A systematic mapping study on soft computing techniques to cloud environment

Cloud computing plays an essential role in storage and transfer of big capacity data due to a rapid increase in size and the number of organizational activities. There exist numerous studies in which diverse soft computing techniques are applied to the cloud environment. The relevant extant literature that were clustered into five main categories with respect to precedence are; task optimization, power optimization, security, service selection and cost optimization. Yet, it was discovered that there is a dearth of systematic review/mapping studies particularly on soft computing techniques in cloud environment so as to obtain exclusive insight, to identify existing gaps and future research directions. Therefore the aim of this paper is to conduct a systematic mapping study of recent literature on soft computing techniques in cloud environment. For this purpose, 163 articles were chosen as primary sources that were published within the last decade, which were classified based on study focus area, type of research, contribution facet and particularly the type of soft computing technique used. Findings revealed that task optimization takes part as the highly preferred research focus area. Secondly, most of the articles found are of validation studies. The contributions of most of the studies are concerned about methods and finally the top three soft computing techniques were detected as particle swarm optimization (PSO), genetic algorithm (GA) and hybrid systems. The results of this study confirm that applying soft computing techniques in cloud computing has gained more and more significant attention recently but there still remain challenges and gaps which calls for further investigation especially in the area of cost optimization and also artificial bee colony.