STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
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SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
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IEEE/ACM Transactions on Networking (TON)
Pastry: Scalable, Decentralized Object Location, and Routing for Large-Scale Peer-to-Peer Systems
Middleware '01 Proceedings of the IFIP/ACM International Conference on Distributed Systems Platforms Heidelberg
Measurement, modeling, and analysis of a peer-to-peer file-sharing workload
SOSP '03 Proceedings of the nineteenth ACM symposium on Operating systems principles
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IEEE Transactions on Parallel and Distributed Systems
Locality-Aware Randomized Load Balancing Algorithms for DHT Networks
ICPP '05 Proceedings of the 2005 International Conference on Parallel Processing
Elastic Routing Table with Provable Performance for Congestion Control in DHT Networks
ICDCS '06 Proceedings of the 26th IEEE International Conference on Distributed Computing Systems
Hash-based proximity clustering for load balancing in heterogeneous DHT networks
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Decentralized proactive resource allocation for maximizing throughput of P2P Grid
Journal of Parallel and Distributed Computing
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Load balance is an important problem in the DHT-based P2P networks. In recent years, many solutions have been proposed to address this problem. However, these solutions have some limitations in our opinion. They either make some unrealistic assumptions about the network, or have high communication or maintenance overhead. In this paper, we present a distributed load balancing algorithm for the hypercube-based DHT networks. Our algorithm is based on the concept of fairness and uses the fairness index as the fairness metric. The purpose of our algorithm is to distribute the query load fairly to nodes. Each node periodically monitors the fairness index of current load distribution by using only local computation and it tries to achieve a fairer load distribution by dynamically adjusting its indegree according to its experienced load and the fairness index. The results of our experiments show that our algorithm has low overhead and it can achieve good load balance without unrealistic assumptions about the network.