A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

Applied Sciences

Abdullah Alourani¹  Muhammad Sardaraz²  Muhammad Tahir²  Sania Malik² 

[1] Department of Computer Science and Information, College of Science in Zulfi, Majmaah University, Al-Majmaah 11952, Saudi Arabia;Department of Computer Science, Attock Campus, COMSATS University Islamabad, Attock 43600, Pakistan;

关键词: cloud computing;    resource utilization;    forecasting;    neural networks;    GA;    PSO;   

DOI  :  10.3390/app12042160

来源: DOAJ

【 摘 要 】

Cloud computing has revolutionized the modes of computing. With huge success and diverse benefits, the paradigm faces several challenges as well. Power consumption, dynamic resource scaling, and over- and under-provisioning issues are challenges for the cloud computing paradigm. The research has been carried out in cloud computing for resource utilization prediction to overcome over- and under-provisioning issues. Over-provisioning of resources consumes more energy and leads to high costs. However, under-provisioning induces Service Level Agreement (SLA) violation and Quality of Service (QoS) degradation. Most of the existing mechanisms focus on single resource utilization prediction, such as memory, CPU, storage, network, or servers allocated to cloud applications but overlook the correlation among resources. This research focuses on multi-resource utilization prediction using Functional Link Neural Network (FLNN) with hybrid Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The proposed technique is evaluated on Google cluster traces data. Experimental results show that the proposed model yields better accuracy as compared to traditional techniques.

【 授权许可】

Unknown