Learning a Dynamic Re-combination Strategy of Forecast Techniques at Runtime

Sommer, Matthias; Tomforde, Sven; Hähner, Jörg

doi:10.1109/icac.2015.70

Cited by 11 publications

(4 citation statements)

References 17 publications

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“…This learning problem is about supporting a managing system with reducing a large number of adaptation options (large adaptation space) such that the system can make more efficient decisions. For instance, [83] and [73] use learners to predict quality properties of adaptation options to select options, speeding up analysis.…”

Section: Reduce Large Adaptation Spacementioning

confidence: 99%

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Gheibi,

Weyns,

Quin

2021

Preprint

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Recently, we witness a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analysing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such overview is important for researchers to understand the state of the art and direct future research efforts. This paper reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional MAPE-based feedback loop (Monitor-Analyze-Plan-Execute). The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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Section: Reduce Large Adaptation Spacementioning

confidence: 99%

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Gheibi,

Weyns,

Quin

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…This learning problem is about supporting a managing system with reducing a large number of adaptation options (large adaptation space) such that the system can make more efficient decisions. For instance, [85] and [75] use learners to predict quality properties of adaptation options to select options, speeding up analysis.…”

Section: Reduce Large Adaptation Spacementioning

confidence: 99%

Applying Machine Learning in Self-adaptive Systems

Gheibi

Weyns

Quin

2020

ACM Trans. Auton. Adapt. Syst.

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Recently, we have been witnessing a rapid increase in the use of machine learning techniques in self-adaptive systems. Machine learning has been used for a variety of reasons, ranging from learning a model of the environment of a system during operation to filtering large sets of possible configurations before analyzing them. While a body of work on the use of machine learning in self-adaptive systems exists, there is currently no systematic overview of this area. Such an overview is important for researchers to understand the state of the art and direct future research efforts. This article reports the results of a systematic literature review that aims at providing such an overview. We focus on self-adaptive systems that are based on a traditional Monitor-Analyze-Plan-Execute (MAPE)-based feedback loop. The research questions are centered on the problems that motivate the use of machine learning in self-adaptive systems, the key engineering aspects of learning in self-adaptation, and open challenges in this area. The search resulted in 6,709 papers, of which 109 were retained for data collection. Analysis of the collected data shows that machine learning is mostly used for updating adaptation rules and policies to improve system qualities, and managing resources to better balance qualities and resources. These problems are primarily solved using supervised and interactive learning with classification, regression, and reinforcement learning as the dominant methods. Surprisingly, unsupervised learning that naturally fits automation is only applied in a small number of studies. Key open challenges in this area include the performance of learning, managing the effects of learning, and dealing with more complex types of goals. From the insights derived from this systematic literature review, we outline an initial design process for applying machine learning in self-adaptive systems that are based on MAPE feedback loops.

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“…Recent work (Sommer et al, 2015) focused on the development of a self-optimising forecast module for time series. This module is situated in the Observer at Layer 1.…”

Section: Forecast Modulementioning

confidence: 99%

Forecast-augmented Route Guidance in Urban Traffic Networks based on Infrastructure Observations

Sommer¹,

Tomforde²,

Hähner³

2016

Proceedings of the International Conference on Vehicle Technology and Intelligent Transport Systems

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Increasing mobility and raising traffic demands lead to serious congestion problems. Intelligent traffic management systems try to alleviate this problem with optimised signalisation of traffic lights and dynamic route guidance (DRG). One solution for the former aspect is Organic Traffic Control (OTC), offering a self-organised, decentralised traffic control system. Based on OTC, this paper presents two proactive routing protocols, resembling techniques known from the Internet domain, applied to the traffic routing problem: Distance Vector Routing and Link State Routing. These protocols were adapted to utilise forecasts of traffic flows to offer anticipatory and time-dependant DRG for road users. The efficiency of these protocols is demonstrated with simulations of two Manhattan-type road networks under disturbed and undisturbed conditions. The results indicate their benefit in terms of lower travel times and emissions, even under low compliance rates.

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Learning a Dynamic Re-combination Strategy of Forecast Techniques at Runtime

Cited by 11 publications

References 17 publications

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Applying Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Applying Machine Learning in Self-adaptive Systems

Forecast-augmented Route Guidance in Urban Traffic Networks based on Infrastructure Observations

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