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- Gheibi, Omid, et al.
(författare)
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Lifelong Self-Adaptation : Self-Adaptation Meets Lifelong Machine Learning
- 2022
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Ingår i: Proceedings - 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. - New York, NY, USA : ACM Press. - 9781450393058 ; , s. 1-12
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Konferensbidrag (refereegranskat)abstract
- In the past years, machine learning (ML) has become a popular approach to support self-Adaptation. While ML techniques enable dealing with several problems in self-Adaptation, such as scalable decision-making, they are also subject to inherent challenges. In this paper, we focus on one such challenge that is particularly important for self-Adaptation: ML techniques are designed to deal with a set of predefined tasks associated with an operational domain; they have problems to deal with new emerging tasks, such as concept shift in input data that is used for learning. To tackle this challenge, we present lifelong self-Adaptation: A novel approach to self-Adaptation that enhances self-Adaptive systems that use ML techniques with a lifelong ML layer. The lifelong ML layer tracks the running system and its environment, associates this knowledge with the current tasks, identifies new tasks based on differentiations, and updates the learning models of the self-Adaptive system accordingly. We present a reusable architecture for lifelong self-Adaptation and apply it to the case of concept drift caused by unforeseen changes of the input data of a learning model that is used for decision-making in self-Adaptation. We validate lifelong self-Adaptation for two types of concept drift using two cases.
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| 2. |
- Quin, Federico, et al.
(författare)
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SEAByTE : A Self-Adaptive Micro-service System Artifact for Automating A/B Testing
- 2022
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Ingår i: Proceedings - 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. - New York, NY, USA : ACM Press. - 9781450393058 ; , s. 77-83
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Konferensbidrag (refereegranskat)abstract
- Micro-services are a common architectural approach to software development today. An indispensable tool for evolving micro-service systems is A/B testing. In A/B testing, two variants, A and B, are applied in an experimental setting. By measuring the outcome of an evaluation criterion, developers can make evidence-based decisions to guide the evolution of their software. Recent studies highlight the need for enhancing the automation when such experiments are conducted in iterations. To that end, we contribute a novel artifact that aims at enhancing the automation of an experimentation pipeline of a micro-service system relying on the principles of self-Adaptation. Concretely, we propose SEAByTE, an experimental framework for testing novel self-Adaptation solutions to enhance the automation of continuous A/B testing of a micro-service based system. We illustrate the use of the SEAByTE artifact with a concrete example.
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| 3. |
- Weyns, Danny, et al.
(författare)
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Preliminary Results of a Survey on the Use of Self-Adaptation in Industry
- 2022
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Ingår i: Proceedings - 17th Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2022. - New York, NY, USA : IEEE. - 9781450393058 ; , s. 70-76
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Konferensbidrag (refereegranskat)abstract
- Self-Adaptation equips a software system with a feedback loop that automates tasks that otherwise need to be performed by operators. Such feedback loops have found their way to a variety of practical applications, one typical example is an elastic cloud. Yet, the state of the practice in self-Adaptation is currently not clear. To get insights into the use of self-Adaptation in practice, we are running a largescale survey with industry. This paper reports preliminary results based on survey data that we obtained from 113 practitioners spread over 16 countries, 62 of them work with concrete self-Adaptive systems. We highlight the main insights obtained so far: motivations for self-Adaptation, concrete use cases, and difficulties encountered when applying self-Adaptation in practice. We conclude the paper with outlining our plans for the remainder of the study. © 2022 ACM.
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