Multiobjective Dynamic Resource Allocation in Cloud Computing using Harris Hawk Optimization Algorithm (MDLB-HHO)

Effective load balancing and resource distribution strategies are essential for optimizing performance and resource usage in cloud computing. Cloud computing necessitates flexible, dynamic load balancing and resource allocation among multiple goals. Load balancing and resource allocation in cloud computing are complex tasks. The primary goal of dynamic load balancing in cloud computing systems is to enhance resource usage and improve the efficiency of task allocation. The HHO algorithm is accountable for the dynamic allocation of tasks to virtual machines (VMs) according to the distribution of workload and usage of resources. Multiple experimental evaluations and comparisons with alternative load-balancing approaches have proved that the HHO algorithm successfully and efficiently manages dynamic load-balancing. These technological advancements have led to improved reaction times and enhanced resource efficiency. This approach offers a viable and efficient alternative for tackling load-balancing challenges in dynamic scenarios using the cooperative foraging behavior observed in hawks. The proposed approach accounts for the ever-changing demands of cloud applications and dynamically modifies the resource allocation technique. To accomplish this, a multiobjective fitness function is used to cut down on response time and overuse of resources while simultaneously improving resource efficiency. These findings suggest it may help make cloud-based services more efficient and sustainable. The Harris Hawks perform an extensive review of the solution space, wherein they identify the optimal approach for task allocation and adapt to the dynamic workload conditions by employing a process characterized by iterative interactions and positional updates. This approach uses hawks’ collaborative search behavior to dynamically assign tasks to VMs while accounting for load balancing and resource utilization. The proposed methodology can adapt to ever-changing workload demand situations. It employs a multiobjective fitness function to effectively improve key performance indicators such as response time, resource utilization, and efficiency. This work demonstrates how the HHO algorithm could improve the effectiveness and longevity of cloud-based services under changing conditions.