mirror of
https://github.com/logos-storage/das-research.git
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Merge d108c18065b0322f3d2565e3abd0f29a13fcd5d7 into db5fd6c157fffb293a57a5b02fbca1316e049252
This commit is contained in:
Mikel Cortes
GitHub
commit
c20b1eac02
8
.gitignore
vendored
8
.gitignore
vendored
@ -1,7 +1,13 @@
|
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*.swp
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*.pyc
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results/*
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myenv*/
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*env*/
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doc/_build
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!results/plots.py
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Frontend/
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.idea
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# DHT module related
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DHT/imgs
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DHT/csvs
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DHT/.ipynb_checkpoints
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3
.gitmodules
vendored
Normal file
3
.gitmodules
vendored
Normal file
@ -0,0 +1,3 @@
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[submodule "py-dht"]
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path = py-dht
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url = https://github.com/cortze/py-dht
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@ -81,3 +81,8 @@ class Block:
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print(line+"|")
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print(dash)
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def getUniqueIDforSegment(self, rowID, columnID):
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"""It returns a unique ID for a segment indicating its coordinates in the block"""
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return f"r{rowID}-c{columnID}"
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@ -1,10 +1,7 @@
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#!/bin/python
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import networkx as nx
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import logging, random
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import pandas as pd
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from functools import partial, partialmethod
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from datetime import datetime
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from DAS.tools import *
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from DAS.results import *
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from DAS.observer import *
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@ -216,12 +213,12 @@ class Simulator:
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self.logger.debug("Column %d, Neighbor %d sent: %s" % (c, val.columnNeighbors[c][nc].node.ID, val.columnNeighbors[c][nc].received), extra=self.format)
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self.logger.debug("Column %d, Neighbor %d has: %s" % (c, val.columnNeighbors[c][nc].node.ID, self.validators[val.columnNeighbors[c][nc].node.ID].getColumn(c)), extra=self.format)
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def run(self):
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def runBlockBroadcasting(self):
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"""It runs the main simulation until the block is available or it gets stucked."""
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self.glob.checkRowsColumns(self.validators)
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for i in range(0,self.shape.numberNodes):
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if i == self.proposerID:
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self.validators[i].initBlock()
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self.block = self.validators[i].initBlock() # Keep the OG block that we are broadcasting
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else:
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self.validators[i].logIDs()
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arrived, expected, ready, validatedall, validated = self.glob.checkStatus(self.validators)
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@ -235,7 +232,7 @@ class Simulator:
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oldMissingSamples = missingSamples
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self.logger.debug("PHASE SEND %d" % steps, extra=self.format)
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for i in range(0,self.shape.numberNodes):
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self.validators[i].send()
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self.validators[i].sendToNeigbors()
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self.logger.debug("PHASE RECEIVE %d" % steps, extra=self.format)
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for i in range(1,self.shape.numberNodes):
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self.validators[i].receiveRowsColumns()
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@ -118,7 +118,7 @@ class Validator:
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self.logger.debug("Selected columns: "+str(self.columnIDs), extra=self.format)
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def initBlock(self):
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"""It initializes the block for the proposer."""
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"""It initializes and returns the block for the proposer"""
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if self.amIproposer == 0:
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self.logger.warning("I am not a block proposer", extra=self.format)
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else:
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@ -177,6 +177,8 @@ class Validator:
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measuredFailureRate = nbFailures * 100 / (self.shape.blockSize * self.shape.blockSize)
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self.logger.debug("Number of failures: %d (%0.02f %%)", nbFailures, measuredFailureRate, extra=self.format)
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return self.block
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def getColumn(self, index):
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"""It returns a given column."""
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return self.block.getColumn(index)
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@ -446,7 +448,7 @@ class Validator:
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if self.statsTxInSlot >= self.bwUplink:
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return
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def send(self):
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def sendToNeigbors(self):
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""" Send as much as we can in the timestep, limited by bwUplink."""
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# process node level send queue
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35
DHT/README.md
Normal file
35
DHT/README.md
Normal file
@ -0,0 +1,35 @@
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# DHT simulations for DAS
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Simulate the seeding and the retrieval of Ethereum DAS samples in a Kademlia-based DHT.
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## Dependencies
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The DHT module relies on [`py-dht`](https://github.com/cortze/py-dht) to run, however it is already installed together with the DAS block disemination dependencies.
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```shell
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# once the venv is created (make sure the venv name match with the `install_dependencies.sh` one)
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das-research$ bash install_dependencies.sh
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```
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## How to run it
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To run the seeding and retrieval simulation of the DHT, these are the steps that would need to be taken:
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1. configure the desired parameters in the `dhtConf.py`. NOTE: the script will create the CSV and IMG folders for you!
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2. execute the experiment by running:
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```shell
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# venv needs to be activated
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# $ source venv/bin/activate
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das-research/DHT$ python3 dhtStudy.py dhtSmallConf.py
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```
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the output should look like this for each of the possible configurations:
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```shell
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network init done in 52.08381795883179 secs
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[===============================================================================================================================================================================================================================] 100%
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test done in 159.97085118293762 secs
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DHT fast-init jobs:8 done in 52.08381795883179 secs
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12000 nodes, k=20, alpha=3, 10000 lookups
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mean time per lookup : 0.010750784277915955
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mean aggr delay (secs): 0.31828715
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mean contacted nodes: 8.7223
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time to make 10000 lookups: 107.50784277915955 secs
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done with the studies in 167.69087147712708
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```
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3. all the visualization graphs can be generated using the `retrieval_on_das_plotting.ipynb` notebook.
