Temporal-Variation-Aware Profit-Maximized and Delay-Bounded Task Scheduling in Green Data Center
An increasing number of enterprises deploy their business applications in green data centers (GDCs) to address irregular and drastic natures in task arrival of global users. GDCs aim to schedule tasks in the most cost-effective way, and achieve the profit maximization by increasing green energy usage and reducing brown one. However, prices of power grid, revenue, solar and wind energy vary dynamically within tasks’ delay constraints, and this brings a high challenge to maximize the profit of GDCs such that their delay constraints are strictly met. Different from existing studies, a Temporal-variation-aware Profit-maximized Task Scheduling (TPTS) algorithm is proposed to consider dynamic differences, and intelligently schedule all tasks to GDCs within their delay constraints. In each interval, TPTS solves a constrained profit maximization problem by a novel Simulated-annealing-based Chaotic Particle swarm optimization (SCP). Compared to several state-of-the-art scheduling algorithms, TPTS significantly increases throughput and profit while strictly meeting tasks’ delay constraints.
KeywordsGreen computing Hybrid clouds Profit maximization Simulated annealing Particle swarm optimization Chaotic search
This work was supported in part by the National Natural Science Foundation of China (NSFC) under Grants 61802015 and 61703011, in part by the Major Science and Technology Program for Water Pollution Control and Treatment of China under Grant 2018ZX07111005, and in part by the National Defense Pre-Research Foundation of China under Grants 41401020401 and 41401050102.
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