Enhancing collaborative peer-to-peer systems using resource aggregation and caching

Abstract

We envision Peer-to-Peer (P2P) systems that allow for the integration and collaboration of peers with diverse capabilities to form virtual communities. Such communities will be able to engage in greater tasks beyond what can be accomplished by individual peers, yet are beneficial to all the peers. These emerging systems will share a variety of resources such as processor cycles, storage capacity, network bandwidth, sensors/actuators, services, middleware, scientific algorithms, and data. Collaborations involving application-specific resources and dynamic quality of service goals will stress current P2P architectures that are designed for best-effort environments with pairwise interactions among nodes with similar resources. Collaborative Peer-to-Peer (P2P) systems require resource discovery solutions to aggregate groups of multi-attribute, dynamic, and distributed resources. Resource and query aware P2P-based multi-attribute resource discovery solutions will be addressed. A distributed caching solution that exploits P2P communities to improve the communitywide and system-wide lookup performance will be presented, with a view to extend it to multi-attribute systems. We analyze the characteristics of resources and queries using data from four real-world systems. A set of mechanisms will be addressed to generate realistic synthetic traces of multi-attribute static and dynamic resources, and range queries, using the statistical behavior learned from real-world datasets. Such traces are useful in large-scale performance studies of resource discovery solutions, job schedulers, etc., not only in collaborative P2P systems, but also in cloud computing.