Özet:

Abstract Cloud computing has emerged as a transformative technology that offers vast computational resources to meet the growing demands of modern applications. However, the efficient allocation of these resources to ensure optimal performance and scalability remains a critical challenge. Load balancing techniques play a pivotal role in optimizing resource utilization and improving the overall performance of cloud-based systems. Cloud service providers are looking for creative ways for dispersing the load across the virtual machines. Recent study suggests that efficient task scheduling or task-virtual machine mapping techniques can be used to achieve load balancing. It's also a well-known NP-Hard problem. Hence, contrary to polynomial-time algorithms, the researchers have been searching for meta-heuristic algorithms. In order to provide a solution to the mentioned issue, this research introduced a modified firefly swarm algorithm. The primary goal is to meet all deadlines while reducing the total amount of time it takes to execute all tasks. The proposed technique is compared to particle swarm optimization, bacteria foraging optimization, and dragonfly optimization to demonstrate its efficacy. 

Anahtar Kelimeler: