Multiple-Choice Random Network for Server Load Balancing

TL;DR: This paper uses hypercube as an example and demonstrates that replicating files at nodes in decreasing order of the length of the common prefix with the original server leads to perfectly balanced load, and finds a multiple-choice random network which drastically reduces the statistical fluctuation of the load.

Abstract: In many networking applications such as file sharing, structured peer-to-peer networks are increasingly used in dynamic situations with fluctuating load, which require proper load balancing The relationship between the network structure and its load-balancing properties has not been fully understood In this paper, we focus on the Plaxton-type networks, which are broad enough to include Pastry, Tapestry, and hypercube We first use hypercube as an example and demonstrate that replicating files at nodes in decreasing order of the length of the common prefix with the original server leads to perfectly balanced load, and does so fast and efficiently Moreover, this replication strategy coincides with a simple on-demand replication/caching strategy based on the observed load One of our main contributions is to show that such desirable properties also exist for a large class of random networks, which are less restrictive and more practical than the hypercube More importantly, we have discovered a multiple-choice random network, which drastically reduces the statistical fluctuation of the load: The maximum load over all replication servers is at most three times the average load for systems of practical sizes The main insight is that this algorithm is related to a variant of the multiple-choice balls-in-bins problem