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IEEE P2P 2013 - Bootstrapping Skynet: Calibration and Autonomic Self-Control of Structured...

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Timo Klerx and Kalman Graffi. Bootstrapping Skynet: Calibration and Autonomic Self-Control of Structured Peer-to-Peer Networks. In IEEE P2P ’13: Proceedings of the International Conference on Peer-to-Peer Computing, 2013. Abstract—Peer-to-peer systems scale to millions of nodes and provide routing and storage functions with best effort quality. In order to provide a guaranteed quality of the overlay functions, even under strong dynamics in the network with regard to peer capacities, online participation and usage patterns, we propose to calibrate the peer-to-peer overlay and to autonomously learn which qualities can be reached. For that, we simulate the peer- to-peer overlay systematically under a wide range of parameter configurations and use neural networks to learn the effects of the configurations on the quality metrics. Thus, by choosing a specific quality setting by the overlay operator, the network can tune itself to the learned parameter configurations that lead to the desired quality. Evaluation shows that the presented self-calibration succeeds in learning the configuration-quality interdependencies and that peer-to-peer systems can learn and adapt their behavior according to desired quality goals.
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Bootstrapping Skynet: Calibration and Autonomic Self-Control of Structured Peer-to-Peer Networks Timo Klerx and Kalman Graffi Department of Computer Science University of Paderborn Research Group Knowledge-Based Systems Hans Kleine Büning September 11, 2013 Knowledge-Based Systems UNIVERSITY OF PADERBORN
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  • 1. Bootstrapping Skynet:Calibration and Autonomic Self-Control ofStructured Peer-to-Peer NetworksTimo Klerx and Kalman GraffiDepartment of Computer ScienceUniversity of PaderbornResearch Group Knowledge-Based SystemsHans Kleine BningSeptember 11, 2013UNIVERSITY OF PADERBORNKnowledge-Based Systems

2. Motivation Approach Evaluation ConclusionOutline1 Motivation2 Approach3 Evaluation4 Conclusion & Future WorkBootstrapping Skynet Klerx and Graffi 1/17 3. Motivation Approach Evaluation ConclusionOutline1 Motivation2 Approach3 Evaluation4 Conclusion & Future WorkBootstrapping Skynet Klerx and Graffi 2/17 4. Motivation Approach Evaluation ConclusionBootstrapping SkyNetTowards self-optimizationSkyNet: Management layer in PeerfactSim.KOM(P2P-)Systems become more and more complexApplicationsParametersLayers. . .Ideally, systems manage themselvesChoose parametersDefend attacksRestore network structure. . .Bootstrapping Skynet Klerx and Graffi 3/17 5. Motivation Approach Evaluation ConclusionMAPEHow to achieve self-management?MonitorAnalyzePlanExecuteSystems implementing the MAPE circuit are autonomous.Everything except Planning is already implemented.Bootstrapping Skynet Klerx and Graffi 4/17 6. Motivation Approach Evaluation ConclusionOutline1 Motivation2 Approach3 Evaluation4 Conclusion & Future WorkBootstrapping Skynet Klerx and Graffi 5/17 7. Motivation Approach Evaluation ConclusionPlan PhaseIdeaOfflineGather data by simulationLearn the interdependencies in the dataConstruct a regressor with goal as input to compute parametervaluesOnlineDefine a desired goalAsk the regressor for optimal parameter valuesChange parameter values on every nodeBootstrapping Skynet Klerx and Graffi 6/17 8. Motivation Approach Evaluation ConclusionPlan PhaseIdeaOfflineGather data by simulationLearn the interdependencies in the dataConstruct a regressor with goal as input to compute parametervaluesOnlineDefine a desired goalAsk the regressor for optimal parameter valuesChange parameter values on every nodeBootstrapping Skynet Klerx and Graffi 6/17 9. Motivation Approach Evaluation ConclusionNeural NetworksBasicsClassification and regression(Often) supervised learning need labeled training dataLearn effects of parametersInput must be specified preciselyCan approximate arbitrary functions with arbitrary precisionBootstrapping Skynet Klerx and Graffi 7/17 10. Motivation Approach Evaluation ConclusionData GenerationData characteristicsThree types of figuresEnvironment Parameters (E, |E| = 5) Changed by all usersnode count, churn, . . .Overlay Parameters (O, |O| = 8) Changeable by single nodesmessage timeout, max hop count, . . .Metrics (M, |M| = 18) Performance valuesavg. hop