Multiobjective dynamic resource allocation in cloud computing using Harris Hawk Optimization Algorithm (MDLB-HHO)

To increase cloud computing utilization and performance, efficient load balancing and resource distribution techniques are essential. Dynamic load balancing and resource allocation in cloud systems is necessary due to a number of reasons, but this is not an easy and straightforward task. The primary goal of dynamic load balancing of cloud systems is to optimize the workload and resource utilization. The Harris Hawks Optimization (HHO) algorithm is a dynamic method of allocating the workloads to the virtual machines (VMs) according to the workload distribution and the use of the resources. The comparison of experimental analysis and other load-balancing methods shows that the HHO algorithm can be used to control dynamic load balancing in a rather efficient and effective way. With such technical developments, there has been a decrease in time taken to respond as well as the use of resources. The suggested solution is a cost-effective and efficient solution to the load-balancing problem in dynamic conditions and is based on the collaborative hawks hunting behavior. The system converts the resource allocation scheme to the changeable cloud application requirements. This is achieved by a multiobjective fitness function which aims to maximize the efficiency of resources, minimize the response time and resource usage. The primary objective of the study is to ensure that the clouds services become effective and sustainable. The Harris Hawks discover the most optimal distribution techniques of activities by closely observing the space of solutions. They then apply positional updates and iterative interactions to adapt to changing workloads. The system dynamically assigns jobs to virtual machines (VMs) without compromising load balance and efficient resource use through the use of the cooperative search behavior of the hawks. The proposed solution effectively manages the cases when the task requirements are constantly changing. Applying a multiobjective fitness function greatly improves key performance metrics like overall performance, resource usage, and reaction time. This study demonstrates how the HHO algorithm increases the effectiveness and robustness of cloud-based services in dynamic operational environments.