A Decentralised Self-Healing Approach for Network Topology Maintenance

TL;DR

This paper introduces a multi-agent self-healing approach for network topology maintenance that uses local information dissemination, comparing Mobile Agents and Trickle protocols, to recover from node failures efficiently.

Contribution

It presents a novel multi-agent self-healing method for network recovery that leverages local data collection, with two strategies adapted and evaluated for resource efficiency.

Findings

Both strategies effectively recover network topology up to certain failure rates.

Mobile Agents reduce bandwidth but increase memory and message exchanges.

Trickle protocol uses less memory and messages but may require more bandwidth.

Abstract

In many distributed systems, from cloud to sensor networks, different configurations impact system performance, while strongly depending on the network topology. Hence, topological changes may entail costly reconfiguration and optimisation processes. This paper proposes a multi-agent solution for recovering networks from node failures. To preserve the network topology, the proposed approach relies on local information about the network's structure, which is collected and disseminated at runtime. The paper studies two strategies for distributing topological data: one based on Mobile Agents (our proposal) and the other based on Trickle (a reference gossiping protocol from the literature). These two strategies were adapted for our self-healing approach to collect topological information for recovering the network; and were evaluated in terms of resource overheads. Experimental results show that both variants can recover the network topology, up to a certain node failure rate, which depends on the network topology. At the same time, Mobile Agents collect less information, focusing on local dissemination, which suffices for network recovery. This entails less bandwidth overheads than when Trickle is used. Still, Mobile Agents utilise more memory and exchange more messages, during data-collection, than Trickle does. These results validate the viability of the proposed self-healing solution, offering two variant implementations with diverse performance characteristics, which may suit different application domains.