Improved Artificial Bee Colony Using Monarchy Butterfly Optimization Algorithm for Load Balancing (IABC-MBOA-LB) in Cloud Environments

The advent of cloud computing involving virtualization technologies has offered maximum opportunities for hosting low-cost virtual resources without any infrastructure. The cloud data centers generally consist of heterogeneous commodity servers that are capable of hosting multiple Virtual Machines (VMs) with significantly varying specifications and dynamic resource utilization potentialities. In this context, servers hosting heterogeneous VMs with potentially varying specifications cannot handle unpredictable and variable workloads leading to an imbalance in resource utilization on the server causing Service Level Agreement (SLA) violations and degradation in performance. The cloud data centers are highly unpredictable and dynamic due to the fluctuating resource utilization of VMs, irregular resource utilization patterns of cloud consumers constantly requesting VMs, great deviation in the hosts’ performance in the process of handling different levels of load and unstable arrival and departure rate of data center consumers. These situations are responsible for introducing unbalanced loads in the data center of the cloud that results in SLA violations and performance degradation. Moreover, this imbalanced resource utilization is seen in most of the cases when a VM executes computation-rich applications in spite of its low memory requirements. This problem of resource utilization has proved to be a non-deterministic polynomial time hard problem which can be predominantly solved by hybrid metaheuristic approaches. In this paper, an Improved Artificial Bee Colony using Monarchy Butterfly Optimization Algorithm-based Load Balancing (IABC-MBOA-LB) is proposed for effective resource utilization in clouds. The proposed IABC-MBOA-LB includes global exploration capability of ABC and local exploitation potential of MBOA for effective allocation of user tasks to VMs. It focuses on network and computing resources in order to prevent fragmentation and unnecessary increase in the task finishing times as both should be potentially explored for better resource allocation process. The simulation experiments of the proposed IABC-MBOA-LB scheme confirm its predominance in minimizing load variance and standard deviation of utilization, makespan, standard deviation of connections, average imbalance degree and maximizing throughput independent of the number of tasks and VMs in the cloud.