Award Winner 2012
by Shicong Meng
State monitoring is a fundamental building block for Cloud services. The demand for providing state monitoring as services (MaaS) continues to grow and is evidenced by CloudWatch from Amazon EC2, which allows cloud consumers to pay for monitoring a selection of performance metrics with coarse-grained periodical sampling of runtime states. One of the key challenges for wide deployment of MaaS is to provide better balance among a set of critical quality and performance parameters, such as accuracy, cost, scalability and customizability.
This dissertation research is dedicated to innovative research and development of an elastic framework for providing state monitoring as a service (MaaS) . We analyze limitations of existing techniques, systematically identify the need and the challenges at different layers of a Cloud monitoring service platform, and develop a suite of distributed monitoring techniques to support for flexible monitoring infrastructure, cost-effective state monitoring and monitoring-enhanced Cloud management. At the monitoring infrastructure layer, we develop techniques to support multi-tenancy of monitoring services by exploring cost sharing between monitoring tasks and safeguarding monitoring resource usage [2,3]. To provide elasticity in monitoring, we propose techniques to allow the monitoring infrastructure to self-scale with monitoring demand . At the cost-effective state monitoring layer, we devise several new state monitoring functionalities to meet unique functional requirements in Cloud monitoring. Violation likelihood state monitoring explores the benefits of consolidating monitoring workloads by allowing utility-driven monitoring intensity tuning on individual monitoring tasks and identifying correlations between monitoring tasks. Window based state monitoring leverages distributed windows for the best monitoring accuracy and communication efficiency [5,6]. Reliable state monitoring is robust to both transient and long-lasting communication issues caused by component failures or cross-VM performance interferences . At the monitoring-enhanced Cloud management layer, we devise a novel technique to learn about the performance characteristics of both Cloud infrastructure and Cloud applications from cumulative performance monitoring data to increase the cloud deployment efficiency.
This dissertation research presents only one step towards a truly scalable and customizable MaaS solution. The methodology developed in this dissertation can help to address important issues in monitoring heterogeneity, smart cloud management and security and privacy that are critical for MaaS to be a successful service computing metaphor for Cloud state management.
 Shicong Meng and Ling Liu "Enhanced Monitoring-as-a-Service for Effective Cloud Management". IEEE Transactions on Computers (TC), to appear.
 Shicong Meng, Srinivas Kashyap, Chitra Venkatramani and Ling Liu "Resource-Aware Application State Monitoring". IEEE Transactions on Parallel and Distributed Systems (TPDS), VOL. 23, NO. 12, 2315-2329, December 2012.
 Shicong Meng, Srinivas Karshyap, Chitra Venketramani and Ling Liu, "REMO: Resource-Aware Application State Monitoring for Large-Scale Distributed Systems". Proceedings of IEEE Int. Conf. on Distributed Computing (ICDCS'09), June 22-26, in Montreal, Quebec, Canada.
 Shicong Meng, Ling Liu and Ting Wang "State Monitoring in Cloud Datacenters". IEEE Transactions on Knowledge and Data Engineering (TKDE), Special Section on Cloud Data Management, VOL. 23, NO. 9, SEPTEMBER 2011.
 Shicong Meng, Ting Wang and Ling Liu, "Monitoring Continuous State Violation in Datacenters: Exploring the Time Dimension". 26th IEEE International Conference on Data Engineering (ICDE'10), March 1-6, 2010, Long Beach, California, USA.
 Shicong Meng, Arun Iyengar, Isabelle Rouvellou, Ling Liu, Kisung Lee, Balaji Palanisamy, Yuzhe Tang. "Reliable State Monitoring in Cloud Datacenters", Proceedings of IEEE Int. Conf. on Cloud Computing (IEEE Cloud'12), (Best Paper Award), Honolulu, Hawaii, USA on 6/24-6/29, 2012.