count, avg. network messages in, . . .View as function f : E O ! MBootstrapping Skynet Klerx and Graffi 8/17 11. Motivation Approach Evaluation ConclusionData GenerationCombination approachesFull factorial designAQll possible combinations of parameters ni=1 |pi |Takes too much timeOne factorial designOnly one parameter varied at a timeRPest set to default values ni=1 |pi |Few data pointsMixed factorial designsjTradeoff between one and full factorial designSome parameters (E) in full factorial design, others (O) set toQdefault values ej Pt1 || =k=1 |ok |Bootstrapping Skynet Klerx and Graffi 9/17 12. Motivation Approach Evaluation ConclusionData GenerationCombination approachesFull factorial designAQll possible combinations of parameters ni=1 |pi |Takes too much timeOne factorial designOnly one parameter varied at a timeRPest set to default values ni=1 |pi |Few data pointsMixed factorial designsjTradeoff between one and full factorial designSome parameters (E) in full factorial design, others (O) set toQdefault values ej Pt1 || =k=1 |ok |Bootstrapping Skynet Klerx and Graffi 9/17 13. Motivation Approach Evaluation ConclusionData GenerationCombination approachesFull factorial designAQll possible combinations of parameters ni=1 |pi |Takes too much timeOne factorial designOnly one parameter varied at a timeRPest set to default values ni=1 |pi |Few data pointsMixed factorial designsjTradeoff between one and full factorial designSome parameters (E) in full factorial design, others (O) set toQdefault values ej Pt1 || =k=1 |ok |Bootstrapping Skynet Klerx and Graffi 9/17 14. Motivation Approach Evaluation ConclusionNeural NetworksLearn the data characteristicsRemember function f : E O ! MReorder f to ^f : M E ! OM: The preferred stateE: The current environment statev 2 ^f : (m1, ...,mr , e1, ..., es , o1, ..., ot )Approximate ^f : Predict the overlay parameter values when givenenvironment state and a goalOnly realistic goals as inputTrain with resilient backpropagationOne neural network for each overlay parameterSplit data in three disjoint sets: training, validation, predictionBootstrapping Skynet Klerx and Graffi 10/17 15. Motivation Approach Evaluation ConclusionNeural NetworksLearn the data characteristicsRemember function f : E O ! MReorder f to ^f : M E ! OM: The preferred stateE: The current environment statev 2 ^f : (m1, ...,mr , e1, ..., es , o1, ..., ot )Approximate ^f : Predict the overlay parameter values when givenenvironment state and a goalOnly realistic goals as inputTrain with resilient backpropagationOne neural network for each overlay parameterSplit data in three disjoint sets: training, validation, predictionBootstrapping Skynet Klerx and Graffi 10/17 16. Motivation Approach Evaluation ConclusionNeural NetworksLearn the data characteristicsRemember function f : E O ! MReorder f to ^f : M E ! OM: The preferred stateE: The current environment statev 2 ^f : (m1, ...,mr , e1, ..., es , o1, ..., ot )Metrics...EnvironmentParametersOverlayParameterHiddenLayer(s)...Approximate ^f : Predict the overlay parameter values when givenenvironment state and a goalOnly realistic goals as inputTrain with resilient backpropagationOne neural network for each overlay parameterSplit data in three disjoint sets: training, validation, predictionBootstrapping Skynet Klerx and Graffi 10/17 17. Motivation Approach Evaluation ConclusionOutline1 Motivation2 Approach3 Evaluation4 Conclusion & Future WorkBootstrapping Skynet Klerx and Graffi 11/17 18. Motivation Approach Evaluation ConclusionOverviewWhich generation approach leads to good results?Mixed factorial design (65,100 combinations)Most of the overlay parameter values are the default valueAlways predict "default" results in small errorsOne factorial design (80 combinations)Use feature selection (CFS and PCA)Not all parameters predicted successfullyBootstrapping Skynet Klerx and Graffi 12/17 19. Motivation Approach Evaluation ConclusionPrediction QualityError while/after trainingo1 =message timeouto6 =update fingertable intervalo7 =update neighbors intervalo8 =update successor intervalo2 =message resendo3 =operation timeouto4 =operation max. redoso5 =max hop count120100806040200o1 o2 o3 o4 o5 o6 o7 o8Error in PercentParametercfspcano fs(a) validation120100806040200o1 o2 o3 o4 o5 o6 o7 o8Error in PercentParametercfspcano fs(b) predictionFigure: Error on prediction and validation setBootstrapping Skynet Klerx and Graffi 13/17 20. Motivation Approach Evaluation ConclusionPrediction QualityError in the MAPE circuit without feature