Abstract Cloud Computing, a new concept is a pool of virtualized computer resources. An Internet-based development where dynamically scalable and often virtualized resources are provided as a service over the Internet has become a significant issue. Cloud computing describes both a platform and type of application. A cloud computing platform dynamically provisions, configures, reconfigures, and deprovisions servers as needed. Servers in the cloud can be physical machines or virtual machines spanned across the network. Thus it utilizes the computing resources (service nodes) on the network to facilitate the execution of complicated tasks that require large-scale computation. Selecting nodes (load balancing) for executing a task in the cloud computing must be considered, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. In this paper, a soft computing based load balancing approach has been proposed. A local optimization approach Stochastic Hill climbing is used for allocation of incoming jobs to the servers or virtual machines(VMs). Performance of the algorithm is analyzed both qualitatively and quantitatively using CloudAnalyst. CloudAnalyst is a CloudSim-based Visual Modeller for analyzing cloud computing environments and applications. A comparison is also made with Round Robin and First Come First Serve (FCFS) algorithms.