P&P: A Combined Push-Pull Model for Resource Monitoring in Cloud Computing Environment

Cloud computing paradigm contains many shared resources, such as infrastructures, data storage, various platforms and software. Resource monitoring involves collecting information of system resources to facilitate decision making by other components in Cloud environment. It is the foundation of many major Cloud computing operations. In this paper, we extend the prevailing monitoring methods in Grid computing, namely Pull model and Push model, to the paradigm of Cloud computing. In Grid computing, we find that in certain conditions, Push model has high consistency but low efficiency, while Pull model has low consistency but high efficiency. Based on complementary properties of the two models, we propose a user-oriented resource monitoring model named Push&Pull (P&P) for Cloud computing, which employs both the above two models, and switches the two models intelligently according to users’ requirements and monitored resources’ status. The experimental result shows that the P&P model decreases updating costs and satisfies various users’ requirements of consistency between monitoring components and monitored resources compared to the original models.

[1]  Prashant J. Shenoy,et al.  Adaptive push-pull: disseminating dynamic web data , 2001, WWW '01.

[2]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[3]  Ann C. Gentile,et al.  Resource monitoring and management with OVIS to enable HPC in cloud computing environments , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[4]  Ming Wu,et al.  Grid harvest service: A performance system of grid computing , 2006, J. Parallel Distributed Comput..

[5]  Ralph Müller-Pfefferkorn,et al.  User- and job-centric monitoring: Analysing and presenting large amounts of monitoring data , 2008, 2008 9th IEEE/ACM International Conference on Grid Computing.

[6]  Werner Nutt,et al.  The Relational Grid Monitoring Architecture: Mediating Information about the Grid , 2004, Journal of Grid Computing.

[7]  Richard Wolski,et al.  The network weather service: a distributed resource performance forecasting service for metacomputing , 1999, Future Gener. Comput. Syst..

[8]  Rizos Sakellariou,et al.  A taxonomy of grid monitoring systems , 2005, Future Gener. Comput. Syst..

[9]  Srinivasan Parthasarathy,et al.  Adaptive polling of grid resource monitors using a slacker coherence model , 2003, High Performance Distributed Computing, 2003. Proceedings. 12th IEEE International Symposium on.

[10]  Ruay-Shiung Chang,et al.  A new mechanism for resource monitoring in Grid computing , 2009, Future Gener. Comput. Syst..

[11]  Mario Lauria,et al.  A slacker coherence protocol for pull-based monitoring of on-line data sources , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[12]  Werner Nutt,et al.  R-GMA: An Information Integration System for Grid Monitoring , 2003, OTM.

[13]  Tiziana Ferrari,et al.  WMSMonitor: A monitoring tool for workload and job lifecycle in Grids , 2008, 2008 9th IEEE/ACM International Conference on Grid Computing.

[14]  Ruth A. Aydt,et al.  A Grid Monitoring Architecture , 2002 .