Energy-Efficient Multi-Job Scheduling Model for Cloud Computing and Its Genetic Algorithm. (8th February 2012)

• Record Type: Journal Article
• Authors: Wang, Xiaoli; Wang, Yuping; Zhu, Hai
• Other Names: Eriksson Anders Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2012(2012)
• Publication Date: 2012-02-08
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2012/589243
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : For the problem that the energy efficiency of the cloud computing data center is low, from the point of view of the energy efficiency of the servers, we propose a new energy-efficient multi-job scheduling model based on Google's massive data processing framework. To solve this model, we design a practical encoding and decoding method for the individuals and construct an overall energy efficiency function of the servers as the fitness value of each individual. Meanwhile, in order to accelerate the convergent speed of our algorithm and enhance its searching ability, a local search operator is introduced. Finally, the experiments show that the proposed algorithm is effective and efficient.