Policy Based Management in the Global Information Grid

Choudhary, Abdur Rahim

doi:10.1504/ijipt.2008.019298

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…One way to autonomically adapt enterprise DRE pub/sub systems involves policy-based approaches [7][1] [8] that externalize and codify logic to manage the behavior of the systems. Policy-based approaches provide deterministic response times to guide appropriate adjustments given changes in the environment and can be optimized to ensure low-latency performance.…”

Section: Emerging Trends and Challengesmentioning

confidence: 99%

Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

Hoffert

Mack

Schmidt

2010

NPA

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Quality-of-service (QoS)-enabled publish/subscribe (pub/sub) middleware provides the infrastructure needed to disseminate data predictably, reliably, and scalably in distributed real-time and embedded (DRE) systems. Maintaining QoS properties as the operating environment fluctuates is challenging, however, since the chosen mechanism (e.g., transport protocol or caching algorithm for data persistence) may no longer provide the needed QoS. Moreover, some adaptation approaches are tailored for particular types of operating environments, such as environments whose configuration properties (e.g., number of data receivers or data sending rate) are known prior to runtime versus unknown until runtime. For DRE pub/sub systems operating in dynamic environments, adjustments to mechanisms must be timely, accurate for known environments, and resilient to environments unknown until runtime. Several adaptation approaches, such as policy-based [1] and reinforcement learning [2] have been developed to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for DRE pub/sub systems, however, due to their stringent accuracy, timeliness, and development complexity requirements. Supervised machine learning techniques, such as artificial neural networks (ANNs) [3] and support vector machines (SVMs) [4], are promising approaches to address the accuracy, time complexity, and development complexity concerns of adaptive enterprise DRE systems. This article describes the results of research that (1) empirically evaluates supervised machine learning techniques used to adapt the transport protocols of QoS-enabled pub/sub middleware autonomically in a dynamic environment and (2) integrates multiple techniques to increase accuracy for environments known a priori and not known until runtime. Our results show that both ANNs and SVMs provide constant time complexity, low latency, and reduced development complexity. ANNs are generally more accurate in providing adaptation guidance for environments whose properties are known prior to runtime and provide sub-μsec response times, whereas SVMs provide higher accuracy with μsec latencies for environments whose properties are not known until runtime. Both approaches can be leveraged together with QoS-enabled pub/sub middleware to address the timeliness, accuracy, and development complexity needs of enterprise DRE systems executing in dynamic environments.

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Section: Emerging Trends and Challengesmentioning

confidence: 99%

Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

Hoffert

Mack

Schmidt

2010

NPA

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“…One way to adapt enterprise DRE systems autonomically involves the use of policy-based approaches [3] that externalize and codify logic to determine the behavior of managed systems. Policy-based approaches provide deterministic response times to perform appropriate adjustments given changes in the environment.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments

Hoffert¹,

Schmidt²,

Gokhale³

2011

International Journal of Computational Intelligence Systems

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Several adaptation approaches have been devised to ensure end-to-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable, however, for the stringent accuracy, timeliness, and development complexity requirements of distributed real-time and embedded (DRE) systems. This paper empirically evaluates constant-time supervised machine learning techniques, such as artificial neural networks (ANNs) and support vector machines (SVMs), and presents a composite metric to support quantitative evaluation of accuracy and timeliness for these adaptation approaches.

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“…One way to adapt enterprise DRE systems autonomically involves the use of policy-based approaches [1] that externalize and codify logic to determine the behavior of managed systems. Policy-based approaches provide deterministic response times to perform appropriate adjustments given changes in the environment and can be optimized to ensure efficient performance.…”

Section: Introductionmentioning

confidence: 99%

Evaluating Supervised Machine Learning for Adapting Enterprise DRE Systems

Hoffert

Schmidt

2010

2010 International Symposium on Intelligence Information Processing and Trusted Computing

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Abstract-Several adaptation approaches, such as policy-based and reinforcement learning, have been devised to ensure endto-end quality-of-service (QoS) for enterprise distributed systems in dynamic operating environments. Not all approaches are applicable for distributed real-time and embedded (DRE) systems, however, which have stringent accuracy, timeliness, and development complexity requirements. Supervised machine learning techniques, such as artificial neural networks (ANNs), are a promising approach to address time complexity concerns of adaptive enterprise DRE systems. Likewise, ANNs address the development complexity of adaptive DRE systems by ensuring that adaptations are appropriate for the operating environment. This paper empirically evaluates the accuracy and timeliness of the ANN machine learning technique for environments on which it has been trained. Our results show ANNs are highly accurate in determining correct adaptations and provide predictable time complexity, e.g., with response times less than 6 µseconds.

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Policy Based Management in the Global Information Grid

Cited by 4 publications

References 11 publications

Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

Integrating Machine Learning Techniques to Adapt Protocols for QoS-enabled Distributed Real-time and Embedded Publish/Subscribe Middleware

Evaluating Timeliness and Accuracy Trade-offs of Supervised Machine Learning for Adapting Enterprise DRE Systems in Dynamic Environments

Evaluating Supervised Machine Learning for Adapting Enterprise DRE Systems

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