Internal Server State Estimation Using Event-based Particle Filtering
(2018) 4th International Conference on Event-Based Control, Communication and Signal Processing- Abstract
- 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... (More) - 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. (Less) - Abstract (Swedish)
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/26b0a789-ea5e-4a16-89b4-129076a1de05
- author
- Ruuskanen, Johan LU and Cervin, Anton LU
- organization
- publishing date
- 2018-06-27
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Proceedings of the 4th International Conference on Event-Based Control, Communication, and Signal Processing
- conference name
- 4th International Conference on Event-Based Control, Communication and Signal Processing
- conference location
- Perpignan, France
- conference dates
- 2018-06-27 - 2018-06-29
- project
- Event-Based Control of Stochastic Systems with Application to Server Systems
- Event-Based Information Fusion for the Self-Adaptive Cloud
- language
- English
- LU publication?
- yes
- id
- 26b0a789-ea5e-4a16-89b4-129076a1de05
- date added to LUP
- 2018-08-17 12:58:31
- date last changed
- 2021-04-10 02:19:45
@inproceedings{26b0a789-ea5e-4a16-89b4-129076a1de05, abstract = {{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.<br/><br/>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.}}, author = {{Ruuskanen, Johan and Cervin, Anton}}, booktitle = {{Proceedings of the 4th International Conference on Event-Based Control, Communication, and Signal Processing}}, language = {{eng}}, month = {{06}}, title = {{Internal Server State Estimation Using Event-based Particle Filtering}}, url = {{https://lup.lub.lu.se/search/files/49609380/wip_ebccsp_18.pdf}}, year = {{2018}}, }