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Efficient offloading and scientific task scheduling are crucial for managing computational tasks in research environments. This involves determining the optimal location for executing a workflow task and allocating the task to computing resources to optimize performance. The challenge is to minimize completion time, energy consumption, and cost. This study proposes three methods: latency-centric offloading (LCO) for delay-sensitive applications; energy-based offloading (EBO) for energy-saving; and efficient offloading (EO) for balanced task distribution across tiers. Scheduling in this paper uses a genetic algorithm (GA) with a weighted sum objective function considering makespan, cost, and energy for IoT-fog-cloud. Comparative studies involving Montage, Cybershake, and epigenomics workflows indicate that LCO excels in terms of makespan and cost but ranks the lowest in energy. EBO excels in energy efficiency, aligning closely with the base method. EO competes effectively with the base method in terms of makespan and cost but consumes more energy. This research enables the selection of the most suitable method based on the type of application and its prioritization of makespan, energy, or cost.
Gamal et al. (Tue,) studied this question.