The development of edge computing has facilitated the development of numerous applications with diverse characteristics and stringent quality of service (QoS) requirements; these applications demand significant computational power and have strict time-sensitive constraints. While cloud computing offers seemingly unlimited computational resources, it often fails to meet the real-time demands of certain applications because of the latency introduced by the distance between edge devices and cloud data centers. Edge computing enables computational services closer to edge devices, better fulfilling these time-sensitive demands. Task scheduling that tries to share tasks among diverse virtual machines in an optimum manner concerning overall system performance metrics, such as minimal execution time or reduced energy consumption, is one of the key challenges of this heterogeneous computing environment. Task scheduling is an NP-complete problem. Therefore, metaheuristic algorithms are usually applied to obtain near-optimal solutions. The study presents an enhanced grey wolf optimization hybridized by a dimension learning-based strategy, EGWODLB, for optimizing QoS objectives focusing on execution time and energy consumption. The experimental results reflect that EGWODLB outperforms the benchmark algorithms by achieving significant improvements in both execution time, energy consumption, and VM utilization.
Aminu et al. (Thu,) studied this question.