selection0.30.280.260.240.220.20.180.160.140.12w1 w1 w1 w2 w2 w2 w3 w3 w3Avg. Duration (m16)Message Timeout (o1)optimalpredicted200180160140120100806040200optimalpredictedw1 w1 w1 w2 w2 w2 w3 w3 w3Avg. Net Messages Out (m2)Update Fingertable Interval (o6)9080706050403020optimalpredictedw1 w1 w1 w2 w2 w2 w3 w3 w3Avg. Net Messages Out (m2)Update Neighbors Interval (o7)6560555045403530optimalpredictedw1 w1 w1 w2 w2 w2 w3 w3 w3Avg. Net Messages Out (m2)Update Successor Interval (o8)Bootstrapping Skynet Klerx and Graffi 14/17 21. Motivation Approach Evaluation ConclusionOutline1 Motivation2 Approach3 Evaluation4 Conclusion & Future WorkBootstrapping Skynet Klerx and Graffi 15/17 22. Motivation Approach Evaluation ConclusionConclusionMixed factorial design not suitableMAPE circuit closed in a proof-of-conceptFeature selection not beneficialEvaluation results are ambiguousGood results for some parameters, bad for othersBootstrapping Skynet Klerx and Graffi 16/17 23. Motivation Approach Evaluation ConclusionFuture WorkInvestigate the other parametersTry full factorial design with less parameters, but more granularDesign more metricsEmbed the implemented MAPE circuit in a real systemDecentralize the neural network(s) use local viewBootstrapping Skynet Klerx and Graffi 17/17 24. Motivation Approach Evaluation ConclusionFuture WorkInvestigate the other parametersTry full factorial design with less parameters, but more granularDesign more metricsEmbed the implemented MAPE circuit in a real systemDecentralize the neural network(s) use local viewThank you for your attention!Bootstrapping Skynet Klerx and Graffi 17/17 25. Parameter values EvaluationOutline5 Parameter values6 EvaluationBootstrapping Skynet Klerx and Graffi A-1 26. Parameter values EvaluationOverlay ParametersCode Overlay Parameter Unit defaulto1 Message Timeout s 10o2 Message Resend # 3o3 Operation Timeout s 120o4 Operation Max. Redos # 3o5 Max Hop Count # 50o6 Upd. Finger Table Intv. ms 30o7 Upd. Neighbors Intv. ms 30o8 Upd. Successor Intv. ms 30Bootstrapping Skynet Klerx and Graffi A-2 27. Parameter values EvaluationEnvironment ParametersCode Env. Parameter Unit defaulte1 Node Count # 1000e2 Churn Factor # 0e3 Mean Session Length s 1e4 Bandwidth MB/s OECDe5 Random Lookup Rate 1/h 30Bootstrapping Skynet Klerx and Graffi A-3 28. Parameter values EvaluationMetricsCode Metrics UnitMessagesm1 Avg. Network Message In #m2 Avg. Network Message Out #m3 Avg. Transport Message In #m4 Avg. Transport Message Out #m5 Avg. Forwarded Queries #m6 Avg. Service Message Throughput #/sm7 St. Dev. Service Message Throughput #/sm8 Avg. Service Message Count #m9 St. Dev. Service Message Count #Trafficm10 Avg. Network Bytes Sent kBm11 Avg. Transport Bytes Sent kBm12 Avg. Free Upload Bandwidth kB/sPerformancem13 Avg. Hop Count #m14 Avg. Lookup Hops #m15 St. Dev. Lookup Hops #m16 Avg. Lookup Duration sm17 St. Dev. Lookup Duration sm18 Avg. Operation Duration sBootstrapping Skynet Klerx and Graffi A-4 29. Parameter values EvaluationParameter VariationsCode Mixed Factorial One Factorialo1 5, 10, 20 2,3,4,5,8,10,12,15,18,20o2 0, 1, 3, 10 0,1,2,3,4,5,7,8,9o3 60, 120, 300 60,90,120,150,180,210,240,270,285,300o4 0, 1, 3, 10 0,1,2,3,4,5,7,8,9,10o5 5, 10, 25, 50, 100 3,5,7,10,17,35,50,75,100o6 3, 10, 30, 60 3,5,7,10,15,20,30,40,50,60o7 3, 10, 30, 60 3,5,7,10,15,20,30,40,50,60o8 3, 10, 30, 60 3,5,7,10,15,20,30,40,50,60e1 10, 33, 100, 330, 10001000, 3300, 10000e2 0, 110 , 310 0e3 30, 60; 180,1 1e4 OECD, random:1-2, 5-10, 10-30, 1-30OECDe5 0, 1, 3, 6, 30 30Bootstrapping Skynet Klerx and Graffi A-5 30. Parameter values EvaluationOutline5 Parameter values6 EvaluationBootstrapping Skynet Klerx and Graffi A-6 31. Parameter values EvaluationProcess of EvaluationParameterValueXParameterValueXMetricVectorMMetricVectorMNeuralNetworkSimulation SimulationcomparecompareBootstrapping Skynet Klerx and Graffi A-7 32. Parameter values EvaluationResults of EvaluationTable: Comparison of X, X0, M and M0Timestamp X X0 M M0 |1 MM0 | |1 XX0 |For o1 and m16w1 6s 6s 0.14 0.15 0.07 0.00w2 11s 11s 0.18 0.20 0.10 0.00w3 19s 18s 0.24 0.27 0.11 0.06For o6 and m2w1 4s 4s 175.00 173.66 0.01 0.00w2 25s 26s 39.60 38.34 0.03 0.04w3 55s 54s 24.50 25.51 0.04 0.02For o7 and m2w1 6s 4.6s 65.80 77.27 0.15 0.30w2 25s 27.5s 36.60 35.98 0.02 0.09w3 51s 55s 31.70 31.63 0.00 0.07For o8 and m2w1 4s 3.9s 60.21 60.90 0.01 0.03w2 37s 36s 34.27 34.10 0.00 0.03w3 55s 52s 33.21 33.00 0.01 0.06Bootstrapping Skynet Klerx and Graffi A-8


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