Handling Cloud Computing Issues Services by Swarm Intelligence

Abstract

As the cloud computing has become the present and the future of the internet. The huge amount of research is going on. There have been large numbers of issues raised while designing the cloud system. One of such problem is balancing load and scaling the cloud network. Load balancing Problem not only incurs is huge cost to the service provider but also has an environmental impact. The importance of communication capacity in blended (hybrid) cloud specifically at the time of communication paths and these routes are generally positioned at the cloud backbone, delay in such path boost the difficulties in the load balancing algorithms since the available bandwidth of these paths can swing drastically. To deliver Quality of Service and viable cost to hybrid infrastructure the following parameter plays vital role like communication routes, scalable path awareness, as well as load balancing efficient algorithm. In addition, existing competent algorithms build general cloud infrastructures but these algorithms are not as good for blended clouds as we need much more competent algorithm. The exceptional feature of the improved ACO scheduler was its parallel nature. In this work we provide an Improved ACO (I_ACO) in hybrid cloud provides a competent solution for load balancing, scaling and path optimization in cloud computing environment utilizing efficient optimization based strategy which is Ant Colony Optimization, we too assess the working of I_ACO based process prevailing the state-of art with genetic algorithm for evaluation. newline