Cloud resource mapping through crow search inspired metaheuristic load balancing technique. (July 2021) - Singh, Harvinder; Tyagi, Sanjay; Kumar, Pardeep
Abstract: In the modern era of digitalization, many applications exist that consume a huge computational processing power. These applications due to massive costs involved in procurement and maintenance, looks at resource abundant cloud environments as effective solution. However, cloud environments often suffer from challenges that threaten the performance due to inefficient resource utilization. Load balancing across multiple virtual machines in cloud deployment is one of the major issue that leads to under-utilization of cloud resources. In this research work, the problem of task to resource mapping is addressed using crow search based load balancing algorithm for greater optimization as solution. The solution uses parameters such as average power consumption of data center, average cost of the data center and data center loading for allocating the best resource to the submitted tasks. The proposed solution is validated against standard ant colony optimization based load balancing algorithm. It has been found that the proposed algorithm has outperformed standard algorithm and emerges out as a most optimal load balancing algorithm. Graphical abstract: Highlights: CSLBA is suggested as a solution to load balancing problem in a cloud environment. Performance metrics for dispersing the load among resources have been identified. The proposed algorithm is validated in terms of load mapping ability. CSLBA proved to be effective as it evenly distributed load among all data centers.