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1
DHT/__init__.py
Normal file
1
DHT/__init__.py
Normal file
@ -0,0 +1 @@
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from plots import *
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270
DHT/dhtRetrievals.py
Normal file
270
DHT/dhtRetrievals.py
Normal file
@ -0,0 +1,270 @@
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import time
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import progressbar
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import random
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import numpy as np
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import pandas as pd
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import dht
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from utils import getStrFromDelayRange
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TOTAL_PERCENTAGE = 100
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PERCENTAGE_INTERVALS = 1
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class SingleDHTretrievalStudy:
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def __init__(self, csvFolder, imgFolder, jobs, nn, rn, samples,
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fErrR, sErrR, cDelR, fDelR, sDelR, k, a, b, y, stepsToStop):
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self.csvFolder = csvFolder
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self.imgFolder = imgFolder
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self.jobs = jobs
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self.nn = nn
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self.rn = rn
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self.samples = samples
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self.fastErrorRate = fErrR
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self.slowErrorRate = sErrR
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self.connDelayRange = cDelR
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self.fastDelayRange = fDelR
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self.slowDelayRange = sDelR # timeouts
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self.k = k
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self.alpha = a
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self.beta = b
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self.gamma = y
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self.stepsToStop = stepsToStop
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# namings
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s = ""
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s += f"_nn{nn}"
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s += f"_rn{rn}"
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s += f"_sampl{samples}"
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s += f"_fer{fErrR}"
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s += f"_ser{sErrR}"
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s += f"_cdr{getStrFromDelayRange(cDelR)}"
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s += f"_fdr{getStrFromDelayRange(fDelR)}"
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s += f"_sdr{getStrFromDelayRange(sDelR)}"
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s += f"_k{k}"
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s += f"_a{a}"
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s += f"_b{b}"
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s += f"_y{y}"
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s += f"_steps{stepsToStop}"
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self.studyName = s
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print(f"Retrieval Study => {s}")
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def run(self):
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# Init the DHT Network
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testInitTime = time.time()
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network = dht.DHTNetwork(
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0,
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self.fastErrorRate,
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self.slowErrorRate,
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self.connDelayRange,
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self.fastDelayRange,
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self.slowDelayRange,
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self.gamma)
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initStartTime = time.time()
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network.init_with_random_peers(
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self.jobs,
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self.nn,
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self.k,
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self.alpha,
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self.beta,
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self.stepsToStop)
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self.networkInitTime = time.time() - initStartTime
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print(f"network init done in {self.networkInitTime} secs")
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# get random node to propose publish the
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builderNode = network.nodestore.get_node(random.randint(0, self.nn))
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# create and publish @@@ number of samples to the network
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# lookups metrics
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ks = []
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nns = []
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stepstostops = []
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fastErrorRate = []
