Dynamic OBL-driven whale optimization algorithm for independent tasks offloading in fog computing

Abstract

Cloud computing has been the core infrastructure for providing services to the offloaded workloads from IoT devices. However, for time-sensitive tasks, reducing end-to-end delay is a major concern. With advancements in the IoT industry, the computation requirements of incoming tasks at the cloud are escalating, resulting in compromised quality of service. Fog computing emerged to alleviate such issues. However, the resources at the fog layer are limited and require efficient usage. The Whale Optimization Algorithm is a promising meta-heuristic algorithm extensively used to solve various optimization problems. However, being an exploitation-driven technique, its exploration potential is limited, resulting in reduced solution diversity, local optima, and poor convergence. To address these issues, this study proposes a dynamic opposition learning approach to enhance the Whale Optimization Algorithm to offload independent tasks. Opposition-Based Learning (OBL) has been extensively used to improve the exploration capability of the Whale Optimization Algorithm. However, it is computationally expensive and requires efficient utilization of appropriate OBL strategies to fully realize its advantages. Therefore, our proposed algorithm employs three OBL strategies at different stages to minimize end-to-end delay and improve load balancing during task offloading. First, basic OBL and quasi-OBL are employed during population initialization. Then, the proposed dynamic partial-opposition method enhances search space exploration using an information-based triggering mechanism that tracks the status of each agent. The results illustrate significant performance improvements by the proposed algorithm compared to SACO, PSOGA, IPSO, and oppoCWOA using the NASA Ames iPSC and HPC2N workload datasets.