A Geographically Aware Poll-Based Distributed File Consistency Maintenance Method for P2P Systems

TLDR: A poll-based distributed file consistency maintenance method called geographically aware wave (GeWave), which dramatically reduces the overhead and yields significant improvements on effectiveness, scalability, and churn resilience of previous consistency maintenance methods.

Abstract: File consistency maintenance in P2P systems is a technique for maintaining consistency between files and their replicas. Most previous consistency maintenance methods depend on either message spreading or structure-based pushing. Message spreading generates high overhead due to a large amount of messages; structure-based pushing methods reduce this overhead. However, both approaches cannot guarantee that every replica node receives an update in churn, because replica nodes passively wait for updates. As opposed to push-based methods that are not effective in high-churn and low-resource P2P systems, polling is churn resilient and generates low overhead. However, it is faced with a number of challenges: 1) ensuring a limited inconsistency; 2) realizing polling in a distributed manner; 3) considering physical proximity in polling; and 4) leveraging polling to further reduce polling overhead. To handle these challenges, this paper introduces a poll-based distributed file consistency maintenance method called geographically aware wave (GeWave). GeWave further reduces update overhead, enhances the fidelity of file consistency, and takes proximity into account. Using adaptive polling in a dynamic structure, GeWave avoids redundant file updates and ensures that every node receives an update in a limited time period even in churn. Furthermore, it propagates updates between geographically close nodes in a distributed manner. Extensive experimental results from the PlanetLab real-world testbed demonstrate the efficiency and effectiveness of GeWave in comparison with other representative consistency maintenance schemes. It dramatically reduces the overhead and yields significant improvements on effectiveness, scalability, and churn resilience of previous file consistency maintenance methods.