Formally modeling and analyzing cost‐aware job scheduling for cloud data center. (30th May 2018)

• Record Type: Journal Article
• Authors: Fan, Guisheng; Chen, Liqiong; Yu, Huiqun; Liu, Dongmei
• Is Part Of: Software, practice & experience. Volume 48:Number 9(2018)
• Page Start: 1536
• Page End: 1559
• Publication Date: 2018-05-30
• Subjects: alternating direction method of multipliers -- cloud data center -- cost -- job scheduling -- Petri nets; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2590
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: With the rapid development of cloud computing, many distributed data centers have been deployed. This means larger energy consumption requirements from the data center. How to reduce the cost of data center has received significant attention recently. Although there are several efforts in studying energy consumption of the data center, very few have considered modeling and analyzing cost‐aware job scheduling for the cloud data center. To address this emerging problem, we propose a systematic approach that considers both basic elements and their relationships in cloud data center. First, we present a formal language to describe the cloud data center, and a job scheduling net is proposed to formally model the basic elements such as user request, Web portal, data center, and server. Second, we minimize the total cost of the cloud data center by considering the multidimensional resource and local electricity price on the basis of the state space of constructed model. The dynamic job scheduling algorithm and its specific execution steps are proposed based on the alternating direction method of multipliers algorithm. Third, the operational semantics and related theories of Petri nets for establishing the correctness of our proposed method are presented. Finally, a series of simulations are performed to illustrate that the proposed method can guarantee the correct behavior of job scheduling in the cloud data center while meeting the required cost.