A statistical based resource allocation scheme in cloud

Recently, cloud computing has emerged as a new computing paradigm on the Internet. With the development of cloud computing, enterprise data centers shift towards a utility computing model where many critical business applications share a common pool of infrastructure resources offering capacity on demand. The virtual machine with the features of strong isolation and flexible is usually assigned as the basic unit. However, as the demand of each type of VM can fluctuate independently at run time, it becomes a challenging problem to allocate data center resources to each VM to balance the workload in the cloud. In this paper, we introduce an approach (Statistic based Load Balance, SLB) that makes use of the statistical prediction and available resource evaluation mechanism to make online resource allocation decisions. Unlike the methods that balance load based on SLA (Service Level Agreement) of VMs, SLB achieves load balancing by predicting the VM's resource demand. The approach includes two parts:(1) A data analysis of on-line historical performance for forecasting the resource demand of each VM, and (2) An algorithm for choosing a proper host in the resource pool to run the VM. Experiments show that SLB can perform load balance in time, and also perform more balanced use of different resources.