Exploiting Availability Prediction in Distributed SystemsJames W. Mickens and Brian D. NobleReview by Mario Almeida (EMDC)

Problem statement

What is the problem addressed?Distributed Systems have scalability and cost advantages, but also introduce issues such as availability of nodes. This paper presents techniques for predicting availability such as availability-guided replica placement, improvement routing in delay-tolerant networks and combining availability prediction with virus modeling. It claims that this way one can reduce overheads by predicting availability and planning for changing availability instead of reacting to it.

Why is it important?Because availability can be crucial for keeping a service functionality as well as replication of contents. Availability of nodes can have a direct impact on the overall performance of any distributed system, such as churn that can lead to significant overheads.

Proposal

What is the proposed solution?The paper proposes the definition of new techniques for predicting availability. They used multiple predictors such as the RightNow Predictor, SatCount Predictor, state-based predictor, TwiddledHistory Predictor, Linear Predictor and a Hybrid predictor that changes predictor depending on lookahead periods.

Hypotheses

What were the expected effects of the proposed solution?The paper describes three applications using availability prediction. They expected the first application, a modified DHT with availability-aware replica placement, to transmit fewer objects for regeneration and to have a better data availability. The second application to have a better routing performance and the last to combine virus modeling with availability predicting for achieving better forecasts.

Experiments

What were the experiments?The hybrid predictor was tested using PlanetLab and Microsoft traces. They also used overnet traces to model uptime patterns.In order to experiment on decentralized availability data, the three case studies mentioned before 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.

Results

What were the results?The availability predictors introduced in this paper have shown that the overhead can be reduced 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. And finally, forecast of global infection is improved by combining availability predictions with virus.