destID: nextHop = destID # lookup would fail
|
||||
else: nextHop = store[sourceID].lookup(store[destID].info)
|
||||
nextHopIdx = nodeIdxs[nextHop]
|
||||
cache[getCacheIndex(nNodes, sourceIdx, destIdx)] = nextHopIdx
|
||||
return cache
|
||||
|
||||
def testPaths(store, dists):
|
||||
cache = getCache(store)
|
||||
nodeIDs = sorted(store.keys())
|
||||
nNodes = len(nodeIDs)
|
||||
idxs = dict()
|
||||
for nodeIdx in xrange(nNodes):
|
||||
nodeID = nodeIDs[nodeIdx]
|
||||
idxs[nodeID] = nodeIdx
|
||||
results = dict()
|
||||
for sourceIdx in xrange(nNodes):
|
||||
sourceID = nodeIDs[sourceIdx]
|
||||
print "Testing paths from node {} / {} ({})".format(sourceIdx+1, len(nodeIDs), sourceID)
|
||||
#dists = dijkstra(store, sourceID)
|
||||
for destIdx in xrange(nNodes):
|
||||
destID = nodeIDs[destIdx]
|
||||
if destID == sourceID: continue # Skip self
|
||||
distIdx = getCacheIndex(nNodes, sourceIdx, destIdx)
|
||||
eHops = dists[distIdx]
|
||||
if not eHops: continue # The network is split, no path exists
|
||||
hops = 0
|
||||
for pair in ((sourceIdx, destIdx), (destIdx, sourceIdx)): # Either direction because source routing
|
||||
nHops = 0
|
||||
locIdx = pair[0]
|
||||
dIdx = pair[1]
|
||||
while locIdx != dIdx:
|
||||
locIdx = cache[getCacheIndex(nNodes, locIdx, dIdx)]
|
||||
nHops += 1
|
||||
if not hops or nHops < hops: hops = nHops
|
||||
if eHops not in results: results[eHops] = dict()
|
||||
if hops not in results[eHops]: results[eHops][hops] = 0
|
||||
results[eHops][hops] += 1
|
||||
return results
|
||||
|
||||
def getAvgStretch(pathMatrix):
|
||||
avgStretch = 0.
|
||||
checked = 0.
|
||||
for eHops in sorted(pathMatrix.keys()):
|
||||
for nHops in sorted(pathMatrix[eHops].keys()):
|
||||
count = pathMatrix[eHops][nHops]
|
||||
stretch = float(nHops)/float(max(1, eHops))
|
||||
avgStretch += stretch*count
|
||||
checked += count
|
||||
avgStretch /= max(1, checked)
|
||||
return avgStretch
|
||||
|
||||
def getMaxStretch(pathMatrix):
|
||||
maxStretch = 0.
|
||||
for eHops in sorted(pathMatrix.keys()):
|
||||
for nHops in sorted(pathMatrix[eHops].keys()):
|
||||
stretch = float(nHops)/float(max(1, eHops))
|
||||
maxStretch = max(maxStretch, stretch)
|
||||
return maxStretch
|
||||
|
||||
def getCertSizes(store):
|
||||
# Returns nCerts frequency distribution
|
||||
# De-duplicates common certs (for shared prefixes in the path)
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
certs = set()
|
||||
for peer in node.peers.values():
|
||||
pCerts = set()
|
||||
assert len(peer.path) == 2
|
||||
assert peer.coords[-1] == peer.path[0]
|
||||
hops = peer.coords + peer.path[1:]
|
||||
for hopIdx in xrange(len(hops)-1):
|
||||
send = hops[hopIdx]
|
||||
if send == node.info.nodeID: continue # We created it, already have it
|
||||
path = hops[0:hopIdx+2]
|
||||
# Each cert is signed by the sender
|
||||
# Includes information about the path from the sender to the next hop
|
||||
# Next hop is at hopIdx+1, so the path to next hop is hops[0:hopIdx+2]
|
||||
cert = "{}:{}".format(send, path)
|
||||
certs.add(cert)
|
||||
size = len(certs)
|
||||
if size not in sizes: sizes[size] = 0
|
||||
sizes[size] += 1
|
||||
return sizes
|
||||
|
||||
def getMinLinkCertSizes(store):
|
||||
# Returns nCerts frequency distribution
|
||||
# De-duplicates common certs (for shared prefixes in the path)
|
||||
# Based on the minimum number of certs that must be traded through a particular link
|
||||
# Handled per link
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
