Energy and cost optimization mechanism for workflow scheduling in the cloud

Abstract

Cloud computing has become one of the most important platforms for various applications like artificial intelligence, big data, the Internet of Things, and others, especially demand for cloud computing has exploded in recent years. Because of the increased demand for cloud computing, it has become more complex, which can lead to software or hardware failure. Due to the advanced infrastructure, failure may not be detected and repaired at a given timeline and will end up costing higher. In addition, the advent of cloud computing has another advantage over the massive spread of scientific work. Scientific workflow refers to a series of computations that enable data analysis in a distributed and systematic way; because this work flow has a large number of works, energy consumption is a major problem. Besides these issues, it also suffers from system reliability; in the response to these issues, several researchers have designed their mechanism, however they failed to understand cloud complex environment. Therefore, here we have designed a mechanism that efficiently reduces energy consumption, improve the fault tolerance to achieve reliability, performs operations in very less time, and optimize the cost in the workflow model. We have also demonstrated efficient energy optimization techniques by reducing task loads.