MachineLearningforNetworkingZi Yin

Dept.ofElectricalEngineeringJune9,2017

ML/NeuralNetworksinNetworking

DCN

DeliberateControl

ReflexControl

Workload

ReinforcementLearning(Control)

FunctionApproximation(Learning)

PatternRecognition(Inference/Learning)

Thistalk:MLforFunctionApproximation,LearningandControl

FunctionApproximation

• Exampleapproximable algorithms:- Lasso(inference)- SVM(classification)- MaxWt Matching- LoadBalancing- ViterbiDecoder,Kalman Filter(forparticularchannels)- etc

AlgorithmInput Output

NeuralNetwork

FAExample1:Lasso• Recall:LassousedforestimatingswitchqueuesfromTx andRxtimestampsofprobes/packets

LassoNoisyLinearEqn:

D=AQ+N EstimateofQ

NeuralNetwork

Algorithm Relativeerror

Lasso 9%

ReLUNN 7.4%

FAExample2:LearningSVM

ApproximatedByNeuralNetwork

SVM

NeuralNetwork

PatternRecognition

• Learnnetworkworkloadsandjobs– Machinetomachinecommunication– Jobidentification

• Mainquestion:– CanNLPandCVtechniquesbeusedtorecognizeandclassifypatternsinmachine-to-machinecommunication?

• Twoexamples:– UseRNNtopredictwebcacherequests– UseCNNtorecognizepatternsinSparkjobsfromnetworktrafficmatrices

• Givenatraceofweb requests{url1, url2, …} uptoi, predicttherequestati+1

• Important for making decisionson eviction, pre-fetching, …

MachineCommunication:Web Caching

TheMainSteps

NLPforhumanspeech1. Encodewordsasvectorsin

high-dimensionalspace– NNsperformarithmetic

operations;henceneedtotransformwordstovectors

2. UseanLSTMtoprocessstringofvectorsandpredictnextvector(word)– LSTMlearnsco-occurrence

relations

NLPforM2Mcomms1. Encodeurls asvectorsinhigh-

dimensionalspace

2. UseanLSTMtoprocessstringofvectorsandpredictnextword

ALanguageModelforWebCacheRequests

• Observation:hypotheticaluseralwaysusesasearchenginetologontoFacebook

• LanguageModelsfromNLP– URLsà ddimensionalvectors(enablesarithmetics)– Occurrencerelationsà LSTMneuralnetwork(learnspatterns)

• Data:URLrequesttrace(BostonUniversity)• Length: ~1M• 90k unique URLs• ThemostfrequentURLaccountsfor10%oftheentiretrace

• Using URL embedding + LSTM neural net: 47% prediction accuracy

• Using Lempel-Ziv: 26% accuracy- Lempel-Zivisknowntobeoptimalpredictor forstationary-ergodicsources

• Removing temporal correlationsbyrandomlypermuting the trace- accuracy droppedto9%

Prediction of Next WebRequest

CPUCacheTrace• OptimizeCPUcachingtohelpminimizeI/Otime

• Importantdecisionmaking:evictionandpre-fetching

• LearnCPUrequestsequence

Methodology• Embedding+LSTMmodels

• Tested on 4 Spec2006 CPU traces– hmmer-100M.trace.gz (Biosequence analysisusinghiddenMarkovmodels)

– sjeng-100M.trace.gz (PlaysthegameofChess)– sphinx3-100M.trace.gz (SpeechtotextprogramfromCMU)– art-100M.trace.gz (Imagerecognitionusingneuralnetworks)

Predict Next Memory AccessUsingLSTM UsingMostFrequent UsingLast

sphinx3-100M.trace.gz 3.2% 2.6% 0.7%

mcf-100M.trace.gz 5.7% 4.9% 0.5%

sjeng-100M.trace.gz 3.0% 2.6% 0.0%

hmmer-100M.trace.gz 2.7% 2.7% 0.7%

• Resultsdependontheprogram,butalwaysbetterthansimpleschemes

Job Identification• Classify jobs by observing traffic

• Spark SQL query with 3 nodes– Traffic matrix (3 nodes):

– 30 second with 1 second aggregateà 3x90 matrix

JobIdentification• RecurringSparkjobs

• Sameprogram,dataandconfiguration

• Canbewellclassifiedbyaconvolutionalneuralnetwork

JobIdentification• Classifywith90%accuracyusingfirst20secondsoftraffic

• Afterjobisidentified,futurenetworkusagecanbeinferredtoplanaheadandoptimizeperformance

Summary• Neural networks can learn many known algorithms

• Neural networks can be used to learn machinetomachinecommunicationand jobs

• Optimization can be done based on the knowledge learnedbytheneuralnetworks

Thankyou