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slowErrorRate = []
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connDelayRange = []
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fastDelayRange = []
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slowDelayRange = []
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alphas = []
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betas = []
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gammas = []
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providers = []
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sampleNames = []
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provideLookupAggrTime = []
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provideAggrTime = []
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provideOperationAggrTime = []
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provideSuccNodes = []
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provideFailedNodes = []
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samples = []
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for i in range(self.samples):
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sampleContent = f"sample {i}"
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summary, _ = builderNode.provide_block_segment(sampleContent)
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samples.append((sampleContent, sampleContent, summary))
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# add metrics for the csv
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ks.append(self.k)
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alphas.append(self.alpha)
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betas.append(self.beta)
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gammas.append(self.gamma)
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nns.append(self.nn)
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stepstostops.append(self.stepsToStop)
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fastErrorRate.append(f"{self.fastErrorRate}")
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slowErrorRate.append(f"{self.slowErrorRate}")
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connDelayRange.append(f"{getStrFromDelayRange(self.connDelayRange)}")
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fastDelayRange.append(f"{getStrFromDelayRange(self.fastDelayRange)}")
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slowDelayRange.append(f"{getStrFromDelayRange(self.slowDelayRange)}")
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providers.append(builderNode.ID)
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sampleNames.append(sampleContent)
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provideLookupAggrTime.append(summary['lookupDelay'])
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provideAggrTime.append(summary['provideDelay'])
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provideOperationAggrTime.append(summary['operationDelay'])
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provideSuccNodes.append(len(summary['succesNodeIDs']))
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provideFailedNodes.append(len(summary['failedNodeIDs']))
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# save the provide data
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df = pd.DataFrame({
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"number_nodes": nns,
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"k": ks,
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"alpha": alphas,
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"beta": betas,
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"gamma": gammas,
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"stop_steps": stepstostops,
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"fast_error_rate": fastErrorRate,
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"slow_error_rate": slowErrorRate,
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"connection_delay_range": connDelayRange,
|
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"fast_delay_range": fastDelayRange,
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"slow_delay": slowDelayRange,
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"provider": providers,
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"sample": sampleNames,
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"provide_lookup_aggr_time": provideLookupAggrTime,
|
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"provide_aggr_time": provideAggrTime,
|
||||
"provide_operation_aggr_time": provideOperationAggrTime,
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"provide_succ_nodes": provideSuccNodes,
|
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"provide_fail_nodes": provideFailedNodes,
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})
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df.to_csv(self.csvFolder + f"/retrieval_provide{self.studyName}.csv")
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network.reset_network_metrics()
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del df
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|
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nns = []
|
||||
ks = []
|
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alphas = []
|
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betas = []
|
||||
gammas = []
|
||||
stepstostops = []
|
||||
fastErrorRate = []
|
||||
slowErrorRate = []
|
||||
connDelayRange = []
|
||||
fastDelayRange = []
|
||||
slowDelayRange = []
|
||||
retrievers = []
|
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sampleNames = []
|
||||
lookupTimes = []
|
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lookupAggrDelays = []
|
||||
attemptedNodes = []
|
||||
finishedConnAttempts = []
|
||||
successfullCons = []
|
||||
failedCons = []
|
||||
valRetrievable = []
|
||||
totalDiscNodes = []
|
||||
accuracies = []
|
||||
|
