QoS aware grey wolf optimization for task allocation in cloud infrastructure

Abstract

Cloud computing is a reliable computing platform for large computationally intensive or data-intensive tasks. This has been accepted by many industrial giants of the software industry for their software solutions; companies like Microsoft, Accenture, Ericson, etc. have adopted cloud computing as their first choice for cheap and reliable computing. By increasing the number of clients adopting this, there is a requirement of much more cost-efficient and high-performance computing for more trust and reliability among the client, and the service providers to guarantee cheap and more efficient solutions. So the tasks in cloud need to be allocated in an efficient manner to provide high resource utilization and least execution time for high performance, and at the same time provide least computational execution cost. We have proposed a learning-based grey wolf optimization algorithm for task allocation to reduce the request time and scheduling time to improve QoS (Quality of Service). Proposed algorithm has been inspired from behaviour of wolfs hunting in real world with a unique technique which they have evolved from long evolution and learning cycle