A state-of-art on cloud load balancing algorithms

The cloud computing is alarmingly getting into mainstream for the booming companies and the research organizations as; they seek to gain benefits from its on-demand access, service models and deployment models. It provides unique features like on-demand access to shared pool of resources over internet in a self-accessible, dynamically scalable and metered manner. It is widely accepted because of its "pay-as-you-go" model. These features make this paradigm a buzzword in the area of high-performance distributed computing (HPDC). Though, this domain is widely accepted still it demands enhancements to bring out the optimized performance. The load balancing among the virtual machines (VMs) belongs to NP-hard problem as far as the equilibrium load distribution is concerned. The hardness of this problem can be defined by considering two factors such as: Large solution space and polynomial bounded computation. One of the major issues in cloud computing which, needs serious attention is load balancing for its efficient performance. In the present work, a deep literature study has been carried out by considering the state of art algorithms for cloud load balancing. The algorithm includes traditional methods, heuristic, meta-heuristic, and hybrid approach. From the analysis and study of the methods presented in the deep literature survey, it has been observed that the existing heuristic algorithms are not generating near to optimal solution within polynomial time. The amalgamation of meta-heuristics, and hybrid-heuristics techniques have been proved to produce suboptimal solutions within reasonable time. This paper provides an extensive historical survey and comparative analysis on various existing load balancing (LB) literature. The presented work will be a help hand tool for researchers to design new efficient load balancing algorithms in the Cloud computing domain.