Name
Title | ||
|---|---|---|
Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers |
Abstract | ||
|---|---|---|
The significant growth in the number and types of tasks of heterogeneous applications in green cloud data centers (GCDCs) dramatically increases their providers' revenue from users as well as energy consumption. It is a big challenge to maximize such revenue, while minimizing energy cost in a market where prices of electricity, availability of renewable power generation, and behind-the-meter renewable generation contract models differ among the geographical sites of the GCDCs. A multiobjective optimization method that investigates such spatial differences in the GCDCs is for the first time proposed to trade off such two objectives by cost-effectively executing all tasks while meeting their delay constraints. In each time slot, a constrained biobjective optimization problem is formulated and solved by an improved multiobjective evolutionary algorithm based on decomposition. Realistic data-based simulations prove that the proposed method achieves a larger total profit in faster convergence speed than the two state-of-the-art algorithms. Note to Practitioners-This article considers the tradeoff between profit maximization and energy cost minimization for the green cloud data center (GCDC) providers while meeting the delay constraints of all tasks. Current task-scheduling methods fail to take the advantage of spatial variations in many factors, e.g., prices of electricity and availability of renewable power generation at geographically distributed GCDC locations. As a result, they fail to execute all tasks of heterogeneous applications within their delay bounds in a high-revenue and low-energy-cost manner. In this article, a multiobjective optimization method that addresses the disadvantages of the existing methods is proposed. It is realized by a proposed intelligent optimization algorithm. Simulations demonstrate that in comparison with the two state-of-the-art scheduling algorithms, the proposed one increases the profit and reduces the convergence time. It can be readily implemented and integrated into actual industrial GCDCs. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TASE.2020.2971512 | IEEE Transactions on Automation Science and Engineering |
Keywords | DocType | Volume |
Data centers,energy optimization,green clouds,multiobjective evolutionary algorithm,task scheduling | Journal | 18 |
Issue | ISSN | Citations |
2 | 1545-5955 | 1 |
PageRank | References | Authors |
0.35 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haitao Yuan | 1 | 314 | 31.27 |
| Heng Liu | 2 | 153 | 27.10 |
| Jing Bi | 3 | 244 | 25.49 |
| MengChu Zhou | 4 | 8989 | 534.94 |