A Comparison of Synchronous and Asynchronous Distributed Particle Swarm Optimization for Edge Computing

Abstract

Edge computing needs to deal with concerns such as load balancing, resource provisioning, and workload placement as optimization problems. Particle Swarm Optimization (PSO) is a nature-inspired stochastic optimization approach that aims at iteratively improving a solution of a problem over a given objective. Utilising PSO in a distributed edge setting would allow the transfer of resource-intensive computational tasks from a central cloud to the edge, this providing a more efficient use of existing resources. However, there are challenges to meet performance and fault tolerance targets caused by the resource-constrained edge environment with a higher probability of faults. We introduce here distributed synchronous and asynchronous variants of the PSO algorithm. These two forms specifically target the performance and fault tolerance requirements in an edge network. The PSO algorithms distribute the load across multiple nodes in order to effectively realize coarse-grained parallelism, resulting in a significant performance increase.