Optimizing Heterogeneous Task Allocation for Edge Compute Micro Clusters Using PSO Metaheuristic

Optimised task allocation is essential for efficient and effective edge computing; however, task allocation differs in edge systems compared to the powerful centralised cloud data centres, given the limited resource capacities in edge and the strict QoS requirements of many innovative Internet of Things (IoT) applications. This paper aims to optimise heterogeneous task allocation specifically for edge micro-cluster platforms. We extend our previous work on optimising task allocation for micro-clusters by presenting a linear-based model and propose a metaheuristic Particle Swarm Optimisation (PSO) technique to minimise the makespan time and the allocation overhead time of heterogeneous workloads in batch execution. We present a comparative performance evaluation of metaheuristic PSO, mixed-integer programming (MIP) and randomised allocation based on the computation overhead time and the quality of the solutions. Our results show a crossover implying that mixed-integer programming is efficient for small-scale clusters, whereas PSO scales better and provides near-optimal solutions for larger-scale micro-clusters.