Abstract:
Energy costs dominate cloud computing today. Energy-aware task scheduling is crucial. Heterogeneous clusters can save energy by scheduling tasks based on deadlines, data locality, and resource utilization. Task list construction, scheduling, and slot list updating comprise the framework.
A new job sequence is suggested to create a reasonable task list based on deadlines, job slots, and processing times. Data locality is greatly improved by task scheduling from rack-local, cluster-local, and remote servers to promising slots.
After task and slot assignment, a cluster slot update using fuzzy logic to find available slots and improve server resource utilization is suggested. Experimental results show that the proposed heuristic consumes less energy than adapted algorithms with variable slot counts.
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