||||
bar = progressbar.ProgressBar(
|
||||
maxval=self.rn,
|
||||
widgets=[progressbar.Bar('=', '[', ']'), ' ', progressbar.Percentage()])
|
||||
bar.start()
|
||||
|
||||
for i in range(self.rn):
|
||||
retrieverNode = network.nodestore.get_node(random.randint(0, self.nn))
|
||||
while retrieverNode.ID == builderNode.ID:
|
||||
retrieverNode = network.nodestore.get_node(random.randint(0, self.nn))
|
||||
|
||||
for l in range(self.samples):
|
||||
sampleContent = f"sample {l}"
|
||||
sh = dht.Hash(sampleContent)
|
||||
lstime = time.time()
|
||||
closest, val, summary, aggrDelay = retrieverNode.lookup_for_hash(
|
||||
key=sh, trackaccuracy=True, finishwithfirstvalue=True)
|
||||
lduration = time.time() - lstime
|
||||
|
||||
if val == sampleContent:
|
||||
valRetrievable.append(1)
|
||||
else:
|
||||
valRetrievable.append(0)
|
||||
|
||||
nns.append(self.nn)
|
||||
ks.append(self.k)
|
||||
alphas.append(self.alpha)
|
||||
betas.append(self.beta)
|
||||
gammas.append(self.gamma)
|
||||
stepstostops.append(self.stepsToStop)
|
||||
fastErrorRate.append(f"{self.fastErrorRate}")
|
||||
slowErrorRate.append(f"{self.slowErrorRate}")
|
||||
connDelayRange.append(f"{getStrFromDelayRange(self.connDelayRange)}")
|
||||
fastDelayRange.append(f"{getStrFromDelayRange(self.fastDelayRange)}")
|
||||
slowDelayRange.append(f"{getStrFromDelayRange(self.slowDelayRange)}")
|
||||
retrievers.append(retrieverNode.ID)
|
||||
sampleNames.append(sampleContent)
|
||||
lookupTimes.append(lduration)
|
||||
lookupAggrDelays.append(aggrDelay)
|
||||
finishedConnAttempts.append(summary['connectionFinished'])
|
||||
attemptedNodes.append(summary['connectionAttempts'])
|
||||
successfullCons.append(summary['successfulCons'])
|
||||
failedCons.append(summary['failedCons'])
|
||||
totalDiscNodes.append(summary['totalNodes'])
|
||||
accuracies.append(summary['accuracy'])
|
||||
|
||||
# clean up the memory
|
||||
del sh
|
||||
del summary
|
||||
del closest
|
||||
|
||||
# percentajes
|
||||
bar.update(i + 1)
|
||||
|
||||
bar.finish()
|
||||
|
||||
testDuration = time.time() - testInitTime
|
||||
print(f"test done in {testDuration} secs")
|
||||
print(f"DHT fast-init jobs:{self.jobs} done in {self.networkInitTime} secs")
|
||||
print(f"{self.nn} nodes, k={self.k}, alpha={self.alpha}, {len(lookupTimes)} lookups")
|
||||
print(f"mean time per lookup : {np.mean(lookupTimes)}")
|
||||
print(f"mean aggr delay (secs): {np.mean(lookupAggrDelays) / 1000}")
|
||||
print(f"mean contacted nodes: {np.mean(attemptedNodes)}")
|
||||
print(f"time to make {len(lookupTimes)} lookups: {np.sum(lookupTimes)} secs")
|
||||
print()
|
||||
|
||||
# Create the panda objs and export the to csvs
|
||||
df = pd.DataFrame({
|
||||
"number_nodes": nns,
|
||||
"k": ks,
|
||||
"alpha": alphas,
|
||||
"beta": betas,
|
||||
"gamma": gammas,
|
||||
"stop_steps": stepstostops,
|
||||
"fast_error_rate": fastErrorRate,
|
||||
"slow_error_rate": slowErrorRate,
|
||||
"connection_delay_range": connDelayRange,
|
||||
"fast_delay_range": fastDelayRange,
|
||||
"slow_delay": slowDelayRange,
|
||||
"retriever": retrievers,
|
||||
"sample": sampleNames,
|
||||
"lookup_wallclock_time": lookupTimes,
|
||||
"lookup_aggregated_delay": lookupAggrDelays,
|
||||
"attempted_nodes": attemptedNodes,
|
||||
"finished_connection_attempts": finishedConnAttempts,
|
||||
"successful_connections": successfullCons,
|
||||
"failed_connections": failedCons,
|
||||
"total_discovered_nodes": totalDiscNodes,
|
||||
"retrievable": valRetrievable,
|
||||
"accuracy": accuracies,
|
||||
})
|
||||
df.to_csv(self.csvFolder + f"/retrieval_lookup{self.studyName}.csv")
|
||||
|
||||
# save the network metrics
|
||||
networkMetrics = network.connection_metrics()
|
||||
network_df = pd.DataFrame(networkMetrics)
|
||||
network_df.to_csv(self.csvFolder + f"/retrieval_lookup_network{self.studyName}.csv")
|
||||
|
||||
del network
|
||||
del df
|
||||
del network_df
|
||||
25
DHT/dhtSmallConf.py
Normal file
25
DHT/dhtSmallConf.py
Normal file
@ -0,0 +1,25 @@
|
||||
# Output Folders
|
||||
csvsFolder = "csvs/retrieval_test"
|
||||
imgFolder = "imgs/retrieval_test"
|
||||
|
||||
# Simulation
|
||||
# Define the type of study that we want to perform: "retrieval"
|
||||
studyType = "retrieval"
|
||||
|
||||
# Network
|
||||
jobs = 8
|
||||
nodeNumber = [2_000]
|
||||
nodesRetrieving = [100]
|
||||
samples = [20]
|
||||
fastErrorRate = [10]
|
||||
slowErrorRate = [0]
|
||||
connectionDelayRange = [range(50, 76, 1)] # ms
|
||||
fastDelayRange = [range(50, 101, 1)] # ms
|
||||
slowDelays = [None] # ms
|
||||
gammas = [0.125] # ms
|
||||
|
||||
# DHT config
|
||||
ks = [20]
|
||||
alphas = [3]
|
||||
betas = [20]
|
||||
stepsToStops = [3]
|
||||
77
DHT/dhtStudy.py
Normal file
77
DHT/dhtStudy.py
Normal file
@ -0,0 +1,77 @@
|
||||
import gc
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import importlib
|
||||
import itertools
|
||||
from dhtRetrievals import SingleDHTretrievalStudy
|
||||
|
||||
|
||||
def study(config):
|
||||
studyStartTime = time.time()
|
||||
|
||||
for nn, nr, samples, fastErrR, slowErrR, connDelayR, fastDelayR, slowD, k, a, b, y, steps4stop in itertools.product(
|
||||
config.nodeNumber,
|
||||
config.nodesRetrieving,
|
||||
config.samples,
|
||||
config.fastErrorRate,
|
||||
config.slowErrorRate,
|
||||
config.connectionDelayRange,
|
||||
config.fastDelayRange,
|
||||
config.slowDelays,
|
||||
config.ks,
|
||||
config.alphas,
|
||||
config.betas,
|
||||
config.gammas,
|
||||
config.stepsToStops):
|
||||
|
||||
if config.studyType == "retrieval":
|
||||
singleStudy = SingleDHTretrievalStudy(
|
||||
config.csvsFolder,
|
||||
config.imgFolder,
|
||||
config.jobs,
|
||||
nn,
|
||||
nr,
|
||||
samples,
|
||||
fastErrR,
|
||||
slowErrR,
|
||||
connDelayR,
|
||||
fastDelayR,
|
||||
slowD,
|
||||
k,
|
||||
a,
|
||||
b,
|
||||
y,
|
||||
steps4stop)
|
||||
else:
|
||||
print(f"study type not recognized: {config.studyType}")
|
||||
exit(1)
|
||||
|
||||
# if the study type is correct, run the simulation
|
||||
singleStudy.run()
|
||||
|
