Load Balancing in Cloud Computing Using Stochastic Hill Climbing-A Approach
Utilizes the computing resources on the network to facilitate the execution of complicated tasks that requires large-scale computation. Se-lects nodes (load balancing) is crucial for executing a task in the cloud computing, and to exploit the effectiveness of the resources, they have to be properly selected according to the properties of the task. Proposes a soft computing based load balancing approach, uses a lo-cal optimization approach Stochastic Hill climbing for allocation of incoming jobs to the servers or virtual machines (VMs). Analyzes per-formance of the algorithm both qualitatively and quantitatively using CloudAnalyst. Makes a comparison with Round Robin and First Come First Serve (FCFS) algorithms. The comparison reflect the advantage of local optimization approach Stochastic Hill climbing in se-lecting load balance node.
Cloud ComputingLoding BalanceSoft ComputingStochastic Hill ClimbingCloudAnalyst
NETL
NSTL
万方数据
