Abstract
Cloud computing provides unprecedented advantages of using computing resources with very less efforts and cost. The energy utilization in cloud data centers has forced the cloud service providers to raise the expense of using its services and has increased the carbon footprints in the environment. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users’ requests. Thus, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. The simulations are performed using Java. The results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%.
Highlights
Cloud computing is the trading of resources over the Internet with reliability and security of data [1, 2]. e ease of accessing computing resources has attracted big organizations to avail the services from cloud computing on pay-peruse model
Results and Discussion e bin-packing approach revolves around the implementation of 4 prime techniques, namely, LPBP, best-fit decreasing (BFD), PCABFD, and EU-BFD [12]. e techniques are static in nature as indicated above and have been converted into dynamic one for comparison with the proposed approaches DEEBFD and EM switcher
Servers are sorted based on power capacity Virtual machines are sorted in decreasing order of CPU capacity While true If VMs are waiting in job after S seconds Enter CPU capacity and execution time frame for M virtual machines Allot VM to the server with required computing capacity and create a data structure Compute total energy consumed by current configuration End If End While Evaluate the total resource utilization and makespan of servers
Summary
Us, in this paper, two new dynamic best-fit decreasing-based bin-packing algorithms are proposed wherein the first technique is for service providers and focuses on increasing server utilization and the second approach acts as a switcher to harness best results among all algorithms. Many static bin-packing algorithms exist which can reduce energy by some percentage, but with new era of digitization, advanced and dynamic techniques are required which can serve heterogeneous users and random users’ requests. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. Both techniques deliberately achieve high performance in terms of total energy consumption, resource utilization, and makespan along with serving continuous and varying requests from customers. e simulations are performed using Java. e results exhibited that DEE-BFD can escalate resource utilization by 96% and EM switcher can reduce total energy consumption by 49% and reduce makespan by 56%
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