An Adaptive Genetic Algorithm-Based Load Balancing-Aware Task Scheduling Technique for Cloud Computing

Task scheduling in highly elastic and dynamic processing environments such as cloud computing have become the most discussed problem among researchers. algorithms are responsible for allocation of tasks resources their execution, an inefficient task algorithm results under-or over-utilization resources, which turn leads to degradation services. Therefore, proposed work, load balancing is considered important criterion a environment it can help reducing overhead critical decision-oriented process. In this paper, we propose adaptive genetic algorithm-based (GALB)-aware technique that not only better utilization but also helps optimizing values key performance indicators makespan, improvement ratio, degree imbalance. The concept crossover mutation used work adaptation fittest individual current generation prevents them from elimination. CloudSim simulator has been carry out simulations obtained establish GALB performs all outperforms its peers taken into consideration.