Abstract
In this paper, we present an optimization model for task scheduling for minimizing energy consumption in cloud-computing data centers. The proposed approach was formulated as an integer programming problem to minimize the cloud-computing data center energy consumption by scheduling tasks to a minimum numbers of servers while keeping the task response time constraints. We prove that the average task response time and the number of active servers needed to meet such time constraints are bounded through the use of a greedy task-scheduling scheme. In addition, we propose the most-efficient server- first task-scheduling scheme to minimize energy expenditure as a practical scheduling scheme. We model and simulate the proposed scheduling scheme for a data center with heterogeneous tasks. The simulation results show that the proposed taskscheduling scheme reduces server energy consumption on average over 70 times when compared to the energy consumed under a (not-optimized) random-based task-scheduling scheme. We show that energy savings are achieved by minimizing the allocated number of servers.
| Original language | English (US) |
|---|---|
| Article number | 6679892 |
| Pages (from-to) | 226-231 |
| Number of pages | 6 |
| Journal | Proceedings - International Conference on Distributed Computing Systems |
| DOIs | |
| State | Published - 2013 |
| Event | 33rd IEEE International Conference on Distributed Computing Systems Workshops, ICDCSW 2013 - Philadelphia, PA, United States Duration: Jul 8 2013 → Jul 11 2013 |
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Computer Networks and Communications
Keywords
- Cloud computing
- Energy
- Greedy Algorithm
- Green data centers
- Integer Programming
- Task Scheduling
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