Load Balancing in Cloud Computing Using Genetic Algorithm and Fuzzy Logic

Cloud computing systems play a vital role in the digital age. A critical bottleneck in most scenarios in cloud computing is the high degree of unpredictability with respect to resource availability and network bandwidth, which may lead to low Quality of Service (like low response times), which can be improved by Load Balancing. Load balancing concerns with efficiently distributing incoming network traffic across a group of servers. This ensures no single server bears too much demand, and thus the availability of applications and websites for users is increased. Due to the huge state-space of such a problem, implementing task scheduling algorithms in load balancing can be very effective. In this paper, we propose a hybrid intelligent approach to load balancing: a Genetic Algorithm module arranges the jobs randomly, and a fuzzy logic module builds the objective function for determining busy states of servers according to their RAM and CPU task queues. The fuzzy input variables include the satisfaction degree and the start and end times of the service, and the fuzzy output is service availability. Computational experiments showed that the best solution was obtained within half of the planned execution time, which leads to higher user satisfaction degree.