入侵檢測系統(tǒng)中告警分析和關(guān)聯(lián)方法研究課件

APrediction-basedFairReplicationAlgorithminStructuredP2PSystemsXianshuZhu,DafangZhang,WenjiaLi,KunHuangPresentedby:XianshuZhuCollegeofComputer&Communication,HunanUniversity,P.R.ChinaAPrediction-basedFairReplic1OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction2IntroductionQueryHotspotStructuredPeer-to-PeerNetworkSummaryofReplicationSchemes

IntroductionQueryHotspot3QueryHotspot

FGIJCDEHBFileQueryHotspot:thenumberofrequestsforpopularobjectsincreasesdramatically,andleadstoconsequentdroppingqueriesandsevereperformancefailures.QueryHotspotQueryHotspotFGIJCDEHBFileQue4StructuredP2PNetworkAdvantage：

-Scalability-EfficientSearchingDisadvantage：TheImplementationofStructuredP2PNetworkAssumesthatAllDataItemsareoftheSamePopularity.NoMechanismCanHandleHotspotProblem

StructuredP2PNetworkAdvantag5ReplicationSchemesBasicIdea：-DistributeReplicasofthePopularDataItemstoVariousLight-loadedNodes-Fairly

DistributeLoadontoEachNode.WhenApplyReplicationTechnique:－ReplicaCreation:Time,Number,Location－ReplicaUtilization

ReplicationSchemesBasicIdea：6ReplicationSchemesClassificationAccordingtoReplicaLocation:-PathReplication-OwnerReplication-RandomReplicationABCDEFFileFileFileFileFileFileHighReplicationOverheadReplicationSchemesClassificat7ReplicationSchemesABCDEFFileA1.NewQueryHotspot2.LowReplicationSpeedClassificationAccordingtoReplicaLocation:-PathReplication

-OwnerReplication:GopalakrishnanproposedLAR-RandomReplicationFileBFileDFileDFileBFileAReplicationSchemesABCDEFFile8ReplicationSchemesABCDEFFileFileFileFileClassificationAccordingtoReplicaLocation:-PathReplication-OwnerReplication

-RandomReplicationReplicationSchemesABCDEFFileF9OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction10ContributionDesignGoals:-DroppedQueriesbyOnlyIntroducingMinimumReplicationOverhead-MinimizetheDrawbacksofLARAlgorithm

(OwnerReplication)Prediction-basedFairReplicationAlgorithm(PFR)thatCanAlmostFairlyDistributeLoadontoEachNode,SoAstoMeettheAboveDesignGoal.

ContributionDesignGoals:11ContributionFairnessGoalofPFR

-AdaptivelyDeterminetheReplicationSpeedandReplicationLocationAccordingtoNode’sPredictedLoadFractionABCDEFGContributionFairnessGoalofP12OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction13Predict(n+1)PFR-AppropriateReplicationTime

TokeeptheSystemPerformanceataHighLevel,PreventiveActionsShouldbeTakenBeforeQueryHotspotReallyHappensPeriodExponentialWeightPredictionAlgorithmPredict(n+1)=Current(n)+PredictDiff(n+1)12nn+1n-1CurrentTimePredictedPossibleTrafficDifferenceBetweennthand(n+1)thInterval

Predict(n+1)PFR-AppropriateR14PeriodExponentialWeightPredictionAlgorithm-OnlyIncursLowComputationOverhead-ApplicabletoOnlinePredictionOurReplicationStrategyisSetBasedonThePredictedload

PFR-AppropriateReplicationTime

PeriodExponentialWeightPred15ReplicationSpeed:ABCDEFFileFileFileFile3/6ReplicationSpeed=(theNumberofNodesChosentoHoldReplicas)/(theNumberofAllNodesthatHaveEncounteredAlongtheQueryPath)PFR-Fairly-decidedReplicationSpeedReplicationSpeed:ABCDEFFileFi16ReplicationLevel:NN/23N/4N/41DON’TcreatereplicasN:TotalNumberofNodesAlongaQueryPathPFR-Fairly-decidedReplicationSpeedReplicationSpeedPredictedLoadFraction(0.5)(0.3)(0.6)(0.7)(0.8)(1)NodeHomogeneityReplicationLevel:NN/23N/4N/4117PFR-Replication&ReplicaUtilizationABCDEFGC:FileF:0.25E:0.15F:0.25E:0.15F:0.25D:0.3C:0.55E:0.15F:0.25D:0.3B:0.3C:0.55E:0.15F:0.25D:0.3A:0.9B:0.3C:0.55E:0.15F:0.25D:0.3RS:N/4=1A:FileA:FileA:FileA:FileA:FileRS:NE:CE:CE:CB,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:AD:AN=6PFR-Replication&ReplicaUti18OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction19PerformanceEvaluation

HighlymodifiedChordSimulatorfromMITandLARImplementationCode：SystemSize1000TheTimeEachNetworkhoptakes25msNumberofdata32767Averagesystemload25%Nodecapacity10persecNumberofQueriesGenerateperSec500Node’squeuelength32Predictioninterval1sPerformanceEvaluationHighly20PerformanceEvaluationNumberofQueriesDroppedOverTime

28%90%oftheinputqueriesaredirectedto1

itemLARPFRPerformanceEvaluationNumbero21PerformanceEvaluationTotalNumberofDocumentsReplicatedLARPFRPerformanceEvaluationTotalNu22PerformanceEvaluationTotalNumberofFingerTablesReplicatedLARPFRPerformanceEvaluationTotalNu23PerformanceEvaluationTotalNumberofReplicaLocationHintsCreatedPFRLARPerformanceEvaluationTotalNu24OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction25ConclusionPrediction-basedFairReplicationAlgorithmCanConductFairReplicationthrough:-AppropriateReplicationTime-Fairly-decidedReplicationSpeed-Fairly-decidedReplicationLocation-HighReplicaUtilizationRatePerformanceEvaluation:-NotablyDecreasetheNumberofDroppedQueries-LowReplicationOverhead

