Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/7436
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dc.contributor.authorKhanna, R.
dc.contributor.authorGanguli, M.
dc.contributor.authorNarayan, A.
dc.contributor.authorAbhiram, R.
dc.contributor.authorGupta, P.
dc.date.accessioned2020-03-30T09:59:06Z-
dc.date.available2020-03-30T09:59:06Z-
dc.date.issued2015
dc.identifier.citation2014 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2014, 2015, Vol., , pp.-en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7436-
dc.description.abstractIn a cloud service management environment, service level agreements (SLA) define the expectation of quality (Quality-of-Service) for managing performance loss in a given service-hosting environment comprising of a pool of compute resources. Typically, complexity of resource inter-dependencies in a server system often results to sub-optimal behaviors leading to performance loss. A well behaved model can anticipate the demand patterns and proactively react to the dynamic stresses in a timely and well optimized manner. Dynamic characterization methods can synthesize self-correcting workload fingerprint code-book that facilitates phase prediction to achieve continuous tuning through proactive workload-allocation and load-balancing. In this paper we introduce the methodology that facilitates the coordinated tuning of the system resources through phase-assisted dynamic characterization. We describe the method to develop a multi-variate phase model by learning and classifying the run-time behavior of workloads. We demonstrate the workload phase forecasting method using phase extraction using a combination of machine learning approach. Results show the new model is about 98% accurate in phase identification and 97.15% accurate in forecasting the compute demands. � 2014 IEEE.en_US
dc.titleAutonomic characterization of workloads using workload fingerprintingen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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