Distributed, Secure Load Balancing with Skew, Heterogeneity, and Churn

Jonathan Ledlie, Margo Seltzer

Abstract

Numerous proposals exist for load balancing in peer-to-peer (p2p) networks. Some focus on namespace balancing, making the distance between nodes as uniform as possible. This technique works well under ideal conditions, but not under those found empirically. Instead, researchers have found heavy-tailed query distributions (skew), high rates of node join and leave (churn), and wide variation in node network and storage capacity (heterogeneity). Other approaches tackle these less-than-ideal conditions, but give up on important security properties. We propose an algorithm that both facilitates good performance and does not dilute security. Our algorithm, k-Choices, achieves load balance by greedily matching nodes' target workloads with actual applied workloads through limited sampling, and limits any fundamental decrease in security by basing each node's set of potential identifiers on a single certificate. Our algorithm compares favorably to four others in trace-driven simulations. We have implemented our algorithm and found that it improved aggregate throughput by 20% in a widely heterogeneous system in our experiments.
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