Performance Management in Clustered Edge Architectures Using Particle Swarm Optimization

Abstract

Recently distributed computing capacities are brought to the edge of the Internet, permitting Internet-of-Things applications to process calculation all the more locally and subsequently more productively and this has brought a totally different scope of apparatuses and usefulness. This instrument can The most significant characterizing highlights of edge processing are low latency, location awareness, wide geographic distribution, versatility, support for countless nodes, etc. We want to likely limit the latency and delay in edge-based structures. We center around a progressed compositional setting that considers communication and processing delays and the management effort notwithstanding a real request execution time in an operational efficiency situation. Our design is based on multi-cluster edge layer with nearby autonomous edge node clusters. We will contend that particle swarm optimization as a bio-motivated optimization approach is a perfect candidate for distributed IoT load handling in self-managed edge clusters. By designing a controller and utilizing a particle swarm optimization algorithm, we show that delay and end-to-end latency can be reduced.