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Exploiting Availability Prediction in Distributed Systems

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Exploiting Availability Prediction in Distributed Systems

  1. 1. Exploiting AvailabilityPrediction in DistributedSystemsJames W. Mickens and Brian D. NobleReview by Mario Almeida (EMDC)Problem statementWhat is the problem addressed?Distributed Systems have scalability and cost advantages, but also introduce issues suchas availability of nodes. This paper presents techniques for predicting availability such asavailability-guided replica placement, improvement routing in delay-tolerant networks andcombining availability prediction with virus modeling. It claims that this way one can reduceoverheads by predicting availability and planning for changing availability instead of reacting toit.Why is it important?Because availability can be crucial for keeping a service functionality as well as replicationof contents. Availability of nodes can have a direct impact on the overall performance of anydistributed system, such as churn that can lead to significant overheads.ProposalWhat is the proposed solution?The paper proposes the definition of new techniques for predicting availability. They usedmultiple predictors such as the RightNow Predictor, SatCount Predictor, state-based predictor,TwiddledHistory Predictor, Linear Predictor and a Hybrid predictor that changes predictordepending on lookahead periods.
  2. 2. HypothesesWhat were the expected effects of the proposed solution?The paper describes three applications using availability prediction. They expected the firstapplication, a modified DHT with availability-aware replica placement, to transmit fewer objectsfor regeneration and to have a better data availability. The second application to have a betterrouting performance and the last to combine virus modeling with availability predicting forachieving better forecasts.ExperimentsWhat were the experiments?The hybrid predictor was tested using PlanetLab and Microsoft traces. They also used overnettraces to model uptime patterns.In order to experiment on decentralized availability data, the three case studies mentionedbefore were used : ● simulation of Chord DHT in order to see how availability-guided data placement reduces copy overhead by using replica sites; ● message delay in delay-tolerant networks; ● combine virus modeling with availability predicting for achieving better forecasts.ResultsWhat were the results?The availability predictors introduced in this paper have shown that the overhead can bereduced by planning of changing availability. In the first case test, by using replica storage,bandwidth consumption was reduced and data availability was increased. For the second case,latencies in the delay-tolerant network were decreased by using availability predictors. Andfinally, forecast of global infection is improved by combining availability predictions with virus.

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