Due to the revolution of Internet of Things (IoT), the amount of data generation has been redoubling, leading to higher latency and network traffic. This has resulted in delays in services and increased energy consumption of cloud servers. Fog computing tackles the issues associated with long geographical distance between end-users and cloud servers by extending service provision closer to the network edge, reducing latency and makespan, and optimizing energy consumption during workload execution. Instead of offloading all tasks to the cloud, delay-sensitive tasks are executed at fog nodes, while others are offloaded to the cloud. However, the resources at the fog layer are limited, posing a challenge for task scheduling in fog computing, particularly as a multi-objective optimization problem. Meta-heuristic algorithms have been potent to find an optimal solution for such problems within a reasonable amount of time. The Whale Optimization Algorithm (WOA) is a relatively new meta-heuristic algorithm that has received significant attention from researchers due to its impressive optimization characteristics. However, being an exploitationoriented technique, it falls into local optima due to a lack of generating new solutions over time. Limited exploration capabilities also compromise the diversity of the solution space and prolong convergence time. Therefore, in this study, an enhanced Ripple-induced Whale Optimization Algorithm (RWOA) is proposed, utilizing ripple effects to schedule independent tasks in fog computing. It aims to minimize makespan and energy consumption while maximizing throughput in a fog-cloud infrastructure by improving poor solutions through substantial changes. Extensive simulations are performed to assess the effectiveness of the proposed algorithm. The proposed RWOA outperformed TCaS, HFSGA, MGWO, and WOAmM on two workload datasets: Random and NASA Ames iPSC. The statistical significance of the results is validated by the Friedman test and Wilcoxon Signed-rank test.
