An improved strategy of piece selection in P2P
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
A self-adaptive load balancing strategy for p2p grids
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part II
Virtual machine placement for predictable and time-constrained peak loads
GECON'11 Proceedings of the 8th international conference on Economics of Grids, Clouds, Systems, and Services
A dynamic load balancing strategy with the push and pull approaches in DHT networks
Computers and Electrical Engineering
A dynamic load balancing scheme with incentive mechanism in heterogeneous structured P2P networks
Computers and Electrical Engineering
Web search results caching service for structured P2P networks
Future Generation Computer Systems
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Application layer peer to peer (P2P) networks are considered to be the most important development for next generation Internet infra-structure. For these systems to be effective, load balancing among the peers is critical. Most structured P2P systems rely on ID-space partitioning schemes to solve the load imbalance problem, and has been known to result in an imbalance factor of Θ(log N) in the zone sizes. This paper makes two contributions. First, we propose to address the virtual server-based load balancing problem systematically using an optimization based approach, and derived an effective algorithm to re-arrange loads among the peers. We demonstrate the superior performance of our proposal in general, and its advantages over previous strategies in particular. We also explore other important issues vital to the performance in the virtual server framework, such as the effect of the number of directories employed in the system, and the performance ramification of user registration strategies. Secondly, and perhaps more significantly, we characterize systematically the effect of heterogeneity on load balancing algorithm performance, and the conditions in which heterogeneity may be easy or hard to deal with based on extensive study of a wide spectrum of load and capacity scenarios.