||||
# clean up memory
|
||||
del singleStudy
|
||||
_ = gc.collect()
|
||||
|
||||
print(f"done with the studies in {time.time() - studyStartTime}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
if len(sys.argv) < 2:
|
||||
print("please provide a configuration file")
|
||||
|
||||
try:
|
||||
config = importlib.import_module(sys.argv[1])
|
||||
except ModuleNotFoundError as e:
|
||||
try:
|
||||
config = importlib.import_module(str(sys.argv[1]).replace(".py", ""))
|
||||
except ModuleNotFoundError as e:
|
||||
print(e)
|
||||
print("You need to pass a configuration file in parameter")
|
||||
exit(1)
|
||||
|
||||
# Make sure that the output folders exist
|
||||
for folder in [config.csvsFolder, config.imgFolder]:
|
||||
os.makedirs(folder, exist_ok=True)
|
||||
study(config)
|
||||
265
DHT/plots.py
Normal file
265
DHT/plots.py
Normal file
@ -0,0 +1,265 @@
|
||||
import os
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
from IPython.display import display
|
||||
|
||||
# Tag Identifiers
|
||||
RETRIEVAL = "retrieval"
|
||||
LOOKUP = "lookup"
|
||||
NETWORK = "network"
|
||||
NN = "nn"
|
||||
RN = "rn"
|
||||
SAMPL = "sampl"
|
||||
FER = "fer"
|
||||
SER = "ser"
|
||||
CDR = "cdr"
|
||||
FDR = "fdr"
|
||||
SDR = "sdr"
|
||||
K = "k"
|
||||
A = "a"
|
||||
B = "b"
|
||||
Y = "y"
|
||||
STEPS = "steps"
|
||||
# --
|
||||
OPERATION = "operation"
|
||||
NUMBER_NODES = "number_nodes"
|
||||
RETRIEVAL_NODES = "retrieval_nodes"
|
||||
CONCURRENT_SAMPLES = "concurrent_samples"
|
||||
FAST_ERROR_RATE = "fast_error_rate"
|
||||
SLOW_ERROR_RATE = "slow_error_rate"
|
||||
CONNECTION_DELAYS = "connection_delays"
|
||||
FAST_ERROR_DELAYS = "fast_error_delays"
|
||||
SLOW_ERROR_DELAYS = "slow_error_delays"
|
||||
K_PARAMETER = "k_replication"
|
||||
ALPHA = "alpha"
|
||||
BETA = "beta"
|
||||
GAMMA = "overhead"
|
||||
STEPS_TO_STOP = "steps_to_stop"
|
||||
|
||||
|
||||
# Utils
|
||||
tag_example = "retrieval_lookup_nn12000_rn1_sampl100_fer10_ser0_cdr50-75_fdr50-100_sdr0_k20_a3_b20_y1.0_steps3"
|
||||
def tag_parser(tag: str):
|
||||
params = {
|
||||
OPERATION: "",
|
||||
NUMBER_NODES: "",
|
||||
RETRIEVAL_NODES: "",
|
||||
CONCURRENT_SAMPLES: "",
|
||||
FAST_ERROR_RATE: "",
|
||||
SLOW_ERROR_RATE: "",
|
||||
CONNECTION_DELAYS: "",
|
||||
FAST_ERROR_DELAYS: "",
|
||||
SLOW_ERROR_DELAYS: "",
|
||||
K_PARAMETER: "",
|
||||
ALPHA: "",
|
||||
BETA: "",
|
||||
GAMMA: "",
|
||||
STEPS_TO_STOP: "",
|
||||
}
|
||||
# split the tag into - type & parameters
|
||||
raw_params = tag.split("_")
|
||||
for param in raw_params:
|
||||
if NN in param:
|
||||
params[NUMBER_NODES] = param.replace(NN, "")
|
||||
elif RN in param:
|
||||
params[RETRIEVAL_NODES] = param.replace(RN, "")
|
||||
elif SAMPL in param:
|
||||
params[CONCURRENT_SAMPLES] = param.replace(SAMPL, "")
|
||||
elif FER in param:
|
||||
params[FAST_ERROR_RATE] = param.replace(FER, "")
|
||||
elif SER in param:
|
||||
params[SLOW_ERROR_RATE] = param.replace(SER, "")
|
||||
elif CDR in param:
|
||||
params[CONNECTION_DELAYS] = param.replace(CDR, "")
|
||||
elif FDR in param:
|
||||
params[FAST_ERROR_DELAYS] = param.replace(FDR, "")
|
||||
elif SDR in param:
|
||||
params[SLOW_ERROR_DELAYS] = param.replace(SDR, "")
|
||||
elif K in param and param != "lookup":
|
||||
params[K_PARAMETER] = param.replace(K, "")
|
||||
elif A in param:
|
||||
params[ALPHA] = param.replace(A, "")
|
||||
elif B in param:
|
||||
params[BETA] = param.replace(B, "")
|
||||
elif Y in param:
|
||||
params[GAMMA] = param.replace(Y, "")
|
||||
elif STEPS in param:
|
||||
params[STEPS_TO_STOP] = param.replace(STEPS, "")
|
||||
else:
|
||||
if params[OPERATION] == "":
|
||||
params[OPERATION] = param
|
||||
else:
|
||||
params[OPERATION] += f"_{param}"
|
||||
return params
|
||||
|
||||
def compose_legend(params, labels):
|
||||
legend = ""
|
||||
for label in labels:
|
||||
if legend == "":
|
||||
legend = f"{label}={params[label]}"
|
||||
else:
|
||||
legend += f" {label}={params[label]}"
|
||||
return legend
|
||||
|
||||
def make_folder(folder, reason):
|
||||
try:
|
||||
os.mkdir(folder)
|
||||
print(f"created folder {folder} for {reason}")
|
||||
except FileExistsError:
|
||||
print(f"folder {folder} was already created")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
|
||||
|
||||
# --- Single Metrics ---
|
||||
class SingleMetrics:
|
||||
|
||||
metrics = {
|
||||
"lookup_aggregated_delay": {
|
||||
"title_tag": "delay",
|
||||
"xlabel_tag": "delay (ms)",
|
||||
"ylabel_tag": "",
|
||||
},
|
||||
"finished_connection_attempts": {
|
||||
"title_tag": "hops",
|
||||
"xlabel_tag": "hops",
|
||||
"ylabel_tag": "",
|
||||
},
|
||||
"accuracy": {
|
||||
"title_tag": "accuracy",
|
||||
"xlabel_tag": "accuracy",
|
||||
"ylabel_tag": "",
|
||||
},
|
||||
}
|
||||
|
||||
def __init__(self, file, output_image_folder, operation, metrics: dict = dict()):
|
||||
self.file = file
|
||||
self.df = pd.read_csv(file)
|
||||
self.label = file.split("/")[-1].replace(".csv", "")
|
||||
self.targetFolder = output_image_folder+"/"+self.label
|
||||
self.operation = operation
|
||||
# add metrics to pre-existing ones
|
||||
self.metrics.update(metrics)
|
||||
# Make sure there is a valid folder for the imgaes
|
||||
make_folder(self.targetFolder, f"for keeping the lookup related images about {self.label}\n")
|
||||
print(f"plotting {self.label}, saving figures at {self.targetFolder}\n")
|
||||
# display the lookup wallclock cdf
|
||||
|
||||
# display the aggregated delay cdf
|
||||
for metric_name, metric_opts in self.metrics.items():
|
||||
self.plot_cdf(metric_name, metric_opts)
|
||||
self.plot_pdf(metric_name, metric_opts)
|
||||
|
||||
def plot_cdf(self, column_name, column_opts):
|
||||
df = self.df.sort_values(column_name)
|
||||
# CDF
|
||||
sns.set()
|
||||
g = sns.lineplot(data=df, x=column_name, y=np.linspace(0, 1, len(df)), color='red', ci=None)
|
||||
g.set(title=f"Simulated {self.operation} {column_name} CDF ({self.label})",
|
||||
xlabel=f"Simulated {column_opts['xlabel_tag']}", ylabel=f"{self.operation} {column_opts['ylabel_tag']}")
