Designing a wide-area distributed hash table (DHT) that provides high-throughput and low-latency network storage is a challenge. Existing systems have explored a range of solutions, including iterative routing, recursive routing, proximity routing and neighbor selection, erasure coding, replication, and server selection. This

The routing tables of Distributed Hash Tables (DHTs) can vary from size O(1) to O(n). Currently, what is lacking is an analytic framework to suggest the optimal routing table size for a given workload. This paper (1) compares DHTs with O(1) to O(n) routing tables and identifies some good design points; and (2) proposes protocols to realize the potential of those good design points. We use total traffic as the uniform metric to compare heterogeneous DHTs and emphasize the balance between maintenance cost and lookup cost. Assuming a node on average processes 1,000 or more lookups during its entire lifetime, our analysis shows that large routing tables actually lead to both low traffic and low lookup hops. These good design points translate into one-hop routing for systems of medium size and two-hop routing for large systems. Existing one-hop or two-hop protocols are based on a hierarchy. We instead demonstrate that it is possible to achieve completely decentralized one-hop or two-hop routing, i.e., without giving up being peer-to-peer. We propose 1h-Calot for one-hop routing and 2h-Calot for two-hop routing. Assuming a moderate lookup rate, compared with DHTs that use O(log n) routing tables, 1h-Calot and 2h-Calot save traffic by up to 70 % while resolving lookups in one or two hops as opposed to O(log n) hops.

Epidemic broadcast algorithms have a number of characteristics, such as strong resilience to node failures, that make them an appealing technology to build survivable systems. However, the performance of existing protocols is highly dependent of run-time parameters, such as the network load or network topology. In the current extended abstract we advocate, using concrete illustrations, the use of self-adapting epidemic broadcast algorithms.

In this paper we propose a novel probabilistic broadcast protocol that reduces the average end-to-end latency by dynamically adapting to network topology and traffic conditions. It does so by using an unique strategy that consists in adjusting the fanout and preferred targets for different gossip rounds as a function of the properties of each node. Node classification is light-weight and integrated in the protocol membership management. Furthermore, each node is not required to have full knowledge of the group membership or of the network topology. The paper shows how the protocol can be configured and evaluates its performance with a detailed simulation model. 1