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Documents authored by Esper, Khalil

Document
DOI: 10.4230/OASIcs.NG-RES.2024.4
History-Based Run-Time Requirement Enforcement of Non-Functional Properties on MPSoCs

Authors: Khalil Esper and Jürgen Teich

Published in: OASIcs, Volume 117, Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024)

Abstract
Embedded system applications usually have requirements regarding non-functional properties of their execution like latency or power consumption. Enforcement of such requirements can be implemented by a reactive control loop, where an enforcer determines based on a system response (feedback) how to control the system, e.g., by selecting the number of active cores allocated to a program or by scaling their voltage/frequency mode. It is of a particular interest to design enforcement strategies for which it is possible to provide formal guarantees with respect to requirement violations, especially under a largely varying environmental input (workload) per execution. In this paper, we consider enforcement strategies that are modeled by a finite state machine (FSM) and the environment by a discrete-time Markov chain. Such a formalization enables the formal verification of temporal properties (verification goals) regarding the satisfaction of requirements of a given enforcement strategy. In this paper, we propose history-based enforcement FSMs which compute a reaction not just on the current, but on a fixed history of K previously observed system responses. We then analyze the quality of such enforcement FSMs in terms of the probability of satisfying a given set of verification goals and compare them to enforcement FSMs that react solely on the current system response. As experimental results, we present three use cases while considering requirements on latency and power consumption. The results show that history-based enforcement FSMs outperform enforcement FSMs that only consider the current system response regarding the probability of satisfying a given set of verification goals.
Cite as

Khalil Esper and Jürgen Teich. History-Based Run-Time Requirement Enforcement of Non-Functional Properties on MPSoCs. In Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024). Open Access Series in Informatics (OASIcs), Volume 117, pp. 4:1-4:11, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@InProceedings{esper_et_al:OASIcs.NG-RES.2024.4,
  author =	{Esper, Khalil and Teich, J\"{u}rgen},
  title =	{{History-Based Run-Time Requirement Enforcement of Non-Functional Properties on MPSoCs}},
  booktitle =	{Fifth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2024)},
  pages =	{4:1--4:11},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-313-3},
  ISSN =	{2190-6807},
  year =	{2024},
  volume =	{117},
  editor =	{Yomsi, Patrick Meumeu and Wildermann, Stefan},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2024.4},
  URN =		{urn:nbn:de:0030-drops-197074},
  doi =		{10.4230/OASIcs.NG-RES.2024.4},
  annote =	{Keywords: Verification, Runtime Requirement Enforcement, History, Latency}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2023.7
RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs

Authors: Khalil Esper, Jan Spieck, Pierre-Louis Sixdenier, Stefan Wildermann, and Jürgen Teich

Published in: OASIcs, Volume 108, Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)

Abstract
In embedded systems, applications frequently have to meet non-functional requirements regarding, e.g., real-time or energy consumption constraints, when executing on a given MPSoC target platform. Feedback-based controllers have been proposed that react to transient environmental factors by adapting the DVFS settings or degree of parallelism following some predefined control strategy. However, it is, in general, not possible to give formal guarantees for the obtained controllers to satisfy a given set of non-functional requirements. Run-time requirement enforcement has emerged as a field of research for the enforcement of non-functional requirements at run-time, allowing to define and formally verify properties on respective control strategies specified by automata. However, techniques for the automatic generation of such controllers have not yet been established. In this paper, we propose a technique using reinforcement learning to automatically generate verifiable feedback-based enforcers. For that, we train a control policy based on a representative input sequence at design time. The learned control strategy is then transformed into a verifiable enforcement automaton which constitutes our run-time control model that can handle unseen input data. As a case study, we apply the approach to generate controllers that are able to increase the probability of satisfying a given set of requirement verification goals compared to multiple state-of-the-art approaches, as can be verified by model checkers.
Cite as

Khalil Esper, Jan Spieck, Pierre-Louis Sixdenier, Stefan Wildermann, and Jürgen Teich. RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs. In Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023). Open Access Series in Informatics (OASIcs), Volume 108, pp. 7:1-7:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{esper_et_al:OASIcs.NG-RES.2023.7,
  author =	{Esper, Khalil and Spieck, Jan and Sixdenier, Pierre-Louis and Wildermann, Stefan and Teich, J\"{u}rgen},
  title =	{{RAVEN: Reinforcement Learning for Generating Verifiable Run-Time Requirement Enforcers for MPSoCs}},
  booktitle =	{Fourth Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2023)},
  pages =	{7:1--7:16},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-268-6},
  ISSN =	{2190-6807},
  year =	{2023},
  volume =	{108},
  editor =	{Terraneo, Federico and Cattaneo, Daniele},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2023.7},
  URN =		{urn:nbn:de:0030-drops-177380},
  doi =		{10.4230/OASIcs.NG-RES.2023.7},
  annote =	{Keywords: Verification, Runtime Requirement Enforcement, Reinforcement Learning}
}
Document
DOI: 10.4230/OASIcs.NG-RES.2022.2
Multi-Requirement Enforcement of Non-Functional Properties on MPSoCs Using Enforcement FSMs - A Case Study

