Internal Server State Estimation Using Event-based Particle Filtering

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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 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.
Original languageEnglish
Title of host publicationProceedings of the 4th International Conference on Event-Based Control, Communication, and Signal Processing
Publication statusPublished - 2018 Jun 27
Event4th International Conference on Event-Based Control, Communication and Signal Processing - Perpignan, France
Duration: 2018 Jun 272018 Jun 29
https://www.ebccsp2018.org/

Conference

Conference4th International Conference on Event-Based Control, Communication and Signal Processing
Abbreviated titleEBCCSP 2018
Country/TerritoryFrance
CityPerpignan
Period2018/06/272018/06/29
Internet address

Subject classification (UKÄ)

  • Control Engineering

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