A Novel Method for Efficient Resource Management in Cloud Environment Using Improved Ant Colony Optimization

Cloud has a revolutionary change in Information Technology (IT) for data storage and retrieval operations compared to the traditional system. The drastic change in demand for cloud services has put several challenges for efficient resource allocation to customers. Moreover, competitive cloud service delivery and Service Level Agreement (SLA) violation have required a proficient technique to manage cloud resources. But, traditional resource management policies are unable to provide an appropriate match, hence inappropriate match leads to performance degradation. Swarms are capable of efficiently identify resource requirements through the computation process by using the available number of Virtual Machines (VMs) and allowing their optimal utilization. This research work has opted Ant Colony Optimization (ACO). The new proposed Adaptive Resource Availability Based Multiple Ant Colony Optimization (RABMACO) algorithm has generated an optimal solution for VMs allocation based on availability. The research work addressed in the way for developing a method used to optimize the performance of existing cloud environment by taking parameters for ACO algorithm, which was further experimentally determined. Then, the ACO algorithm has been optimized to the next level by developing resource availability based VM configuring and allocation. The experiment has been implemented with Datacenter, Host and a set of 5–50 VMs for running 100–1000 tasks of Montage dataset under the work flow sim simulation platform. The results have been evaluated on the basis of execution cost, execution time and VMs utilization. It has improved the availability of resources by releasing VMs earlier for performing next set of tasks.