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Model-Based Autonomic Performance Management of Distributed Enterprise Systems and Applications

Distributed computing systems (DCS) host a wide variety of enterprise applications in dynamic and uncertain operating environments. These applications require stringent reliability, availability, and quality of service (QoS) guarantee to maintain their service level agreements (SLAs). Due to the growing size and complexity of DCS, an autonomic performance management system is required to maintain SLAs of these applications. A model-based autonomic performance management structure is developed in this dissertation for applications hosted in DCS. A systematic application performance modeling approach is introduced in this dissertation to define the dependency relationships among the system parameters, which impact the application performance. The developed application performance model is used by a model-based predictive controller for managing multi-dimensional QoS objectives of the application. A distributed control structure is also developed to provide scalability for performance management and to eliminate the requirement of approximate behavior modeling in the hierarchical arrangement of DCS. A distributed monitoring system is also introduced in this dissertation to keep track of computational resources utilization, application performance statistics, and scientific application execution in a DCS, with minimum latency and controllable resource overhead. The developed monitoring system is self-configuring, self-aware, and fault-tolerant. It can also be deployed for monitoring of DCS with heterogeneous computing systems. A configurable autonomic performance management system is developed using modelintegrated computing methodologies, which allow administrators to define the initial settings of the application, QoS objectives, system components’ placement, and interaction among these components in a graphical domain specific modeling environment. This configurable performance management system facilitates reusability of the same components, algorithms, and application performance models in different deployment settings.

Identiferoai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-4128
Date14 December 2013
CreatorsMehrotra, Rajat
PublisherScholars Junction
Source SetsMississippi State University
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations

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