Soft Computing Techniques for Energy Consumption and Resource Aware Allocation on Cloud: A Progress and Systematic Review

Abstract

Cloud computing plays a vital role in storage and transfer of immense capacity data due to a rapid growth in size and the quantity of organizational tasks. There are many studies in which varied soft computing methods are applied to the cloud environment. In large data centers the cloud services indorse not only the energy consumption price of the substructure resources but also with a considerable growth in environmental costs. These subjects are significant requisites to decrease the energy cost and carbon footprint of cloud computing systems. To minimize energy consumption, the intelligent machines are required to achieve crossways numerous diverse machines, and strategies corresponding across the hardware and software layers to balance performance and energy, as well as to proficiently exploit multiple resources. Energy-efficient Cloud Organization Resource Allocation Framework is getting acceptance as it is paying operative consideration to cloud data management with an interpretation to achieve maximum revenue and minimum cost. The primary objective of the chapter is to conduct the systematic study and mapping of recent soft computing techniques to resolve the resource allocation and energy consumption problems in cloud computing. The chapter discuss the various soft computing techniques which are used in cloud environment for energy-resource allocation, workflow scheduling and performing the migration on cloud computing system. The first section of the chapter comprises of Introduction, motivation, background works which includes Framework for Energy and resource aware allocation using soft computing techniques, various issues, benefits of the work and application areas of soft computing techniques for cloud. The next section of the chapter highlighted the reported work which covers the detailed study of the researchers for energy efficiency and resource allocation using soft commuting techniques. The final section of the chapter discuss the comparative analysis which compares the work of different researchers by using various performance parameters such as execution time, power consumption, energy efficiency, resource utilization, response time and makespan.