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Adaptation Logic

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Christian Becker – One of the best experts on this subject based on the ideXlab platform.

  • ICAC – A Dynamic Software Product Line Approach for Adaptation Planning in Autonomic Computing Systems
    2017 IEEE International Conference on Autonomic Computing (ICAC), 2017
    Co-Authors: Martin Pfannemueller, Markus Weckesser, Christian Krupitzer, Christian Becker
    Abstract:

    Modeling the reasoning component of self-adapting systems including its context is a challenging task. Context feature models used in dynamic software product lines help to capture the capabilities of a software as well as the monitored context values. This enables the possibility to add constraints between the context and system features. In this paper, we present an Adaptation Logic architecture for specifying the knowledge for reasoning in a model-based manner by means of dynamic software product lines. The whole knowledge for reasoning is encapsulated inside a component which enables the reuse of the Adaptation Logic for various application scenarios. Thus, the system designer only has to specify the Adaptation Logic‘s knowledge and implement the according interfaces in the managed resource. We evaluate the Adaptation Logic using our architecture in a distributed computing scenario.

  • ICAC – Adding Self-Improvement to an Autonomic Traffic Management System
    2017 IEEE International Conference on Autonomic Computing (ICAC), 2017
    Co-Authors: Christian Krupitzer, Felix Maximilian Roth, Alexander Frömmgen, Julian Otto, Christian Becker
    Abstract:

    Autonomic Computing and self-adaptive systems are a response to the increasing complexity required to cope with changing environments and varying system resources. However, the complexity of the Adaptation Logic itself increases with the available information in particular for distributed systems. This leads to uncertainty at runtime resulting in incompleteness in the representation of Adaptation goals, models, or rules. Self-improvement which changes the Adaptation Logic at runtime through meta-Adaptation addresses the uncertainty issue.In this paper, we present and discuss a self-improvement case study for an autonomic traffic management system. We adapt parameters of the Adaptation Logic through rule learning as well as the structure of the Adaptation Logic, e.g., from central to decentralized control. We show that the resulting implementation enables continuous self-improvement of the system even in situations that have not been taken into account at design time.

  • PerCom Workshops – RoCoSys: A framework for coordination of mobile IoT devices
    2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017
    Co-Authors: Christian Krupitzer, Christian Becker, Martin Breitbach, Johannes Saal, Michele Segata, Renato Lo Cigno
    Abstract:

    Mobile IoT devices enable new classes of systems, such as cyber-physical systems. These systems pose challenges as they should seamlessly interact with users and other systems. In this paper, we address the problem of interaction between mobile pervasive IoT devices. Our contributions are threefold. First, we present a concept for a framework for coordination of mobile IoT devices. Second, we implement a reusable robot platform using the Mindstorms toolkit and a customizable Adaptation Logic for their coordination based on our framework. Third, we show its usability with two applications: an intelligent vehicle highway system as well as a smart vacuum cleaner.

Christian Krupitzer – One of the best experts on this subject based on the ideXlab platform.

  • ICAC – A Dynamic Software Product Line Approach for Adaptation Planning in Autonomic Computing Systems
    2017 IEEE International Conference on Autonomic Computing (ICAC), 2017
    Co-Authors: Martin Pfannemueller, Markus Weckesser, Christian Krupitzer, Christian Becker
    Abstract:

    Modeling the reasoning component of self-adapting systems including its context is a challenging task. Context feature models used in dynamic software product lines help to capture the capabilities of a software as well as the monitored context values. This enables the possibility to add constraints between the context and system features. In this paper, we present an Adaptation Logic architecture for specifying the knowledge for reasoning in a model-based manner by means of dynamic software product lines. The whole knowledge for reasoning is encapsulated inside a component which enables the reuse of the Adaptation Logic for various application scenarios. Thus, the system designer only has to specify the Adaptation Logic‘s knowledge and implement the according interfaces in the managed resource. We evaluate the Adaptation Logic using our architecture in a distributed computing scenario.

  • ICAC – Adding Self-Improvement to an Autonomic Traffic Management System
    2017 IEEE International Conference on Autonomic Computing (ICAC), 2017
    Co-Authors: Christian Krupitzer, Felix Maximilian Roth, Alexander Frömmgen, Julian Otto, Christian Becker
    Abstract:

    Autonomic Computing and self-adaptive systems are a response to the increasing complexity required to cope with changing environments and varying system resources. However, the complexity of the Adaptation Logic itself increases with the available information in particular for distributed systems. This leads to uncertainty at runtime resulting in incompleteness in the representation of Adaptation goals, models, or rules. Self-improvement which changes the Adaptation Logic at runtime through meta-Adaptation addresses the uncertainty issue.In this paper, we present and discuss a self-improvement case study for an autonomic traffic management system. We adapt parameters of the Adaptation Logic through rule learning as well as the structure of the Adaptation Logic, e.g., from central to decentralized control. We show that the resulting implementation enables continuous self-improvement of the system even in situations that have not been taken into account at design time.