peerCerts = dict()
|
||||
for peer in node.peers.values():
|
||||
pCerts = set()
|
||||
assert len(peer.path) == 2
|
||||
assert peer.coords[-1] == peer.path[0]
|
||||
hops = peer.coords + peer.path[1:]
|
||||
for hopIdx in xrange(len(hops)-1):
|
||||
send = hops[hopIdx]
|
||||
if send == node.info.nodeID: continue # We created it, already have it
|
||||
path = hops[0:hopIdx+2]
|
||||
# Each cert is signed by the sender
|
||||
# Includes information about the path from the sender to the next hop
|
||||
# Next hop is at hopIdx+1, so the path to next hop is hops[0:hopIdx+2]
|
||||
cert = "{}:{}".format(send, path)
|
||||
pCerts.add(cert)
|
||||
peerCerts[peer.nodeID] = pCerts
|
||||
for peer in peerCerts:
|
||||
size = 0
|
||||
pCerts = peerCerts[peer]
|
||||
for cert in pCerts:
|
||||
required = True
|
||||
for p2 in peerCerts:
|
||||
if p2 == peer: continue
|
||||
p2Certs = peerCerts[p2]
|
||||
if cert in p2Certs: required = False
|
||||
if required: size += 1
|
||||
if size not in sizes: sizes[size] = 0
|
||||
sizes[size] += 1
|
||||
return sizes
|
||||
|
||||
def getPathSizes(store):
|
||||
# Returns frequency distribution of the total number of hops the routing table
|
||||
# I.e. a node with 3 peers, each with 5 hop coord+path, would count as 3x5=15
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
size = 0
|
||||
for peer in node.peers.values():
|
||||
assert len(peer.path) == 2
|
||||
assert peer.coords[-1] == peer.path[0]
|
||||
peerSize = len(peer.coords) + len(peer.path) - 1 # double-counts peer, -1
|
||||
size += peerSize
|
||||
if size not in sizes: sizes[size] = 0
|
||||
sizes[size] += 1
|
||||
return sizes
|
||||
|
||||
def getPeerSizes(store):
|
||||
# Returns frequency distribution of the number of peers each node has
|
||||
sizes = dict()
|
||||
for node in store.values():
|
||||
nPeers = len(node.peers)
|
||||
if nPeers not in sizes: sizes[nPeers] = 0
|
||||
sizes[nPeers] += 1
|
||||
return sizes
|
||||
|
||||
def getAvgSize(sizes):
|
||||
sumSizes = 0
|
||||
nNodes = 0
|
||||
for size in sizes:
|
||||
count = sizes[size]
|
||||
sumSizes += size*count
|
||||
nNodes += count
|
||||
avgSize = float(sumSizes)/max(1, nNodes)
|
||||
return avgSize
|
||||
|
||||
def getMaxSize(sizes):
|
||||
return max(sizes.keys())
|
||||
|
||||
def getMinSize(sizes):
|
||||
return min(sizes.keys())
|
||||
|
||||
def getResults(pathMatrix):
|
||||
results = []
|
||||
for eHops in sorted(pathMatrix.keys()):
|
||||
for nHops in sorted(pathMatrix[eHops].keys()):
|
||||
count = pathMatrix[eHops][nHops]
|
||||
results.append("{} {} {}".format(eHops, nHops, count))
|
||||
return '\n'.join(results)
|
||||
|
||||
####################################
|
||||
# Functions to run different tests #
|
||||
####################################
|
||||
|
||||
def runTest(store):
|
||||
# Runs the usual set of tests on the store
|
||||
# Does not save results, so only meant for quick tests
|
||||
# To e.g. check the code works, maybe warm up the pypy jit
|
||||
for node in store.values():
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Begin testing network"
|
||||
dists = None
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
peers = getPeerSizes(store)
|
||||
certs = getCertSizes(store)
|
||||
paths = getPathSizes(store)
|
||||
linkCerts = getMinLinkCertSizes(store)
|
||||
avgPeerSize = getAvgSize(peers)
|
||||
maxPeerSize = getMaxSize(peers)
|
||||
avgCertSize = getAvgSize(certs)
|
||||
maxCertSize = getMaxSize(certs)
|
||||
avgPathSize = getAvgSize(paths)
|
||||
