Measures of Fault Tolerance in Distributed Simulated Annealing

TL;DR

This paper analyzes fault tolerance measures in distributed simulated annealing, exploring failure sources, system behavior, and methods to enhance robustness, including implementation in MapReduce and hybrid tolerance techniques.

Contribution

It introduces a comprehensive analysis of fault tolerance in distributed simulated annealing, including implementation strategies and hybrid techniques for improved reliability.

Findings

Hybrid tolerance techniques improve fault resilience.

MapReduce implementation offers scalable fault management.

Proposed methods enhance reaching global optima despite failures.

Abstract

In this paper, we examine the different measures of Fault Tolerance in a Distributed Simulated Annealing process. Optimization by Simulated Annealing on a distributed system is prone to various sources of failure. We analyse simulated annealing algorithm, its architecture in distributed platform and potential sources of failures. We examine the behaviour of tolerant distributed system for optimization task. We present possible methods to overcome the failures and achieve fault tolerance for the distributed simulated annealing process. We also examine the implementation of Simulated Annealing in MapReduce system and possible ways to prevent failures in reaching the global optima. This paper will be beneficial to those who are interested in implementing a large scale distributed simulated annealing optimization problem of industrial or academic interest. We recommend hybrid tolerance technique to optimize the trade-off between efficiency and availability.