Using Global Information for Load Balancing in DHTs

Distributed Hash Tables (DHT) with order-preserving hash functions require load balancing to ensure an even item-load over all nodes. While previous item-balancing algorithms only improve the load imbalance, we argue that due to the cost of moving items, the competing goal of minimizing the used network traffic must be addressed as well. We aim to improve on existing algorithms by augmenting them with approximations of global knowledge, which can be distributed in a DHT with low cost using gossip mechanisms. In this paper we present initial simulation-based results from a decentralized balancing scheme extended with knowledge about the average node load. In addition, we discuss future work including a centralized auction-based algorithm that will be used as a benchmark.