EHHO-GWO: A Novel Hybrid Strategy for Workflow Scheduling in Cloud–Fog Environments

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Abstract
Distributed computing environments, such as fog computing, are attracting increasing attention because they can bring data processing and storage closer to data sources and adapt to the ever-increasing demands of existing and emerging applications executed at the network edge. Workflow scheduling poses a significant challenge in these systems, hindering the full exploitation of their potential. However, workflow scheduling is an NP-hard problem. Despite the numerous existing approaches for addressing this problem, several limitations remain, including high execution latency, suboptimal scheduling performance, and inefficient utilization of the available computational resources. To address these challenges, this work proposes a novel hybrid metaheuristic mechanism, called EHHO-GWO, for workflow scheduling, with the objective of optimizing execution time, monetary cost, and energy consumption. Moreover, the proposed approach is comparatively evaluated against classical optimization algorithms, namely Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), as well as other recent metaheuristic approaches. Using FogWorkflowSim as the simulation framework and a set of scientific workflows as input, this study evaluates the effectiveness and efficiency of the proposed algorithm and demonstrates its superior performance in fog computing environments. The experimental results are statistically validated using the Wilcoxon signed-rank test, revealing consistent performance improvements over well-known competing scheduling algorithms.

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