Fine-grained resource provisioning and task scheduling for heterogeneous applications in distributed green clouds

• Published in: IEEE/CAA journal of automatica sinica Vol. 7; no. 5; pp. 1380 - 1393
• Main Authors: Yuan, Haitao, Zhou, MengChu, Liu, Qing, Abusorrah, Abdullah
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE); Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102 USA%Department of Electrical and Computer Engineering, Faculty of Engineering, and the Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21589, Saudi Arabia
• Subjects: Clean energy; Cloud computing; Computational modeling; Computer simulation; Data centers; Electric power grids; Energy conservation; Energy consumption; Energy costs; Optimization; Power consumption; Processor scheduling; Provisioning; Queues; Queuing theory; Resource allocation; Resource scheduling; Response time; Search algorithms; Servers; Simulated annealing; System effectiveness; Task analysis; Task scheduling; Bees algorithm; intelligent optimization; energy optimization; simulated annealing; data centers; distributed green cloud (DGC); task scheduling; machine learning
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2020.1003177

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud &#x0028 DGC &#x0029 systems for low response time and high cost-effectiveness in recent years. Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption. Many factors in DGCs, e.g., prices of power grid, and the amount of green energy express strong spatial variations. The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations. This work adopts a G &#x002F G &#x002F 1 queuing system to analyze the performance of servers in DGCs. Based on it, a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm &#x0028 SBA &#x0029 to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs, and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications. Realistic data-based experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.