|
||||
fig = g.get_figure()
|
||||
fig.savefig(self.targetFolder+f"/{self.operation.lower()}_{column_name}_cdf.png")
|
||||
plt.show()
|
||||
|
||||
def plot_pdf(self, column_name, column_opts):
|
||||
df = self.df.sort_values(column_name)
|
||||
# Histogram
|
||||
bins = 8
|
||||
sns.set()
|
||||
g = sns.histplot(x=df[column_name], bins=bins)
|
||||
g.set(title=f"Simulated lookup {column_name} PDF ({self.label})",
|
||||
xlabel=f"Simulated {column_opts['xlabel_tag']}", ylabel=f"Lookups {column_opts['ylabel_tag']}")
|
||||
fig = g.get_figure()
|
||||
fig.savefig(self.targetFolder + f"/lookup_{column_name}_pdf.png")
|
||||
plt.show()
|
||||
|
||||
|
||||
# --- Multiple Aggregators ---
|
||||
class CombinedMetrics:
|
||||
metrics = {
|
||||
"lookup_aggregated_delay": {
|
||||
"title_tag": "delay",
|
||||
"xlabel_tag": "delay (ms)",
|
||||
"ylabel_tag": "",
|
||||
},
|
||||
"finished_connection_attempts": {
|
||||
"title_tag": "hops",
|
||||
"xlabel_tag": "hops",
|
||||
"ylabel_tag": "",
|
||||
},
|
||||
"accuracy": {
|
||||
"title_tag": "accuracy",
|
||||
"xlabel_tag": "accuracy",
|
||||
"ylabel_tag": "",
|
||||
},
|
||||
}
|
||||
|
||||
def __init__(self, files, aggregator, filters, operation, output_image_folder, metrics, legend):
|
||||
self.files = files
|
||||
self.dfs = []
|
||||
self.tags = []
|
||||
self.params = []
|
||||
self.tag = aggregator
|
||||
self.filters = filters
|
||||
self.operation = operation
|
||||
# add metrics to pre-existing ones
|
||||
self.metrics.update(metrics)
|
||||
for file in files:
|
||||
if any(filter not in file for filter in filters):
|
||||
continue
|
||||
|
||||
self.dfs.append(pd.read_csv(file))
|
||||
raw_tag = file.split("/")[-1].replace(".csv", "")
|
||||
params = tag_parser(raw_tag)
|
||||
tag = compose_legend(params, legend)
|
||||
self.params.append(params)
|
||||
self.tags.append(tag)
|
||||
|
||||
self.udf = self.unify_dfs(self.dfs) # unified dataframe
|
||||
|
||||
self.targetFolder = output_image_folder+f"/{self.operation.lower}_comparison_{aggregator}"
|
||||
make_folder(self.targetFolder, f"for keeping the {self.operation} related images about {self.tag}\n")
|
||||
print(f"plotting by {aggregator}, saving figures at {self.targetFolder}\n")
|
||||
|
||||
# --- plotting sequence ---
|
||||
for metrics_name, metrics_opts in self.metrics.items():
|
||||
self.plot_cdfs_by(aggregator, metrics_name, metrics_opts)
|
||||
self.plot_pdfs_by(aggregator, metrics_name, metrics_opts)
|
||||
|
||||
def unify_dfs(self, dfs):
|
||||
return pd.concat(dfs)
|
||||
|
||||
def plot_cdfs_by(self, aggregator_tag, column_name, column_opts):
|
||||
# CDF
|
||||
sns.set()
|
||||
palette = sns.color_palette(n_colors=len(self.dfs))
|
||||
for i, df in enumerate(self.dfs):
|
||||
df = df.sort_values(column_name)
|
||||
g = sns.lineplot(data=df, x=column_name, y=np.linspace(0, 1, len(df)), label=self.tags[i],
|
||||
ci=None, color=palette[i])
|
||||
g.set(title=f"Simulated {self.operation} {column_opts['title_tag']} CDF (by {aggregator_tag})",
|
||||
xlabel=f"Simulated {column_opts['xlabel_tag']}",
|
||||
ylabel=f"{self.operation} {column_opts['ylabel_tag']} CDF")
|
||||
plt.legend(loc='lower center', ncols=1, bbox_to_anchor=(0.5, -0.2+(-0.065*len(self.dfs))))
|
||||
fig = g.get_figure()
|
||||
fig.savefig(self.targetFolder+f"/simulated_{self.operation.lower()}_{column_name}_cdf.png")
|
||||
plt.show()
|
||||
|
||||
def plot_pdfs_by(self, aggregator_tag, column_name, column_opts):
|
||||
# Histogram
|
||||
sns.set()
|
||||
by_aggregator = self.udf.groupby([column_name, aggregator_tag]).count()
|
||||
df = by_aggregator.reset_index()
|
||||
g = sns.histplot(data=df, x=df[column_name])
|
||||
"""
|
||||
g = sns.barplot(data=df, x=df[column_name], y="Unnamed: 0", hue=aggregator_tag, width=1.2)
|
||||
"""
|
||||
g.set(title=f"Simulated {self.operation} {column_opts['title_tag']} PDF (by {aggregator_tag})",
|
||||
xlabel=f"Simulated {column_opts['xlabel_tag']}",
|
||||
ylabel=f"{self.operation} {column_opts['ylabel_tag']}")
|
||||
plt.legend(loc='lower center', ncols=1, bbox_to_anchor=(0.5, -0.2+(-0.065*len(self.dfs))))
|
||||
fig = g.get_figure()
|
||||
fig.savefig(self.targetFolder+f"/simulated_{self.operation.lower()}_{column_name}_hist.png")
|
||||
plt.show()
|
||||
116
DHT/requirements.txt
Normal file
116
DHT/requirements.txt
Normal file
@ -0,0 +1,116 @@
|
||||
anyio==4.1.0
|
||||
argon2-cffi==23.1.0
|
||||
argon2-cffi-bindings==21.2.0
|
||||
arrow==1.3.0
|
||||
asttokens==2.4.1
|
||||
async-lru==2.0.4
|
||||
attrs==23.1.0
|
||||
Babel==2.13.1
|
||||
beautifulsoup4==4.12.2
|
||||
bitarray==2.8.0
|
||||
bleach==6.1.0
|
||||
certifi==2023.11.17
|
||||
cffi==1.16.0
|
||||
charset-normalizer==3.3.2
|
||||
comm==0.2.0
|
||||
contourpy==1.2.0
|
||||
cycler==0.12.1
|
||||
debugpy==1.8.0
|
||||
decorator==5.1.1
|
||||
defusedxml==0.7.1
|
||||
dicttoxml==1.7.16
|
||||
exceptiongroup==1.2.0
|
||||
executing==2.0.1
|
||||
fastjsonschema==2.19.0
|
||||
fonttools==4.45.1
|
||||
fqdn==1.5.1
|
||||
idna==3.6
|
||||
ipykernel==6.27.1
|
||||
ipython==8.18.1
|
||||
ipywidgets==8.1.1
|
||||
isoduration==20.11.0
|
||||
jedi==0.19.1
|
||||
Jinja2==3.1.2
|
||||
joblib==1.2.0
|
||||
json5==0.9.14
|
||||
jsonpointer==2.4
|
||||
jsonschema==4.20.0
|
||||
jsonschema-specifications==2023.11.1
|
||||
jupyter==1.0.0
|
||||
jupyter-console==6.6.3
|
||||
jupyter-events==0.9.0
|
||||
jupyter-lsp==2.2.1
|
||||
jupyter_client==8.6.0
|
||||
jupyter_core==5.5.0
|
||||
jupyter_server==2.11.1
|
||||
jupyter_server_terminals==0.4.4
|
||||
jupyterlab==4.0.9
|
||||
jupyterlab-widgets==3.0.9
|
||||
jupyterlab_pygments==0.3.0
|
||||
jupyterlab_server==2.25.2
|
||||
kiwisolver==1.4.5
|
||||
MarkupSafe==2.1.3
|
||||
matplotlib==3.8.2
|
||||
matplotlib-inline==0.1.6
|
||||
mistune==3.0.2
|
||||
mplfinance==0.12.9b7
|
||||
nbclient==0.9.0
|
||||
nbconvert==7.11.0
|
||||
nbformat==5.9.2
|
||||
nest-asyncio==1.5.8
|
||||
networkx==3.2.1
|
||||
notebook==7.0.6
|
||||
notebook_shim==0.2.3
|
||||
numpy==1.26.2
|
||||
overrides==7.4.0
|
||||
