Distributed Slicing in Dynamic Systems

TL;DR

This paper introduces two gossip-based algorithms for distributed slicing in P2P systems, enabling scalable, efficient, and resilient resource partitioning in dynamic, large-scale environments.

Contribution

It proposes novel gossip algorithms for distributed slicing, improving scalability and resilience in dynamic P2P networks.

Findings

Algorithms are theoretically validated.

Algorithms are experimentally validated.

Algorithms demonstrate scalability and resilience.

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 capable of dealing with resource assignment and management in a large-scale, heterogeneous and unreliable environment. 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 this paper we propose two gossip-based algorithms to solve the distributed slicing problem. The first algorithm speeds up an existing algorithm sorting a set of uniform random numbers. The second algorithm statistically approximates the rank of nodes in the ordering. The scalability, efficiency and resilience to dynamics of both algorithms rely on their gossip-based models. These algorithms are proved viable theoretically and experimentally.