Analysis of Energy Efficient Workflow Allocation in Cloud Computing

Growing demand for high performance computing such as cloud since exponential increase data and computing complexity. Cloud provides elastic and on-demand provisioning of high performance computing capabilities using a pay-per-use paradigm, which over the past few years has seen a rapid increase in its usage by scientists and engineers. Many applications consist of many cooperating tasks requiring more processing power than a single computer can provide. Scientific workflows, big data processing workflows and multitier web service workflows are known to be the examples of these applications. Workflow scheduling is an actively researched area in the IaaS cloud environment. Such applications are a big problem that require attention since they required high performance computing and consume excessive amount of energy in cloud data centers. Massive energy use in cloud data centers has an adverse effect on the environment and raises operational costs. As a result, it cannot be ignored. While satisfying the Quality-of-Service constraints set by the user, effective scheduling techniques can considerably reduce energy consumption. Due to the NP-hard nature of the problem, researchers have spent a lot of time studying cloud workflow scheduling. In this study, we offer an overview of existing research for workflow scheduling algorithm in the cloud environments. It provides a perceptive of heuristic and meta heuristic approaches used to solve workflow scheduling. This excerpt is from a full-text study that conducts a thorough analysis of the literature on workflow application in relation to cloud computing. By responding to the three research questions, initial findings are given. 96 research papers were reviewed, and 24 articles were chosen. This study explores that there is a need to find an energy efficient workflow allocation strategy while complying Quality-of-Service parameters set by the user.