Resource Monitoring and Prediction in Cloud Computing Environments

Cloud computing provides elastic, scalable resource sharing service by resource management. Resource monitoring and prediction are the foundation to achieve resource automation, high performance management in cloud computing environment. This paper addresses the resource monitoring and prediction problem in cloud computing environment, designs and implements an adaptive resource monitoring framework for cloud computing, and presents a resource prediction mechanism based on Vector Auto Regression (VAR) by the correlation between various resources. Related experiments show that the proposed resource monitoring framework can effectively monitor the resource usage in cloud computing environment, and prediction mechanism based on vector auto regression compared to other prediction mechanism could be more effective to predict resource usage.

[1]  Song Ying,et al.  resource management in internet-oriented data centers , 2012 .

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

[3]  Kai Hwang,et al.  Adaptive Workload Prediction of Grid Performance in Confidence Windows , 2010, IEEE Transactions on Parallel and Distributed Systems.

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

[5]  Xiaohui Gu,et al.  On Predictability of System Anomalies in Real World , 2010, 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems.

[6]  Luis Rodero-Merino,et al.  A break in the clouds: towards a cloud definition , 2008, CCRV.

[7]  Qiang Li,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .

[8]  Gaétan Hains,et al.  A resource prediction model for virtualization servers , 2012, 2012 International Conference on High Performance Computing & Simulation (HPCS).

[9]  Bo Zhang,et al.  Research on the Resource Monitoring Model Under Cloud Computing Environment , 2010, WISM.

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

[11]  Kang Chen,et al.  Cloud Computing: System Instances and Current Research: Cloud Computing: System Instances and Current Research , 2010 .

[12]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

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

[14]  Limin Xiao,et al.  Resource Management in Internet-Oriented Data Centers: Resource Management in Internet-Oriented Data Centers , 2012 .

[15]  Fang Dong,et al.  An effective data aggregation based adaptive long term CPU load prediction mechanism on computational grid , 2012, Future Gener. Comput. Syst..

[16]  Zheng Wei,et al.  Cloud Computing:System Instances and Current Research , 2009 .

[17]  Li Qiang,et al.  Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing: Adaptive Management and Multi-Objective Optimization for Virtual Machine Placement in Cloud Computing , 2012 .