Optimizing Quality of Service Using Fuzzy Control

Diao, Yixin; Hellerstein, Joseph L.; Parekh, Sujay

doi:10.1007/3-540-36110-3_7

Cited by 54 publications

(33 citation statements)

References 12 publications

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“…Recently, control theory has been applied to computing systems including control of HTTP servers [7], [8], email servers [9], quality of service assurance [10], internet traffic control [11], and load balancing. Computing systems have some characteristics that are typically not seen in traditional control applications in robotics and aircraft.…”

Section: B Feedback Control In Computing Systemsmentioning

confidence: 99%

Adaptive control of Bayesian network computation

Reed

Ishihara

Mengshoel

2012

2012 5th International Symposium on Resilient Control Systems

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Abstract-This paper considers the problem of providing, for computational processes, soft real-time (or reactive) response without the use of a hard real-time operating system. In particular, we focus on the problem of reactively computing fault diagnosis by means of different Bayesian network inference algorithms on non-real-time operating systems where low-criticality (background) process activity and system load is unpredictable.To address this problem, we take in this paper a reconfigurable adaptive control approach. Computation time is modeled using an ARX model where the input consists of the maximum number of background processes allowed to run at any given time. To ensure that the reactive (high-criticality) diagnosis is computed within a set time frame, we introduce a minimum degree pole placement controller to impose a limit on the maximum number of low-criticality processes. Experimentally, we perform electrical power system diagnosis using a Bayesian network model of and data from a NASA electrical power network. The Bayesian network inference algorithms likelihood weighting and junction tree propagation are successfully applied and changed mid-simulation to investigate how inference computation time changes in an unpredictable operating system, as well as how the controller reacts to inference algorithm changes.

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Section: B Feedback Control In Computing Systemsmentioning

confidence: 99%

Adaptive control of Bayesian network computation

Reed

Ishihara

Mengshoel

2012

2012 5th International Symposium on Resilient Control Systems

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“…As a result, businesses are seeking quality of service (QoS) guarantees from their service providers. (Diao et al, 2002a). These guarantees are expressed as part of service level agreements (SLAs).…”

Section: Fuzzy Logic Conceptsmentioning

confidence: 99%

An Application of Fuzzy Controllers: Autonomic Computing Systems

Harish¹,

Sekaran²

2011

Fuzzy Controllers, Theory and Applications

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“…Similarly, the authors in [22] propose a profit-oriented feedback control system that automates the admission control decisions in a way that balances the loss of revenue due to rejected work against the penalties incurred if admitted work has excessive response times. The authors in [23] describe an approach to automate parameter tuning using a fuzzy controller that employs rules incorporating qualitative knowledge of the effect of tuning parameters.…”

Section: Related Workmentioning

confidence: 99%

An Approach in using Differentiated Services to Maximize Profit in an Autonomic Computing System

Venkatarama¹,

Chandrasekaran²

2010

IJCA

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Ecommerce is an area where an Autonomic computing system could be very effectively deployed. The growth of ecommerce has created demand for services with financial incentives for service providers. Revenues accrue if the admitted requests are processed within the specified deadline and costs are incurred otherwise. In case of heavy load, it will not be possible to process all requests within the deadlines. It is beneficial to concentrate on those requests with which larger profits are associated. This paper describes an approach wherein a fuzzy controller is used which automatically allocates resources for priority requests in proportion to the number of priority requests. This is an illustration of the self-optimizing characteristic of an autonomic computing system.

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Optimizing Quality of Service Using Fuzzy Control

Cited by 54 publications

References 12 publications

Adaptive control of Bayesian network computation

Adaptive control of Bayesian network computation

An Application of Fuzzy Controllers: Autonomic Computing Systems

An Approach in using Differentiated Services to Maximize Profit in an Autonomic Computing System

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