AN EFFICIENT SCHEDULING ALGORITHM FOR LOAD BALANCING IN CLOUD COMPUTING ENVIRONMENTS
DOI:
https://doi.org/10.69980/77ssrf09Keywords:
cloud computing, load balancing, task scheduling, resource management, performance optimizationAbstract
The problem of load balancing has become one of the primary aspects of cloud computing because of the asymmetry of the allocation of duties, which may lead to an inefficient use of resources and breakdown of performance. Despite the various scheduling techniques that have been expounded, there is a desire to continue and have simpler and adaptive techniques, which can be able to operate effectively with huge and dynamic workloads. This study evaluates a load-aware scheduling approach designed to improve workload distribution across virtual machines in a cloud environment. A large-scale task scheduling dataset was preprocessed to construct job-level workloads, resulting in 80,000 jobs for analysis. Jobs were ordered by submission time and assigned to virtual machines in a simulation framework. The proposed method dynamically assigns each incoming job to the virtual machine with the minimum current load. Its performance was compared with First-Come-First-Serve (FCFS) and Round Robin scheduling using load variance, standard deviation, and fairness index. The results show that the load-aware approach significantly reduces workload imbalance, achieving approximately 99.9% reduction in load variance and over 96% reduction in standard deviation compared to both baseline methods. It also attains the highest fairness index, indicating a more uniform distribution across virtual machines. These findings suggest that simple load-aware scheduling can effectively improve resource distribution without introducing additional computational complexity, making it a practical solution for large-scale cloud environments.
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