Allocation of Virtual Machines in Cloud Data Centers - A Survey of Problem Models and Optimization Algorithms

Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and also environmental impact. Therefore, cloud providers must optimize the usage of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This paper surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, also pointing out the areas that need further research in the future

Migration Control in Cloud Computing to Reduce the SLA Violation

The requisition of cloud based services are more eminent because of the enormous benefits of cloud such as pay-as-you-use flexibility,scalability and low upfront cost. Day-by-day due to growing number of cloud consumers the load on the datacenters is also increasing. Various load distribution and dynamic load balancing approaches are being followed in the datacenters to optimize the resource utilization so that the performance may be maintained during the increased load. Virtual machine (VM) migration is primarily used to implement dynamic load balancing in the datacenters. But, the poorly designed dynamic VM migration policies may negate its benefits. The VM migration overheads result in the violations of service level agreement (SLA) in the cloud environment.In this paper,an extended VM migration control model is proposedto minimize the SLA violations while controlling the energy consumption of the datacenter during VM migration. The parameters of execution boundary threshold is used to extend an existing VM migration control model. The proposed model is tested through extensive simulations using CloudSim toolkit by executing real world workload. Results are obtained in terms of number of SLA violations while controlling the energy consumption in the datacenter. Results show that the proposed modelachieves better performance in comparison to the existing model

An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers

Today's Cloud applications are dominated by composite applications comprising multiple computing and data components with strong communication correlations among them. Although Cloud providers are deploying large number of computing and storage devices to address the ever increasing demand for computing and storage resources, network resource demands are emerging as one of the key areas of performance bottleneck. This paper addresses network-aware placement of virtual components (computing and data) of multi-tier applications in data centers and formally defines the placement as an optimization problem. The simultaneous placement of Virtual Machines and data blocks aims at reducing the network overhead of the data center network infrastructure. A greedy heuristic is proposed for the on-demand application components placement that localizes network traffic in the data center interconnect. Such optimization helps reducing communication overhead in upper layer network switches that will eventually reduce the overall traffic volume across the data center. This, in turn, will help reducing packet transmission delay, increasing network performance, and minimizing the energy consumption of network components. Experimental results demonstrate performance superiority of the proposed algorithm over other approaches where it outperforms the state-of-the-art network-aware application placement algorithm across all performance metrics by reducing the average network cost up to 67% and network usage at core switches up to 84%, as well as increasing the average number of application deployments up to 18%.Comment: Submitted for publication consideration for the Journal of Network and Computer Applications (JNCA). Total page: 28. Number of figures: 15 figure