A Deployment Scheme of Virtual Machines for Campus Cloud Platform

With the continuous deepening of teaching informatization, campus cloud platform is becoming increasingly popular, but the resource utilization in the campus cloud is still low, resulting in a serious waste of resources. To tackle this problem, this paper puts forward a deployment scheme of virtual machines for campus cloud platform, defines the course requirement model and the physical machine load model, and proposes a virtual machine deployment algorithm. This scheme can adapt to the characteristics of teaching applications such as periodicity, predictability, and batch, and achieve the purpose of energy saving and load balancing. Experiments show that the scheme can effectively reduce power consumption and achieve load balancing.

[1]  Massoud Pedram,et al.  Energy-Efficient Virtual Machine Replication and Placement in a Cloud Computing System , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[2]  Ruay-Shiung Chang,et al.  A Predictive Method for Workload Forecasting in the Cloud Environment , 2013, EMC/HumanCom.

[3]  Rajkumar Buyya,et al.  CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications , 2010, 2010 24th IEEE International Conference on Advanced Information Networking and Applications.

[4]  Hongke Zhang,et al.  An Optimization-Based Scheme for Efficient Virtual Machine Placement , 2013, International Journal of Parallel Programming.

[5]  Samir Khuller,et al.  Energy efficient scheduling via partial shutdown , 2009, SODA '10.

[6]  Xiang Li,et al.  A Sensory-Data-Hosting Oriented Scheduling Strategy on Virtual Machine , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[7]  Vijay Sukthankar,et al.  An optimized capacity planning approach for virtual infrastructure exhibiting stochastic workload , 2010, SAC '10.

[8]  Dai Min Deployment and Scheduling of Virtual Machines in Cloud Computing: An "AHP" Approach , 2013 .

[9]  Rajkumar Buyya,et al.  Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints , 2013, IEEE Transactions on Parallel and Distributed Systems.

[10]  Valli Kumari Vatsavayi,et al.  A model view controller based Self-Adjusting Clustering Framework , 2014, J. Syst. Softw..