  • PerCom Workshops – RoCoSys: A framework for coordination of mobile IoT devices
    2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017
    Co-Authors: Christian Krupitzer, Christian Becker, Martin Breitbach, Johannes Saal, Michele Segata, Renato Lo Cigno
    Abstract:

    Mobile IoT devices enable new classes of systems, such as cyber-physical systems. These systems pose challenges as they should seamlessly interact with users and other systems. In this paper, we address the problem of interaction between mobile pervasive IoT devices. Our contributions are threefold. First, we present a concept for a framework for coordination of mobile IoT devices. Second, we implement a reusable robot platform using the Mindstorms toolkit and a customizable Adaptation Logic for their coordination based on our framework. Third, we show its usability with two applications: an intelligent vehicle highway system as well as a smart vacuum cleaner.

Felix Maximilian Roth – One of the best experts on this subject based on the ideXlab platform.

  • ICAC – Adding Self-Improvement to an Autonomic Traffic Management System
    2017 IEEE International Conference on Autonomic Computing (ICAC), 2017
    Co-Authors: Christian Krupitzer, Felix Maximilian Roth, Alexander Frömmgen, Julian Otto, Christian Becker
    Abstract:

    Autonomic Computing and self-adaptive systems are a response to the increasing complexity required to cope with changing environments and varying system resources. However, the complexity of the Adaptation Logic itself increases with the available information in particular for distributed systems. This leads to uncertainty at runtime resulting in incompleteness in the representation of Adaptation goals, models, or rules. Self-improvement which changes the Adaptation Logic at runtime through meta-Adaptation addresses the uncertainty issue.In this paper, we present and discuss a self-improvement case study for an autonomic traffic management system. We adapt parameters of the Adaptation Logic through rule learning as well as the structure of the Adaptation Logic, e.g., from central to decentralized control. We show that the resulting implementation enables continuous self-improvement of the system even in situations that have not been taken into account at design time.

  • fesas ide an integrated development environment for autonomic computing
    International Conference on Autonomic Computing, 2016
    Co-Authors: Christian Krupitzer, Felix Maximilian Roth, Christian Becker, Markus Weckesser, Malte Lochau, Andy Schurr
    Abstract:

    While Autonomic Computing can ease the maintenance of systems through Adaptations [1], the development of Autonomic Computing systems itself introduces a high complexity. Literature suggests that reusable processes for the development and reusable components in the Adaptation Logic can reduce the complexity. Existing approaches aim to reduce this complexity with tools and frameworks for specific tasks in the development of the Adaptation Logic of Autonomic Computing systems. However, to the best of our knowledge, none of these approaches offer an Integrated Development Environment (IDE) for it. In this paper, we extend FESAS — our framework for building reusable Adaptation Logic components — with Eclipse plug-ins integrated into the FESAS IDE for a simplified development of MAPE components as well as a process for deployment of the components. In this paper, we present these tools. Further, we evaluate their potential to ease the development of self-adaptive systems within five example cases. Last, we discuss the benefits and limitations of the FESAS IDE.

  • ICAC – FESAS IDE: An Integrated Development Environment for Autonomic Computing
    2016 IEEE International Conference on Autonomic Computing (ICAC), 2016
    Co-Authors: Christian Krupitzer, Felix Maximilian Roth, Christian Becker, Markus Weckesser, Malte Lochau, Andy Schurr
    Abstract:

    While Autonomic Computing can ease the maintenance of systems through Adaptations [1], the development of Autonomic Computing systems itself introduces a high complexity. Literature suggests that reusable processes for the development and reusable components in the Adaptation Logic can reduce the complexity. Existing approaches aim to reduce this complexity with tools and frameworks for specific tasks in the development of the Adaptation Logic of Autonomic Computing systems. However, to the best of our knowledge, none of these approaches offer an Integrated Development Environment (IDE) for it. In this paper, we extend FESAS — our framework for building reusable Adaptation Logic components — with Eclipse plug-ins integrated into the FESAS IDE for a simplified development of MAPE components as well as a process for deployment of the components. In this paper, we present these tools. Further, we evaluate their potential to ease the development of self-adaptive systems within five example cases. Last, we discuss the benefits and limitations of the FESAS IDE.

Hongkai Xiong – One of the best experts on this subject based on the ideXlab platform.

  • deep reinforcement learning based rate Adaptation for adaptive 360 degree video streaming
    International Conference on Acoustics Speech and Signal Processing, 2019
    Co-Authors: Kexin Tang, Chenglin Li, Hongkai Xiong
    Abstract:

    In this paper, we propose a deep reinforcement learning (DRL)-based rate Adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to reduce the possible switching latency of the field of view (FoV), we design a new QoE metric by introducing a penalty term for the large buffer occupancy. A scalable FoV method is further proposed to alleviate the combinatorial explosion of the action space in the DRL formulation. Then, we model the rate Adaptation Logic as a Markov decision process and employ the DRL-based algorithm to dynamically learn the optimal video transmission rate. Simulation results show the superior performance of the proposed algorithm compared to the existing algorithms.