packaging==23.2
|
||||
pandas==2.1.3
|
||||
pandocfilters==1.5.0
|
||||
parso==0.8.3
|
||||
pexpect==4.9.0
|
||||
Pillow==10.1.0
|
||||
platformdirs==4.0.0
|
||||
plotly==5.18.0
|
||||
progressbar==2.5
|
||||
prometheus-client==0.19.0
|
||||
prompt-toolkit==3.0.41
|
||||
psutil==5.9.6
|
||||
ptyprocess==0.7.0
|
||||
pure-eval==0.2.2
|
||||
pycparser==2.21
|
||||
Pygments==2.17.2
|
||||
pyparsing==3.1.1
|
||||
python-dateutil==2.8.2
|
||||
python-json-logger==2.0.7
|
||||
pytz==2023.3.post1
|
||||
PyYAML==6.0.1
|
||||
pyzmq==25.1.1
|
||||
qtconsole==5.5.1
|
||||
QtPy==2.4.1
|
||||
referencing==0.31.1
|
||||
requests==2.31.0
|
||||
rfc3339-validator==0.1.4
|
||||
rfc3986-validator==0.1.1
|
||||
rpds-py==0.13.2
|
||||
seaborn==0.13.0
|
||||
Send2Trash==1.8.2
|
||||
six==1.16.0
|
||||
sniffio==1.3.0
|
||||
soupsieve==2.5
|
||||
stack-data==0.6.3
|
||||
tenacity==8.2.3
|
||||
terminado==0.18.0
|
||||
tinycss2==1.2.1
|
||||
tomli==2.0.1
|
||||
tornado==6.4
|
||||
traitlets==5.14.0
|
||||
types-python-dateutil==2.8.19.14
|
||||
typing_extensions==4.8.0
|
||||
tzdata==2023.3
|
||||
uri-template==1.3.0
|
||||
urllib3==2.1.0
|
||||
wcwidth==0.2.12
|
||||
webcolors==1.13
|
||||
webencodings==0.5.1
|
||||
websocket-client==1.6.4
|
||||
widgetsnbextension==4.0.9
|
||||
281
DHT/retrieval_on_das_plotting.ipynb
Normal file
281
DHT/retrieval_on_das_plotting.ipynb
Normal file
@ -0,0 +1,281 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"import pandas as pd\n",
|
||||
"from IPython.display import display\n",
|
||||
"import warnings\n",
|
||||
"warnings.filterwarnings('ignore')\n",
|
||||
"\n",
|
||||
"from plots import make_folder, SingleMetrics, CombinedMetrics\n",
|
||||
"import plots\n",
|
||||
"\n",
|
||||
"# Necessary folders to start\n",
|
||||
"CSV_FOLDER = \"./csvs/retrieval_test\"\n",
|
||||
"IMG_FOLDER = \"./imgs/retrieval_test\"\n",
|
||||
"\n",
|
||||
"# make sure that the output folder exists\n",
|
||||
"make_folder(IMG_FOLDER, \"keeping track of the generated images\")\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Read all the available csv files in the given folder\n",
|
||||
"def read_files_with(target: str):\n",
|
||||
" files = []\n",
|
||||
" for dir, _, files in os.walk(CSV_FOLDER):\n",
|
||||
" for file in files:\n",
|
||||
" if target in file:\n",
|
||||
" files.append(dir+\"/\"+file)\n",
|
||||
" else:\n",
|
||||
" continue\n",
|
||||
" print(f\"found {len(files)} with {target} files in {CSV_FOLDER}\")\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Analysis of the data\n",
|
||||
"#### Individual metrics"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Individual metrics\n",
|
||||
"def print_avg_lookup(df):\n",
|
||||
" print(f\"lookup_wallclock_time\\t\\t\\t {df.lookup_wallclock_time.mean()}\")\n",
|
||||
" print(f\"attempted_nodes\\t\\t\\t\\t\\t {df.attempted_nodes.mean()}\")\n",
|
||||
" print(f\"finished_connection_attempts\\t {df.finished_connection_attempts.mean()}\")\n",
|
||||
" print(f\"successful_connections\\t\\t\\t {df.successful_connections.mean()}\")\n",
|
||||
" print(f\"failed_connections\\t\\t\\t\\t {df.failed_connections.mean()}\")\n",
|
||||
" print(f\"total_discovered_nodes\\t\\t\\t {df.total_discovered_nodes.mean()}\")\n",
|
||||
" print(f\"retrievable\\t\\t\\t\\t\\t\\t {df.retrievable.mean()}\")\n",
|
||||
" print(f\"accuracy\\t\\t\\t\\t\\t\\t {df.accuracy.mean()}\")\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# Display the sigle metrics of the test individually\n",
|
||||
"files = read_files_with(\"retrieval_lookup_nn\")\n",
|
||||
"for file in files:\n",
|
||||
" df = pd.read_csv(file)\n",
|
||||
" print(\"\\nmax simulated lookup delay\")\n",
|
||||
" display(df.loc[df['lookup_aggregated_delay'].idxmax()])\n",
|
||||
"\n",
|
||||
" print(\"\\nmin simulated lookup delay\")\n",
|
||||
" display(df.loc[df['lookup_aggregated_delay'].idxmin()])\n",
|
||||
"\n",
|
||||
" print(\"\\navg simulated lookup delay\")\n",
|
||||
" print_avg_lookup(df)\n",
|
||||
" metrics = SingleMetrics(file, IMG_FOLDER, \"Retrievals\", {\n",
|
||||
" \"retrievable\": {\n",
|
||||
" \"title_tag\": \"retriebable\",\n",
|
||||
" \"xlabel_tag\": \"retriebable\",\n",
|
||||
" \"ylabel_tag\": \"\",\n",
|
||||
" },})"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"is_executing": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"#### Aggregated accross samples"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# aggregate metrics across runs\n",
|
||||
"files = read_files_with(\"lookup_nn\")\n",
|
||||
"unifiedMetrics = CombinedMetrics(\n",
|
||||
" files=files, aggregator=\"fast_delay_range\",\n",
|
||||
" operation=\"retrieval\",\n",
|
||||
" filters=[\"y0.125\", \"cdr50-75\"], output_image_folder=IMG_FOLDER,\n",
|
||||
" metrics={\n",
|
||||
" \"retrievable\": {\n",
|
||||
" \"title_tag\": \"retriebable\",\n",
|
||||
" \"xlabel_tag\": \"retriebable\",\n",
|
||||
" \"ylabel_tag\": \"\",\n",
|
||||
" },\n",
|
||||
" },\n",
|
||||
" legend=[\n",
|
||||
" plots.RETRIEVAL_NODES,\n",
|
||||
" plots.CONCURRENT_SAMPLES,\n",
|
||||
" plots.FAST_ERROR_RATE,\n",
|
||||
" plots.CONNECTION_DELAYS,\n",
|
||||
" plots.GAMMA,\n",
|
||||
" ])"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# example to reproduce the network details\n",
|
||||
"import seaborn as sns\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"file = CSV_FOLDER+\"/retrieval_lookup_network_nn12000_rn100_sampl100_fer10_ser0_cdr50-75_fdr50-100_sdr0_k20_a3_b20_y0.125_steps3.csv\"\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(file)\n",
|
||||
"data = df.groupby([\"from\", \"to\"]).count()\n",
|
||||
"data = data.reset_index()\n",
|
||||
"data = data.rename(columns={\"Unnamed: 0\": \"total_connections\"})\n",
|
||||
"data = data.sort_values(by=\"total_connections\", ascending=False)\n",
|
||||
"pivoted_data = data.pivot(index=\"from\", columns=\"to\", values=\"total_connections\").fillna(0)\n",
|
||||
"display(pivoted_data)\n",
|
||||
"\n",
|
||||
"# plot heatmap of connections\n",
|
||||
"cmap = sns.cm.rocket_r\n",
|
||||
"\n",
|
||||