maxPathSize = getMaxSize(paths)
|
||||
avgLinkCert = getAvgSize(linkCerts)
|
||||
maxLinkCert = getMaxSize(linkCerts)
|
||||
totalCerts = sum(map(lambda x: x*certs[x], certs.keys()))
|
||||
totalLinks = sum(map(lambda x: x*peers[x], peers.keys())) # one-way links
|
||||
avgCertsPerLink = float(totalCerts)/max(1, totalLinks)
|
||||
print "Finished testing network"
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
print "Avg / Max nPeers size: {} / {}".format(avgPeerSize, maxPeerSize)
|
||||
print "Avg / Max nCerts size: {} / {}".format(avgCertSize, maxCertSize)
|
||||
print "Avg / Max total hops in any node's routing table: {} / {}".format(avgPathSize, maxPathSize)
|
||||
print "Avg / Max lower bound cert requests per link (one-way): {} / {}".format(avgLinkCert, maxLinkCert)
|
||||
print "Avg certs per link (one-way): {}".format(avgCertsPerLink)
|
||||
return # End of function
|
||||
|
||||
def rootNodeASTest(path, outDir="output-treesim-AS", dists=None, proc = 1):
|
||||
# Checks performance for every possible choice of root node
|
||||
# Saves output for each root node to a separate file on disk
|
||||
# path = input path to some caida.org formatted AS-relationship graph
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
store = makeStoreASRelGraph(path)
|
||||
nodes = sorted(store.keys())
|
||||
for nodeIdx in xrange(len(nodes)):
|
||||
if nodeIdx % proc != 0: continue # Work belongs to someone else
|
||||
rootNodeID = nodes[nodeIdx]
|
||||
outpath = outDir+"/{}".format(rootNodeID)
|
||||
if os.path.exists(outpath):
|
||||
print "Skipping {}, already processed".format(rootNodeID)
|
||||
continue
|
||||
store = makeStoreASRelGraphFixedRoot(path, rootNodeID)
|
||||
for node in store.values():
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(nodeIdx, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished test for root AS {} ({} / {})".format(rootNodeID, nodeIdx+1, len(store))
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
#break # Stop after 1, because they can take forever
|
||||
return # End of function
|
||||
|
||||
def timelineASTest():
|
||||
# Meant to study the performance of the network as a function of network size
|
||||
# Loops over a set of AS-relationship graphs
|
||||
# Runs a test on each graph, selecting highest-degree node as the root
|
||||
# Saves results for each graph to a separate file on disk
|
||||
outDir = "output-treesim-timeline-AS"
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
paths = sorted(glob.glob("asrel/datasets/*"))
|
||||
for path in paths:
|
||||
date = os.path.basename(path).split(".")[0]
|
||||
outpath = outDir+"/{}".format(date)
|
||||
if os.path.exists(outpath):
|
||||
print "Skipping {}, already processed".format(date)
|
||||
continue
|
||||
store = makeStoreASRelGraphMaxDeg(path)
|
||||
dists = None
|
||||
for node in store.values():
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(date, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished {} with {} nodes".format(date, len(store))
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
#break # Stop after 1, because they can take forever
|
||||
return # End of function
|
||||
|
||||
def timelineDimesTest():
|
||||
# Meant to study the performance of the network as a function of network size
|
||||
# Loops over a set of AS-relationship graphs
|
||||
# Runs a test on each graph, selecting highest-degree node as the root
|
||||
# Saves results for each graph to a separate file on disk
|
||||
outDir = "output-treesim-timeline-dimes"
