BLOA: Optimizing Resource Utilization using Multi-Objective Task Scheduling in Cloud Environment

The Butterfly Lion Optimization Algorithm (BLOA) is proposed as an innovative hybrid metaheuristic approach for Task Scheduling (TS) in Cloud Computing (CC) environments. Proposed algorithm is combination of mating and foraging behaviour of Butterfly Optimization Algorithm (BOA) and hunting strategy of Lion Optimization Algorithm (LOA). TS is a critical challenge in CC, where the goal is to efficiently allocate resources while optimizing multiple objectives such as minimizing makespan, reducing execution time and response time while maximizing the throughput. The BLOA combines the dynamic exploration capabilities of butterfly behavior with the strategic hunting techniques of lions, creating a robust algorithm that balances global exploration and local exploitation. This hybrid algorithm leverages the strengths of both butterflies and lions to navigate complex, high-dimensional search spaces typical of CC environments. By integrating adaptive mechanisms, the BLOA is capable of dynamically adjusting to varying workload demands and resource availability. Comparative evaluations against other leading scheduling algorithms demonstrate that BLOA achieves superior performance in terms of reducing makespan time by 18.15%, execution time by 11.23%, and response time by 14.78%, while maximizing the throughput by 21.11%. The algorithm is especially useful for CC environments that are heterogeneous and distributed, which makes it ideal for cloud service providers who intend to improve resource utilization as well as decrease energy consumption and operational costs.