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Internal Server Sta...
Internal Server State Estimation Using Event-based Particle Filtering
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- Ruuskanen, Johan (author)
- Lund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH,Wallenberg AI, Autonomous Systems and Software Program (WASP)
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- Cervin, Anton (author)
- Lund University,Lunds universitet,Institutionen för reglerteknik,Institutioner vid LTH,Lunds Tekniska Högskola,Department of Automatic Control,Departments at LTH,Faculty of Engineering, LTH
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(creator_code:org_t)
- 2018
- 2018
- English.
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In: Proceedings of the 4th International Conference on Event-Based Control, Communication, and Signal Processing.
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Abstract
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- Closed-loop control of cloud resources requires there to be measurements readily available from the process in order to use the feedback mechanism to form a control law. If utilizing state-feedback control, sought states might be unfeasible or impossible to measure in real applications; instead they must be estimated. However, running the estimators in real time for all measurements will require a lot of computational overhead. Further, if the observer and process are disjoint, sending all measurements will put extra strain on the network.In this work-in-progress paper, we propose an event-based particle filter approach to capture the internal dynamics of a server with CPU-intensive workload whilst minimizing the required computation or inter-system network strain. Preliminary results show some promise as it outperforms estimators derived from analytic expression for stationary systems in service rate estimation over number of samples used for a simulation experiment. Further we show that for the same simulation, an event-based sampling strategy outperforms periodic sampling.
- Closed-loop control of cloud resources requires there to be measurements readily available from the process in order to use the feedback mechanism to form a control law. If utilizing state-feedback control, sought states might be unfeasible or impossible to measure in real applications; instead they must be estimated. However, running the estimators in real time for all measurements will require a lot of computational overhead. Further, if the observer and process are disjoint, sending all measurements will put extra strain on the network. In this work-in-progress paper, we propose an event-based particle filter approach to capture the internal dynamics of a server with CPU-intensive workload whilst minimizing the required computation or inter-system network strain. Preliminary results show some promise as it outperforms estimators derived from analytic expression for stationary systems in service rate estimation over number of samples used for a simulation experiment. Further we show that for the same simulation, an event-based sampling strategy outperforms periodic sampling.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
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