Authors: Khalil Esper, Stefan Wildermann, and Jürgen Teich

Published in: OASIcs, Volume 98, Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)

Abstract
Embedded system applications usually have to meet real-time, energy or safety requirements on programs typically concurrently executed on a given MPSoC target platform. Enforcing such properties, e.g., by adapting the number of processors allocated to a program or by scaling the voltage/frequency mode of involved processors, is a difficult problem to solve, especially with a typically large varying environmental input (workload) per execution. In a previous work [Esper et al., 2021], we formalized the related enforcement problem using (a) finite state machines to model enforcement strategies, (b) discrete-time Markov chains to model the uncertain environment determining the system’s workload, and (c) the system response that defines the feedback for the reactive enforcer. In this paper, we apply that approach to specify and verify multi-requirement enforcement strategies and assess a case study for enforcing two independent requirements at the same time, i.e., latency and energy consumption. We evaluate and compare different enforcement strategies using probabilistic verification for the use case of an object detection application.
Cite as

Khalil Esper, Stefan Wildermann, and Jürgen Teich. Multi-Requirement Enforcement of Non-Functional Properties on MPSoCs Using Enforcement FSMs - A Case Study. In Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022). Open Access Series in Informatics (OASIcs), Volume 98, pp. 2:1-2:13, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)

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@InProceedings{esper_et_al:OASIcs.NG-RES.2022.2,
  author =	{Esper, Khalil and Wildermann, Stefan and Teich, J\"{u}rgen},
  title =	{{Multi-Requirement Enforcement of Non-Functional Properties on MPSoCs Using Enforcement FSMs - A Case Study}},
  booktitle =	{Third Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2022)},
  pages =	{2:1--2:13},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-221-1},
  ISSN =	{2190-6807},
  year =	{2022},
  volume =	{98},
  editor =	{Bertogna, Marko and Terraneo, Federico and Reghenzani, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2022.2},
  URN =		{urn:nbn:de:0030-drops-161102},
  doi =		{10.4230/OASIcs.NG-RES.2022.2},
  annote =	{Keywords: Runtime Requirement Enforcement, Verification, Finite State Machine, Markov Chain, Energy Consumption, Probabilistic Model Cheking, PCTL, MPSoC}
}
Document
Invited Paper
DOI: 10.4230/OASIcs.NG-RES.2021.1
A Comparative Evaluation of Latency-Aware Energy Optimization Approaches in Many-Core Systems (Invited Paper)

Authors: Khalil Esper, Stefan Wildermann, and Jürgen Teich

Published in: OASIcs, Volume 87, Second Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2021)

Abstract
Many applications vary a lot in execution time depending on their workload. A prominent example is image processing applications, where the execution time is dependent on the content or the size of the processed input images. An interesting case is when these applications have quality-of-service requirements such as soft deadlines, that they should meet as good as possible. A further complicated case is when such applications have one or even multiple further objectives to optimize like, e.g., energy consumption. Approaches that dynamically adapt the processing resources to application needs under multiple optimization goals and constraints can be characterized into the application-specific and feedback-based techniques. Whereas application-specific approaches typically statically use an offline stage to determine the best configuration for each known workload, feedback-based approaches, using, e.g., control theory, adapt the system without the need of knowing the effect of workload on these goals. In this paper, we evaluate a state-of-the-art approach of each of the two categories and compare them for image processing applications in terms of energy consumption and number of deadline misses on a given many-core architecture. In addition, we propose a second feedback-based approach that is based on finite state machines (FSMs). The obtained results suggest that whereas the state-of-the-art application-specific approach is able to meet a specified latency deadline whenever possible while consuming the least amount of energy, it requires a perfect characterization of the workload on a given many-core system. If such knowledge is not available, the feedback-based approaches have their strengths in achieving comparable energy savings, but missing deadlines more often.
Cite as

Khalil Esper, Stefan Wildermann, and Jürgen Teich. A Comparative Evaluation of Latency-Aware Energy Optimization Approaches in Many-Core Systems (Invited Paper). In Second Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2021). Open Access Series in Informatics (OASIcs), Volume 87, pp. 1:1-1:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@InProceedings{esper_et_al:OASIcs.NG-RES.2021.1,
  author =	{Esper, Khalil and Wildermann, Stefan and Teich, J\"{u}rgen},
  title =	{{A Comparative Evaluation of Latency-Aware Energy Optimization Approaches in Many-Core Systems}},
  booktitle =	{Second Workshop on Next Generation Real-Time Embedded Systems (NG-RES 2021)},
  pages =	{1:1--1:12},
  series =	{Open Access Series in Informatics (OASIcs)},
  ISBN =	{978-3-95977-178-8},
  ISSN =	{2190-6807},
  year =	{2021},
  volume =	{87},
  editor =	{Bertogna, Marko and Terraneo, Federico},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.NG-RES.2021.1},
  URN =		{urn:nbn:de:0030-drops-134779},
  doi =		{10.4230/OASIcs.NG-RES.2021.1},
  annote =	{Keywords: energy optimization, control-theory, timing analysis, soft real-time, dynamic voltage and frequency scaling, finite state machines, multi-core, many-core}
}
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