Feedback Control of Cyber-physical Systems with Multi Resource Dependencies and Model Uncertainties

Lindberg, Mikael; Årzén, Karl-Erik

doi:10.1109/rtss.2010.14

Cited by 22 publications

(13 citation statements)

References 22 publications

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“…Models well suited for control purpose must be simple enough to allow for the synthesis of a control algorithm, while being able to capture the essence of the system behaviour. Typically, modeling for the control of autonomic computing systems needs to consider trade-offs between the control objectives and the cost needed to reach them through the execution of parallel activities running on shared hardware and software resources [47].…”

Section: Inriamentioning

confidence: 99%

Feedback Control as MAPE-K Loop in Autonomic Computing

Rutten

Marchand

Simon

2017

Lecture Notes in Computer Science

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Abstract:Computing systems are becoming more and more dynamically reconfigurable or adaptive, to be flexible w.r.t. their environment and to automate their administration. Autonomic computing proposes a general structure of feedback loop to take this into account. In this report, we are particularly interested in approaches where this feedback loop is considered as a case of control loop where techniques stemming from Control Theory can be used to design efficient safe, and predictable controllers. This approach is emerging, with separate and dispersed effort, in different areas of the field of reconfigurable or adaptive computing, at software or architecture level. This report surveys these approaches from the point of view of control theory techniques, continuous and discrete, in their application to the feedback control of computing systems, and proposes interpretations of feedback control loops as MAPE-K loop, illustrated with case studies.

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Section: Inriamentioning

confidence: 99%

Feedback Control as MAPE-K Loop in Autonomic Computing

Rutten

Marchand

Simon

2017

Lecture Notes in Computer Science

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“…Although in theory this approach can support existing multiple tasks in the servers, the authors have evaluated their work only using one task in each server. In [24], in addition to the dynamic task execution times, the CPU temperature is considered as a dynamic hardware constraint which affect the total available CPU utilization. We have proposed the AHSF framework in [4] which uses a similar approach as the work presented in [8], but targeting hierarchical scheduling frameworks and also taking the problem of CPU overload into account by adding the criticality of modules (applications, subsystems, partitions) during online resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Adaptive hierarchical scheduling framework: Configuration and evaluation

Khalilzad

Behnam

Nolte

2013

2013 IEEE 18th Conference on Emerging Technologies &Amp; Factory Automation (ETFA)

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Abstract-We have introduced an adaptive hierarchical scheduling framework as a solution for composing dynamic realtime systems, i.e., systems where the CPU demand of its tasks are subjected to unknown and potentially drastic changes during runtime. The framework consists of a controller which periodically adapts the system to the current load situation. In this paper, we unveil and explore the detailed behavior and performance of such an adaptive framework. Specifically, we investigate the controller configurations enabling efficient control parameters which maximizes performance, and we evaluate the adaptive framework against a traditional static one.

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“…Control-theoretic approaches have recently been proposed for thermal management of real-time systems [12,21]. Our previous work [12] proposed a feedback control algorithm that enforces thermal and real-time constraints simultaneously.…”

Section: Related Workmentioning

confidence: 99%

“…In recent years, control-theoretic thermal management approaches have shown promise in [8,11,12,21,34,35,37] handling uncertainties in thermal characteristics. In contrast to heuristic-based design relying on trial-and-error, controltheoretic approaches provide a scientific framework for systematic design and analysis of thermal control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Feedback thermal control of real-time systems on multicore processors

Kottenstette

et al. 2012

Proceedings of the Tenth ACM International Conference on Embedded Software

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Embedded real-time systems face significant challenges in thermal management. While earlier research on feedback thermal control has shown promise in dealing with the uncertainty in thermal characteristics, multicore processors introduce new challenges that cannot be handled by previous solutions designed for single-core processors. Multicore processors require the temperature and real-time performance of multiple cores be controlled simultaneously, leading to multi-input-multi-output control problems with inter-core thermal coupling. Furthermore, current Dynamic Voltage and Frequency Scaling (DVFS) mechanisms only support a finite set of states, leading to discrete control variables that cannot be handled by standard linear control techniques. This paper presents Real-Time Multicore Thermal Control (RT-MTC), a novel feedback thermal control framework specifically designed for multicore real-time systems. RT-MTC dynamically enforces both the desired temperature set point and the schedulable CPU utilization bound of a multicore processor through DVFS. RT-MTC employs a rigorously designed, efficient controller that can achieve effective thermal control with the small number of frequencies commonly supported by current processors. The robustness and advantages of RT-MTC over existing thermal control approaches are demonstrated through both experiments on an Intel Core 2 Duo processor and simulations under a wide range of uncertainties in power consumption.

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Feedback Control of Cyber-physical Systems with Multi Resource Dependencies and Model Uncertainties

Cited by 22 publications

References 22 publications

Feedback Control as MAPE-K Loop in Autonomic Computing

Feedback Control as MAPE-K Loop in Autonomic Computing

Adaptive hierarchical scheduling framework: Configuration and evaluation

Feedback thermal control of real-time systems on multicore processors

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