Abstract
Cloud computing is increasingly implemented by a growing number of organizations in recent years. Their critical business applications are deployed in distributed cloud data centers (CDCs) for fast response and low cost. The ever-increasing consumption of energy makes it highly important to schedule tasks efficiently in CDCs. In addition, many factors in CDCs, e.g., the wind and solar energy and prices of power grid have spatial differences. It becomes a challenging problem of how to achieve the energy cost minimization for CDCs in such a market. This work applies a G/G/1 queuing system to evaluate the optimization of servers in each CDC. Furthermore, a single-objective constrained optimization problem is given and addressed by a proposed Simulated-annealing-based Bees Algorithm to yield a close-to-optimal solution. Based on it, a Fine-grained Task Scheduling (FTS) algorithm is designed to minimize the energy cost of CDCs by intelligently scheduling heterogeneous tasks among distributed CDCs. In addition, it also determines running speeds of servers and the number of switched-on servers in each CDC while strictly meeting tasks' delay bounds. Realistic data-driven results demonstrate that FTS outperforms its typical benchmark scheduling peers in terms of energy cost and throughput.
Original language | English (US) |
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Title of host publication | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1212-1217 |
Number of pages | 6 |
Volume | 2020-October |
ISBN (Electronic) | 9781728185262 |
DOIs | |
State | Published - Oct 11 2020 |
Event | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 - Toronto, Canada Duration: Oct 11 2020 → Oct 14 2020 |
Conference
Conference | 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020 |
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Country/Territory | Canada |
City | Toronto |
Period | 10/11/20 → 10/14/20 |
All Science Journal Classification (ASJC) codes
- Software
- Control and Systems Engineering
- Human-Computer Interaction
- Computer Science Applications
- Electrical and Electronic Engineering
Keywords
- Cloud data centers
- bees algorithm
- energy management
- intelligent optimization
- simulated annealing