Virtual Machine Allocation Policy in Cloud Computing Using CloudSim in Java

Cloud computing is a very powerful concept that can be used to enhance the next generation data center and allow service provider to use data center capability provided by cloud and develop the application based on user requirement. Data center of this cloud computing has huge number of resources and list of applications (with different architecture, configuration and requirement for deployment) want to use those resource. Cloud computing environment uses virtualization concept and provides resources to application by creating and allocating virtual machine to specific application. There for resource allocation policies and load balance policies play very vital role in allocating and managing the resources among various application in clod computing life cycle. CloudSim is an extensible simulation toolkit that enables modeling and simulation of Cloud computing environments. The model proposed by this paper for dynamic load balance policy with considering different attributes and different service level agreements in cloud computing environment helps this environment to utilize their resources and improves performance. The proposed model uses Hungarian algorithm and the result is verified by simulating this model using CloudSim.

[1]  Harold W. Kuhn,et al.  The Hungarian method for the assignment problem , 1955, 50 Years of Integer Programming.

[2]  Bu-Sung Lee,et al.  Optimal virtual machine placement across multiple cloud providers , 2009, 2009 IEEE Asia-Pacific Services Computing Conference (APSCC).

[3]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[4]  Meikang Qiu,et al.  Adaptive resource allocation for preemptable jobs in cloud systems , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[5]  Zhoujun Li,et al.  Adaptive Management of Virtualized Resources in Cloud Computing Using Feedback Control , 2009, 2009 First International Conference on Information Science and Engineering.

[6]  Henri Casanova,et al.  Scheduling distributed applications: the SimGrid simulation framework , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[7]  H. Kuhn The Hungarian method for the assignment problem , 1955 .

[8]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[9]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[10]  Ian T. Foster,et al.  GangSim: a simulator for grid scheduling studies , 2005, CCGrid 2005. IEEE International Symposium on Cluster Computing and the Grid, 2005..

[11]  Foreword and Editorial International Journal of Grid Distribution Computing , .

[12]  Rajkumar Buyya,et al.  GridSim: a toolkit for the modeling and simulation of distributed resource management and scheduling for Grid computing , 2002, Concurr. Comput. Pract. Exp..

[13]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[14]  Marc Frîncu,et al.  Multi-objective Meta-heuristics for Scheduling Applications with High Availability Requirements and Cost Constraints in Multi-Cloud Environments , 2011, 2011 Fourth IEEE International Conference on Utility and Cloud Computing.

[15]  Johan Tordsson,et al.  Cloud brokering mechanisms for optimized placement of virtual machines across multiple providers , 2012, Future Gener. Comput. Syst..