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
# Input files are named ASEdgesX_Y where X = month (no leading 0), Y = year
|
||||
paths = sorted(glob.glob("DIMES/ASEdges/*.gz"))
|
||||
exists = set(glob.glob(outDir+"/*"))
|
||||
for path in paths:
|
||||
date = os.path.basename(path).split(".")[0]
|
||||
outpath = outDir+"/{}".format(date)
|
||||
if outpath in exists:
|
||||
print "Skipping {}, already processed".format(date)
|
||||
continue
|
||||
store = makeStoreDimesEdges(path)
|
||||
# Get the highest degree node and make it root
|
||||
# Sorted by nodeID just to make it stable in the event of a tie
|
||||
nodeIDs = sorted(store.keys())
|
||||
bestRoot = ""
|
||||
bestDeg = 0
|
||||
for nodeID in nodeIDs:
|
||||
node = store[nodeID]
|
||||
if len(node.links) > bestDeg:
|
||||
bestRoot = nodeID
|
||||
bestDeg = len(node.links)
|
||||
assert bestRoot
|
||||
store = makeStoreDimesEdges(path, bestRoot)
|
||||
rootID = "R" + bestRoot[1:]
|
||||
assert rootID in store
|
||||
# Don't forget to set random seed before setitng times
|
||||
# To make results reproducible
|
||||
nodeIDs = sorted(store.keys())
|
||||
random.seed(12345)
|
||||
for nodeID in nodeIDs:
|
||||
node = store[nodeID]
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(date, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished {} with {} nodes".format(date, len(store))
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
break # Stop after 1, because they can take forever
|
||||
return # End of function
|
||||
|
||||
def scalingTest(maxTests=None, inputDir="graphs"):
|
||||
# Meant to study the performance of the network as a function of network size
|
||||
# Loops over a set of nodes in a previously generated graph
|
||||
# Runs a test on each graph, testing each node as the root
|
||||
# if maxTests is set, tests only that number of roots (highest degree first)
|
||||
# Saves results for each graph to a separate file on disk
|
||||
outDir = "output-treesim-{}".format(inputDir)
|
||||
if not os.path.exists(outDir): os.makedirs(outDir)
|
||||
assert os.path.exists(outDir)
|
||||
paths = sorted(glob.glob("{}/*".format(inputDir)))
|
||||
exists = set(glob.glob(outDir+"/*"))
|
||||
for path in paths:
|
||||
gc.collect() # pypy waits for gc to close files
|
||||
graph = os.path.basename(path).split(".")[0]
|
||||
store = makeStoreGeneratedGraph(path)
|
||||
# Get the highest degree node and make it root
|
||||
# Sorted by nodeID just to make it stable in the event of a tie
|
||||
nodeIDs = sorted(store.keys(), key=lambda x: len(store[x].links), reverse=True)
|
||||
dists = None
|
||||
if maxTests: nodeIDs = nodeIDs[:maxTests]
|
||||
for nodeID in nodeIDs:
|
||||
nodeIDStr = str(nodeID).zfill(len(str(len(store)-1)))
|
||||
outpath = outDir+"/{}-{}".format(graph, nodeIDStr)
|
||||
if outpath in exists:
|
||||
print "Skipping {}-{}, already processed".format(graph, nodeIDStr)
|
||||
continue
|
||||
store = makeStoreGeneratedGraph(path, nodeID)
|
||||
# Don't forget to set random seed before setting times
|
||||
random.seed(12345) # To make results reproducible
|
||||
nIDs = sorted(store.keys())
|
||||
for nID in nIDs:
|
||||
node = store[nID]
|
||||
node.info.time = random.randint(0, TIMEOUT)
|
||||
node.info.tstamp = TIMEOUT
|
||||
print "Beginning {}, size {}".format(graph, len(store))
|
||||
if not dists: dists = dijkstrall(store)
|
||||
idleUntilConverged(store)
|
||||
pathMatrix = testPaths(store, dists)
|
||||
avgStretch = getAvgStretch(pathMatrix)
|
||||
maxStretch = getMaxStretch(pathMatrix)