  • ICASSP – Deep Reinforcement Learning-based Rate Adaptation for Adaptive 360-Degree Video Streaming
    ICASSP 2019 – 2019 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2019
    Co-Authors: Nuowen Kan, Kexin Tang, Junni Zou, Ning Liu, Hongkai Xiong
    Abstract:

    In this paper, we propose a deep reinforcement learning (DRL)-based rate Adaptation algorithm for adaptive 360-degree video streaming, which is able to maximize the quality of experience of viewers by adapting the transmitted video quality to the time-varying network conditions. Specifically, to reduce the possible switching latency of the field of view (FoV), we design a new QoE metric by introducing a penalty term for the large buffer occupancy. A scalable FoV method is further proposed to alleviate the combinatorial explosion of the action space in the DRL formulation. Then, we model the rate Adaptation Logic as a Markov decision process and employ the DRL-based algorithm to dynamically learn the optimal video transmission rate. Simulation results show the superior performance of the proposed algorithm compared to the existing algorithms.

Michael Seufert – One of the best experts on this subject based on the ideXlab platform.

  • NOMS – Optimizing HAS for 360-Degree Videos
    NOMS 2020 – 2020 IEEE IFIP Network Operations and Management Symposium, 2020
    Co-Authors: Christian Moldovan, Michael Seufert, Frank Loh, Tobias Hossfeld
    Abstract:

    In recent years, an increasing number of Internet-based applications have been released that use virtual reality for education, training, gaming, and various forms of entertainment. When transmitting an omnidirectional 360° video over the Inter-net, it consumes considerably more data compared to traditional video streaming. To overcome the high network requirements and still reach a high Quality of Experience, HTTP adaptive streaming technology is considered for 360° video streaming. In contrast to traditional streaming, the Adaptation Logic considers not only the current network conditions, but also the viewport of the user, i.e., which part of the 360° sphere the user is currently focusing on. However, as the viewport of the user might change anytime, the Adaptation Logic is required to accurately predict the viewport in the next seconds of the playback to allow for an efficient and smooth streaming with a high visual quality.In this paper, we present novel linear programs that determine the optimal visual quality, which is reachable in a given network scenario using different approaches for viewport prediction. Our results are an important contribution for designing Adaptation Logics for 360° video streaming, which allow for efficient data transmission in the network while reaching a high QoE in VR applications.

  • A Fair Share for All: TCP-Inspired Adaptation Logic for QoE Fairness Among Heterogeneous HTTP Adaptive Video Streaming Clients
    IEEE Transactions on Network and Service Management, 2019
    Co-Authors: Michael Seufert, Nikolas Wehner, Pedro Casas
    Abstract:

    This paper presents a novel Adaptation Logic for HTTP adaptive streaming (HAS), which achieves not only a high quality of experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known transmission contcontrol protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this paper, the design of the TCP-inspired Adaptation Logic (TCPAL) is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS Adaptation Logics. TCPAL is evaluated both in scenarios with stable and fluctuating streaming capacity, and the impact of its parameters is explored. The results suggest that TCPAL performs on par with other HAS Adaptation Logics in terms of QoE and QoE fairness for low link capacities, but significantly improves the QoE fairness for increased link capacity. Moreover, the fairness achieved by TCPAL does not degrade in situations with fluctuating streaming capacity.

  • a fair share for all novel Adaptation Logic for qoe fairness of http adaptive video streaming
    Conference on Network and Service Management, 2018
    Co-Authors: Michael Seufert, Pedro Casas, Nikolas Wehner, Florian Wamser
    Abstract:

    This paper presents a novel Adaptation Logic for HTTP adaptive streaming (HAS), which achieves not only a high Quality of Experience (QoE) but also high QoE fairness among independent and heterogeneous clients. The algorithm forces video clients to adapt the requested quality level based on the current network conditions and their individual bit rate requirements, such that the overall quality levels selected by all currently active streaming clients are fairly distributed, i.e., they do not diverge too much. The design of the algorithm is inspired by the well-known Transmission ContControl Protocol (TCP) congestion control, and drives heterogeneous clients to independently converge on similar quality levels without the need for communicating with each other and/or with a centralized controller in the network. By defining quality levels with equal visual quality, and preparing video representations accordingly, the quality level fairness is extended to QoE fairness. In this work, the design of the algorithm is described and a simulative performance evaluation is conducted to compare the QoE and QoE fairness of the proposed algorithm with other HAS Adaptation Logics.