"sns.set()\n",
|
||||
"plt.show()\n",
|
||||
"g = sns.heatmap(data=pivoted_data, xticklabels=\"to\", yticklabels=\"from\", cmap = cmap)\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# example to reproduce the network details\n",
|
||||
"import networkx as nx\n",
|
||||
"import plotly.graph_objects as go\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"file = CSV_FOLDER+\"/retrieval_lookup_network_nn12000_rn100_sampl100_fer10_ser0_cdr50-75_fdr50-100_sdr0_k20_a3_b20_y0.125_steps3.csv\"\n",
|
||||
"\n",
|
||||
"df = pd.read_csv(file)\n",
|
||||
"df = df.groupby([\"from\", \"to\"]).size().reset_index(name=\"count\")\n",
|
||||
"top_interactions = df.sort_values('count', ascending=False).head(10000) # top 10000 interactions\n",
|
||||
"display(top_interactions)\n",
|
||||
"\n",
|
||||
"G = nx.from_pandas_edgelist(top_interactions, 'to', 'from', ['count'])\n",
|
||||
"pos = nx.spring_layout(G)\n",
|
||||
"\n",
|
||||
"for node in G.nodes():\n",
|
||||
" G.nodes[node]['pos'] = list(pos[node])\n",
|
||||
"\n",
|
||||
"edge_x = []\n",
|
||||
"edge_y = []\n",
|
||||
"for edge in G.edges():\n",
|
||||
" x0, y0 = G.nodes[edge[0]]['pos']\n",
|
||||
" x1, y1 = G.nodes[edge[1]]['pos']\n",
|
||||
" edge_x.extend([x0, x1, None])\n",
|
||||
" edge_y.extend([y0, y1, None])\n",
|
||||
"\n",
|
||||
"node_x = [pos[node][0] for node in G.nodes()]\n",
|
||||
"node_y = [pos[node][1] for node in G.nodes()]\n",
|
||||
"\n",
|
||||
"edge_trace = go.Scatter(\n",
|
||||
" x=edge_x, y=edge_y,\n",
|
||||
" line=dict(width=0.5, color='#888'),\n",
|
||||
" hoverinfo='none',\n",
|
||||
" mode='lines')\n",
|
||||
"\n",
|
||||
"node_trace = go.Scatter(\n",
|
||||
" x=node_x, y=node_y,\n",
|
||||
" mode='markers',\n",
|
||||
" hoverinfo='text',\n",
|
||||
" marker=dict(\n",
|
||||
" showscale=True,\n",
|
||||
" colorscale='YlGnBu',\n",
|
||||
" size=10,\n",
|
||||
" colorbar=dict(\n",
|
||||
" thickness=15,\n",
|
||||
" title='Node Connections',\n",
|
||||
" xanchor='left',\n",
|
||||
" titleside='right'\n",
|
||||
" ),\n",
|
||||
" line_width=2))\n",
|
||||
"\n",
|
||||
"node_adjacencies = []\n",
|
||||
"node_text = []\n",
|
||||
"for node in G.nodes():\n",
|
||||
" adjacencies = list(G.adj[node]) # List of nodes adjacent to the current node\n",
|
||||
" num_connections = len(adjacencies)\n",
|
||||
"\n",
|
||||
" node_adjacencies.append(num_connections)\n",
|
||||
" node_text.append(f'Node id: {node}<br># of connections: {num_connections}')\n",
|
||||
"\n",
|
||||
"node_trace.marker.color = node_adjacencies\n",
|
||||
"node_trace.text = node_text\n",
|
||||
"\n",
|
||||
"fig = go.Figure(data=[edge_trace, node_trace],\n",
|
||||
" layout=go.Layout(\n",
|
||||
" title='Network of Top Address Interactions',\n",
|
||||
" titlefont_size=16,\n",
|
||||
" showlegend=False,\n",
|
||||
" hovermode='closest',\n",
|
||||
" margin=dict(b=0, l=0, r=0, t=0),\n",
|
||||
" annotations=[dict(\n",
|
||||
" text=\"Based on top interactions\",\n",
|
||||
" showarrow=False,\n",
|
||||
" xref=\"paper\", yref=\"paper\",\n",
|
||||
" x=0.005, y=-0.002)],\n",
|
||||
" xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),\n",
|
||||
" yaxis=dict(showgrid=False, zeroline=False, showticklabels=False))\n",
|
||||
" )\n",
|
||||
"fig.update_layout(title_text=\"DHT network's interactions\")\n",
|
||||
"fig.show()\n"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
7
DHT/utils.py
Normal file
7
DHT/utils.py
Normal file
@ -0,0 +1,7 @@
|
||||
|
||||
def getStrFromDelayRange(range):
|
||||
if range == None:
|
||||
delay = "0"
|
||||
else:
|
||||
delay = f"{range[0]}-{range[-1]}"
|
||||
return delay
|
||||
@ -26,7 +26,7 @@ cd das-research
|
||||
```
|
||||
python3 -m venv myenv
|
||||
source myenv/bin/activate
|
||||
pip3 install -r DAS/requirements.txt
|
||||
bash install_dependencies.sh
|
||||
```
|
||||
|
||||
## Run the simulator
|
||||
|
||||
21
install_dependencies.sh
Normal file
21
install_dependencies.sh
Normal file
@ -0,0 +1,21 @@
|
||||
VENV="./venv"
|
||||
|
||||
echo "Installing dependencies for DAS..."
|
||||
|
||||
# activate the venv or raise error if error
|
||||
source $VENV/bin/activate
|
||||
if [ $? -eq 0 ]; then
|
||||
echo "venv successfully sourced"
|
||||
else
|
||||
echo "unable to source venv at $VENV , does it exist?"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
# make sure that the submodule module is correctly downloaded
|
||||
git submodule update --init
|
||||
|
||||
# install requirements for DAS and py-dht and install the dht module from py-dht
|
||||
pip3 install -r DAS/requirements.txt
|
||||
pip3 install -r DHT/requirements.txt
|
||||
pip3 install -r py-dht/requirements.txt
|
||||
python -m pip install -e py-dht/
|
||||
1
py-dht
Submodule
1
py-dht
Submodule
@ -0,0 +1 @@
|
||||
Subproject commit 9da9d74c95ab5f24a3ffa0605560ed5b77a7901b
|
||||
6
study.py
6
study.py
@ -1,6 +1,7 @@
|
||||
#! /bin/python3
|
||||
|
||||
import time, sys, random, copy
|
||||
from datetime import datetime
|
||||
import importlib
|
||||
import subprocess
|
||||
from joblib import Parallel, delayed
|
||||
@ -33,9 +34,8 @@ def runOnce(config, shape, execID):
|
||||
sim.initLogger()
|
||||
sim.initValidators()
|
||||
sim.initNetwork()
|
||||
result = sim.run()
|
||||
result = sim.runBlockBroadcasting()
|
||||
sim.logger.info("Shape: %s ... Block Available: %d in %d steps" % (str(sim.shape.__dict__), result.blockAvailable, len(result.missingVector)), extra=sim.format)
|
||||
|
||||
if config.dumpXML:
|
||||
result.dump()
|
||||
|
||||
@ -79,7 +79,7 @@ def study():
|
||||
|
||||
logger.info("Starting simulations:", extra=format)
|
||||
start = time.time()
|
||||
results = Parallel(config.numJobs)(delayed(runOnce)(config, shape ,execID) for shape in config.nextShape())
|
||||
results = Parallel(config.numJobs)(delayed(runOnce)(config, shape, execID) for shape in config.nextShape())
|
||||
end = time.time()
|
||||
logger.info("A total of %d simulations ran in %d seconds" % (len(results), end-start), extra=format)
|
||||
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user