An adaptive stabilization framework for distributed hash tables

Distributed hash tables (DHT) algorithms obtain good lookup performance bounds by using deterministic rules to organize peer nodes into an overlay network. To preserve the invariants of the overlay network, DHTs use stabilization procedures that reorganize the topology graph when participating nodes join or fail. Most DHTs use periodic stabilization, in which peers perform stabilization at fixed intervals of time, disregarding the rate of change in overlay topology; this may lead to poor performance and large stabilization-induced communication overhead. We propose a novel adaptive stabilization framework that takes into consideration the continuous evolution in network conditions. Each peer collects statistical data about the network and dynamically adjusts its stabilization rate based on the analysis of the data. The objective of our scheme is to maintain nominal network performance and to minimize the communication overhead of stabilization.