Distributed Slicing in Dynamic Systems

TL;DR

This paper introduces two gossip-based algorithms for distributed slicing in P2P systems, improving scalability, efficiency, and resilience in dynamic, large-scale environments.

Contribution

It proposes novel gossip-based algorithms for distributed slicing, enhancing convergence speed and accuracy in dynamic P2P networks.

Findings

Both algorithms are scalable and resilient to churn.

Experimental results confirm the effectiveness of the algorithms.

Theoretical analysis supports their convergence and robustness.

Abstract

Peer to peer (P2P) systems are moving from application specific architectures to a generic service oriented design philosophy. This raises interesting problems in connection with providing useful P2P middleware services that are capable of dealing with resource assignment and management in a large-scale, heterogeneous and unreliable environment. One such service, the slicing service, has been proposed to allow for an automatic partitioning of P2P networks into groups (slices) that represent a controllable amount of some resource and that are also relatively homogeneous with respect to that resource, in the face of churn and other failures. In this report we propose two algorithms to solve the distributed slicing problem. The first algorithm improves upon an existing algorithm that is based on gossip-based sorting of a set of uniform random numbers. We speed up convergence via a heuristic for gossip peer selection. The second algorithm is based on a different approach: statistical approximation of the rank of nodes in the ordering. The scalability, efficiency and resilience to dynamics of both algorithms relies on their gossip-based models. We present theoretical and experimental results to prove the viability of these algorithms.