|
||||
results = getResults(pathMatrix)
|
||||
with open(outpath, "w") as f:
|
||||
f.write(results)
|
||||
print "Finished {} with {} nodes for root {}".format(graph, len(store), nodeID)
|
||||
print "Avg / Max stretch: {} / {}".format(avgStretch, maxStretch)
|
||||
return # End of function
|
||||
|
||||
##################
|
||||
# Main Execution #
|
||||
##################
|
||||
|
||||
if __name__ == "__main__":
|
||||
if True: # Run a quick test
|
||||
random.seed(12345) # DEBUG
|
||||
store = makeStoreSquareGrid(4)
|
||||
runTest(store) # Quick test
|
||||
store = None
|
||||
# Do some real work
|
||||
#runTest(makeStoreDimesEdges("DIMES/ASEdges/ASEdges1_2007.csv.gz"))
|
||||
#timelineDimesTest()
|
||||
#rootNodeASTest("asrel/datasets/19980101.as-rel.txt")
|
||||
#timelineASTest()
|
||||
#rootNodeASTest("hype-2016-09-19.list", "output-treesim-hype")
|
||||
#scalingTest(None, "graphs-20") # First argument 1 to only test 1 root per graph
|
||||
#store = makeStoreGeneratedGraph("bgp_tables")
|
||||
#store = makeStoreGeneratedGraph("skitter")
|
||||
#store = makeStoreASRelGraphMaxDeg("hype-2016-09-19.list") #http://hia.cjdns.ca/watchlist/c/walk.peers.20160919
|
||||
#store = makeStoreGeneratedGraph("fc00-2017-08-12.txt")
|
||||
if store: runTest(store)
|
||||
#rootNodeASTest("skitter", "output-treesim-skitter", None, 0, 1)
|
||||
#scalingTest(1, "graphs-20") # First argument 1 to only test 1 root per graph
|
||||
#scalingTest(1, "graphs-21") # First argument 1 to only test 1 root per graph
|
||||
#scalingTest(1, "graphs-22") # First argument 1 to only test 1 root per graph
|
||||
#scalingTest(1, "graphs-23") # First argument 1 to only test 1 root per graph
|
||||
if not store:
|
||||
import sys
|
||||
args = sys.argv
|
||||
if len(args) == 2:
|
||||
job_number = int(sys.argv[1])
|
||||
#rootNodeASTest("fc00-2017-08-12.txt", "fc00", None, job_number)
|
||||
#rootNodeASTest("skitter", "out-skitter", None, job_number)
|
||||
rootNodeASTest("walk-1517414401.txt.map", "out-walk", None, job_number)
|
||||
else:
|
||||
print "Usage: {} job_number".format(args[0])
|
||||
print "job_number = which job set to run on this node (1-indexed)"
|
||||
|
35
misc/sim/walk2map.py
Normal file
35
misc/sim/walk2map.py
Normal file
@ -0,0 +1,35 @@
|
||||
#!/usr/bin/env python2
|
||||
|
||||
def main():
|
||||
import sys
|
||||
args = sys.argv
|
||||
if len(args) != 2:
|
||||
print "Usage:", args[0], "path/to/walk.txt"
|
||||
return
|
||||
import glob
|
||||
files = glob.glob(args[1])
|
||||
if len(files) == 0:
|
||||
print "File not found:", args[1]
|
||||
return
|
||||
for inFile in files:
|
||||
with open(inFile, 'r') as f: lines = f.readlines()
|
||||
out = []
|
||||
nodes = dict()
|
||||
for line in lines:
|
||||
words = line.strip().strip('[').strip(']').split(',')
|
||||
if len(words) < 5: continue
|
||||
if words[0].strip('"') != "link": continue
|
||||
first, second = words[3], words[4]
|
||||
if first not in nodes: nodes[first] = len(nodes)
|
||||
if second not in nodes: nodes[second] = len(nodes)
|
||||
for line in lines:
|
||||
words = line.strip().strip('[').strip(']').split(',')
|
||||
if len(words) < 5: continue
|
||||
if words[0].strip('"') != "link": continue
|
||||
first, second = nodes[words[3]], nodes[words[4]]
|
||||
out.append("{0} {1}".format(first, second))
|
||||
with open(inFile+".map", "w") as f: f.write("\n".join(out))
|
||||
# End loop over files
|
||||
# End main
|
||||
|
||||
if __name__ == "__main__": main()
|
Loading…
Reference in New Issue
Block a user