Cost-Effective and Energy-Aware Resource Allocation in Cloud Data Centers

Abstract

Cloud computing supports the fast expansion of data and computer centers; therefore, energy and load balancing are vital concerns. The growing popularity of cloud computing has raised power usage and network costs. Frequent calls for computational resources may cause system instability; further, load balancing in the host requires migrating virtual machines (VM) from overloaded to underloaded hosts, which affects energy usage. The proposed cost-efficient whale optimization algorithm for virtual machine (CEWOAVM) technique helps to more effectively place migrating virtual machines. CEWOAVM optimizes system resources such as CPU, storage, and memory. This study proposes energy-aware virtual machine migration with the use of the WOA algorithm for dynamic, cost-effective cloud data centers in order to solve this problem. The experimental results showed that the proposed algorithm saved 18.6%, 27.08%, and 36.3% energy when compared with the PSOCM, RAPSO-VMP, and DTH-MF algorithms, respectively. It also showed 12.68%, 18.7%, and 27.9% improvements for the number of virtual machine migrations and 14.4%, 17.8%, and 23.8% reduction in SLA violation, respectively.

Performance Guarantee for Autonomous Robotic Missions using Resource Management: The PANORAMA Approach

“…In computer science it mainly corresponds to the development of algorithms managing the processor(s) load 0123456789(). : V,-vol 123 to execute many tasks regarding some optimization criteria such as computing performance or energy consumption for embedded systems applications or cloud data centers [40][41][42]. In Industrial Engineering it can concern the allocation policy of human resources to business processes to optimize performance objectives (cost, duration, human usage, etc..) [43,44] or the problem of the machine allocation policy in job-shops environment to reduce the processing time [45] or carbon emission [46].…”