Simple Load Balancing for Distributed Hash Tables

Distributed hash tables have recently become a useful building block for a variety of distributed applications. However, current schemes based upon consistent hashing require both considerable implementation complexity and substantial storage overhead to achieve desired load balancing goals. We argue in this paper that these goals can b e achieved more simply and more cost-effectively. First, we suggest the direct application of the "power of two choices" paradigm, whereby an item is stored at the less loaded of two (or more) random alternatives. We then consider how associating a small constant number of hash values with a key can naturally b e extended to support other load balancing methods, including load-stealing or load-shedding schemes, as well as providing natural fault-tolerance mechanisms.

Cluster-Based Optimizations for Distributed Hash Tables

We consider the problem of performing topological optimizations of distributed hash tables. Such hash tables include Chord and Tapestry and are a popular building block for distributed applications. Optimizing topologies over one dimensional hash spaces is particularly difficult as the higher dimensionality of the underlying network makes close fits unlikely. Instead, current schemes are limited to heuristically performing local optimizations finding the best of small random set of peers. We propose a new class of topology optimizations based on the existence of clusters of close overlay members within the underlying network. By constructing additional overlays for each cluster, a significant portion of the search procedure can be performed within the local cluster with a corresponding reduction in the search time. Finally, we discuss the effects of these additional overlays on spatial locality and other load balancing scheme.