An Empirical Survey on Load Balancing: A Nature-Inspired Approach

Since the dawn of humanity man tried to mimic several animals and their behavior be it in the age of hunting of while designing the aero plane. Human brain holds a significant amount of power in observing the species around him and trying to incorporate their behavior in several walks of life. This mimicking has helped human to evolve into beings which we are now. Some typical examples include navigation systems, designing several gadgets like aero planes, boats, etc. These days these inspirations are several, and their inspiration is being utilized in several fields like operations, supply-chain management, machine learning and several other fields. The similar kind of approach has been discussed in this paper where we tried to analyze different phenomenon in nature and how different algorithms were designed from these and how these can ultimately be used to solve different issues in cloud balancing. Essential component of cloud computing is load balancer which holds a crucial role of task allocation in virtual machines and several kinds of algorithms were developed on different ways of task allocation procedures each holding its significance here we tried to find the optimal resource allocation in terms of task allocation and rather than approaching through traditional methods we tried to solve this issue by using soft computing techniques. Specifically, nature-inspired algorithms as it hold the key to unlocking massive potential regarding research and problem-solving approach. The central idea of this paper is to connect different optimization techniques to load balancer and how could we make a hybrid algorithm to serve the purpose. We also discussed several different types of algorithms each bearing its roots from different natural procedures. All the algorithms in this paper can be broadly tabulated into three different types SO (Swarm optimization techniques), GO (Genetic-based algorithms), PO (Physics-based algorithms)