Reliable state monitoring in cloud datacenters

Shicong Meng, Arun K. Iyengar, Isabelle M. Rouvellou, Ling Liu, Kisung Lee, Balaji Palanisamy, Yuzhe Tang

Research output: Chapter in Book/Entry/PoemConference contribution

41 Scopus citations


State monitoring is widely used for detecting critical events and abnormalities of distributed systems. As the scale of such systems grows and the degree of workload consolidation increases in Cloud data centers, node failures and performance interferences, especially transient ones, become the norm rather than the exception. Hence, distributed state monitoring tasks are often exposed to impaired communication caused by such dynamics on different nodes. Unfortunately, existing distributed state monitoring approaches are often designed under the assumption of always-online distributed monitoring nodes and reliable inter-node communication. As a result, these approaches often produce misleading results which in turn introduce various problems to Cloud users who rely on state monitoring results to perform automatic management tasks such as auto-scaling. This paper introduces a new state monitoring approach that tackles this challenge by exposing and handling communication dynamics such as message delay and loss in Cloud monitoring environments. Our approach delivers two distinct features. First, it quantitatively estimates the accuracy of monitoring results to capture uncertainties introduced by messaging dynamics. This feature helps users to distinguish trustworthy monitoring results from ones heavily deviated from the truth, yet significantly improves monitoring utility compared with simple techniques that invalidate all monitoring results generated with the presence of messaging dynamics. Second, our approach also adapts to non-transient messaging issues by reconfiguring distributed monitoring algorithms to minimize monitoring errors. Our experimental results show that, even under severe message loss and delay, our approach consistently improves monitoring accuracy, and when applied to Cloud application auto-scaling, outperforms existing state monitoring techniques in terms of the ability to correctly trigger dynamic provisioning.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Number of pages8
StatePublished - 2012
Externally publishedYes
Event2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012 - Honolulu, HI, United States
Duration: Jun 24 2012Jun 29 2012

Publication series

NameProceedings - 2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012


Other2012 IEEE 5th International Conference on Cloud Computing, CLOUD 2012
Country/TerritoryUnited States
CityHonolulu, HI


  • Cloud Monitoring
  • Distributed Thresholds
  • Message Delay and Loss
  • Reliability
  • State Monitoring

ASJC Scopus subject areas

  • Software


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