Genetic Algorithm Based Deterministic Workflow Scheduling with Load Balancing

A crucial problem in cloud computing is load balancing, which makes it challenging to guarantee that services operate as intended in accordance with quality of service (QoS), performance reviews, and service contracts that cloud service providers (CSPs) demand of businesses. The main goal is to distribute workloads among computer resources in order to improve performance. Load balancing is categorized as an "NP-hard" problem due to the ample solution space and the time needed to identify the optimal solution. While only a few approaches may provide an exact answer in polynomial time, prior research shows that metaheuristic-based tactics can find correct solutions in a fair amount of time. We use a framework that integrates Neural Network Task Classification (N2TC) and Genetic Algorithm Task Assignment (GATA) for load balancing. By taking load balancing and task completion time into account, this approach seeks to maximize load distribution and reduce process execution time. Extensive simulations of application operations were carried out to evaluate the effectiveness of the suggested load-balancing strategy. Results have shown that the proposed framework outperforms tournament selection genetic algorithm (TSGA), Heterogeneous Earliest Finish Time (HEFT), and Performance Effective Task Scheduling (PETS) heuristics in terms of load balancing up to 4.52% and makespan by 6.47%.