Research on the Resource Monitoring Model Under Cloud Computing Environment

Resource monitoring is an important part of resource management under the cloud computing environment, which provides a better reference for resource allocation, task scheduling and load balancing. Because of the commercial applications target of billing the user for the use of resources, the high virtualization, scalability and transparency of the cloud computing environment's resources, the existing resource monitoring methods of both distributed computing and grid computing can not satisfy the cloud computing environment completely. So, according to the characteristics of cloud computing platforms, we present a novel resource monitoring model appropriately adapted to cloud computing environment, which combines VMM (Virtual Machine Monitor) and the C/C++ called by Java to obtain the information of the resource status. Both theoretical analysis and experiments results show that the model can be used to collect resource monitoring information on nodes and VM (virtual machine), which not only meets the requirements of cloud computing platform features but also has a good property of effectiveness.

[1]  Renato J. O. Figueiredo,et al.  Adaptive Predictor Integration for System Performance Prediction , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[2]  Mark Baker,et al.  A Flexible Monitoring and Notification System for Distributed Resources , 2008, 2008 International Symposium on Parallel and Distributed Computing.

[3]  Wang Peng,et al.  Cloud Computing Model Based on MPI , 2009 .

[4]  Juan Touriño,et al.  Integrating the common information model with MDS4 , 2008, 2008 9th IEEE/ACM International Conference on Grid Computing.

[5]  Yi Liu,et al.  WSRF-Based Distributed Visualization , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[6]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.