Load Balancing in Distributed Cloud Computing: A Reinforcement Learning Algorithms in Heterogeneous Environment

Balancing load in cloud based is an important aspect that plays a vital role order to achieve sharing of between different types resources such as virtual machines lay on servers, storage the form hard drives and servers. Reinforcement learning approaches can be adopted with computing quality service factors minimized cost response time, increased throughput, fault tolerance utilization all available network, thus increasing system performance. Learning result making effective resource by selecting best suitable processor for task execution minimum makespan. Since earlier related work done load, there are limited reinforcement approaches. However this paper, focuses importance RL achieving balanced area distributed computing. A framework proposed implemented tasks heterogeneous environments, particularly, Least Load (LLB) Booster Controller (BRC) Balancing. With help optimal achieved allocation. In workload taken input. results have been evaluated makespan compared existing balancing techniques utilization..