Energy-Efficient Load Balancing Algorithm for Workflow Scheduling in Cloud Data Centers Using Queuing and Thresholds

Applied Sciences

卷:11

Babar Shah¹  Muhammad Sardaraz²  Nimra Malik²  Muhammad Tahir²  Gohar Ali³  Fernando Moreira⁴ 

[1] College of Technological Innovation, Zayed University, Abu Dhabi 144534, United Arab Emirates;

[2] Department of Computer Science, Attock Campus, COMSATS University Islamabad, Attock 43600, Pakistan;

[3] Department of Information Systems and Technology, Sur University College, Sur 411, Oman;

[4] REMIT, Universidade Portucalense, 4200-072 Porto, Portugal;

关键词: cloud computing;    energy consumption;    task scheduling;    load balancing;    makespan;    PSO;   

DOI  :  10.3390/app11135849

来源: DOAJ

【 摘 要 】

Cloud computing is a rapidly growing technology that has been implemented in various fields in recent years, such as business, research, industry, and computing. Cloud computing provides different services over the internet, thus eliminating the need for personalized hardware and other resources. Cloud computing environments face some challenges in terms of resource utilization, energy efficiency, heterogeneous resources, etc. Tasks scheduling and virtual machines (VMs) are used as consolidation techniques in order to tackle these issues. Tasks scheduling has been extensively studied in the literature. The problem has been studied with different parameters and objectives. In this article, we address the problem of energy consumption and efficient resource utilization in virtualized cloud data centers. The proposed algorithm is based on task classification and thresholds for efficient scheduling and better resource utilization. In the first phase, workflow tasks are pre-processed to avoid bottlenecks by placing tasks with more dependencies and long execution times in separate queues. In the next step, tasks are classified based on the intensities of the required resources. Finally, Particle Swarm Optimization (PSO) is used to select the best schedules. Experiments were performed to validate the proposed technique. Comparative results obtained on benchmark datasets are presented. The results show the effectiveness of the proposed algorithm over that of the other algorithms to which it was compared in terms of energy consumption, makespan, and load balancing.

【 授权许可】

Unknown