Particle Swarm Optimization for Performance Management in Multi-cluster IoT Edge Architectures
Shelernaz Azimi, Claus Pahl, Mirsaeid Hosseini Shirvani
Abstract
Edge computing extends cloud computing capabilities to the edge of the network, allowing for instance Internet-of-Things (IoT) applications to process computation more locally and thus more efficiently. We aim to minimize latency and delay in edge architectures. We focus on an advanced architectural setting that takes communication and processing delays into account in addition to an actual request execution time in a performance engineering scenario. Our architecture is based on multi-cluster edge layer with local independent edge node clusters. We argue that particle swarm optimisation as a bio-inspired optimisation approach is an ideal candidate for distributed load processing in semi-autonomous edge clusters for IoT management. By designing a controller and using a particle swarm optimization algorithm, we can demonstrate that processing and propagation delay and the end-to-end latency (i.e., total response time) can be optimized.