ConclusionPrediction-basedFai26FutureWorkTakingNodeHeterogeneityintoConsiderationFutureWorkTakingNodeHeterog27Thankyou!Thankyou!28APrediction-basedFairReplicationAlgorithminStructuredP2PSystemsXianshuZhu,DafangZhang,WenjiaLi,KunHuangPresentedby:XianshuZhuCollegeofComputer&Communication,HunanUniversity,P.R.ChinaAPrediction-basedFairReplic29OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction30IntroductionQueryHotspotStructuredPeer-to-PeerNetworkSummaryofReplicationSchemes

IntroductionQueryHotspot31QueryHotspot

FGIJCDEHBFileQueryHotspot:thenumberofrequestsforpopularobjectsincreasesdramatically,andleadstoconsequentdroppingqueriesandsevereperformancefailures.QueryHotspotQueryHotspotFGIJCDEHBFileQue32StructuredP2PNetworkAdvantage：

-Scalability-EfficientSearchingDisadvantage：TheImplementationofStructuredP2PNetworkAssumesthatAllDataItemsareoftheSamePopularity.NoMechanismCanHandleHotspotProblem

StructuredP2PNetworkAdvantag33ReplicationSchemesBasicIdea：-DistributeReplicasofthePopularDataItemstoVariousLight-loadedNodes-Fairly

DistributeLoadontoEachNode.WhenApplyReplicationTechnique:－ReplicaCreation:Time,Number,Location－ReplicaUtilization

ReplicationSchemesBasicIdea：34ReplicationSchemesClassificationAccordingtoReplicaLocation:-PathReplication-OwnerReplication-RandomReplicationABCDEFFileFileFileFileFileFileHighReplicationOverheadReplicationSchemesClassificat35ReplicationSchemesABCDEFFileA1.NewQueryHotspot2.LowReplicationSpeedClassificationAccordingtoReplicaLocation:-PathReplication

-OwnerReplication:GopalakrishnanproposedLAR-RandomReplicationFileBFileDFileDFileBFileAReplicationSchemesABCDEFFile36ReplicationSchemesABCDEFFileFileFileFileClassificationAccordingtoReplicaLocation:-PathReplication-OwnerReplication

-RandomReplicationReplicationSchemesABCDEFFileF37OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction38ContributionDesignGoals:-DroppedQueriesbyOnlyIntroducingMinimumReplicationOverhead-MinimizetheDrawbacksofLARAlgorithm

(OwnerReplication)Prediction-basedFairReplicationAlgorithm(PFR)thatCanAlmostFairlyDistributeLoadontoEachNode,SoAstoMeettheAboveDesignGoal.

ContributionDesignGoals:39ContributionFairnessGoalofPFR

-AdaptivelyDeterminetheReplicationSpeedandReplicationLocationAccordingtoNode’sPredictedLoadFractionABCDEFGContributionFairnessGoalofP40OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction41Predict(n+1)PFR-AppropriateReplicationTime

TokeeptheSystemPerformanceataHighLevel,PreventiveActionsShouldbeTakenBeforeQueryHotspotReallyHappensPeriodExponentialWeightPredictionAlgorithmPredict(n+1)=Current(n)+PredictDiff(n+1)12nn+1n-1CurrentTimePredictedPossibleTrafficDifferenceBetweennthand(n+1)thInterval

Predict(n+1)PFR-AppropriateR42PeriodExponentialWeightPredictionAlgorithm-OnlyIncursLowComputationOverhead-ApplicabletoOnlinePredictionOurReplicationStrategyisSetBasedonThePredictedload

PFR-AppropriateReplicationTime

PeriodExponentialWeightPred43ReplicationSpeed:ABCDEFFileFileFileFile3/6ReplicationSpeed=(theNumberofNodesChosentoHoldReplicas)/(theNumberofAllNodesthatHaveEncounteredAlongtheQueryPath)PFR-Fairly-decidedReplicationSpeedReplicationSpeed:ABCDEFFileFi44ReplicationLevel:NN/23N/4N/41DON’TcreatereplicasN:TotalNumberofNodesAlongaQueryPathPFR-Fairly-decidedReplicationSpeedReplicationSpeedPredictedLoadFraction(0.5)(0.3)(0.6)(0.7)(0.8)(1)NodeHomogeneityReplicationLevel:NN/23N/4N/4145PFR-Replication&ReplicaUtilizationABCDEFGC:FileF:0.25E:0.15F:0.25E:0.15F:0.25D:0.3C:0.55E:0.15F:0.25D:0.3B:0.3C:0.55E:0.15F:0.25D:0.3A:0.9B:0.3C:0.55E:0.15F:0.25D:0.3RS:N/4=1A:FileA:FileA:FileA:FileA:FileRS:NE:CE:CE:CB,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:AB,D,E,F:AD:AN=6PFR-Replication&ReplicaUti46OutlineIntroductionContributionPFR(Prediction-basedFairReplication)PerformanceEvaluationConclusionandFutureWorkOutlineIntroduction47PerformanceEvaluation

HighlymodifiedChordSimulatorfromMITandLARImplementationCode：SystemSize1000TheTimeEachNetworkhoptakes25msNumberofdata32767Averagesystemload25%Nodecapacity10persecNumberofQueriesGeneratepe