| 1. |
- Afzal, Wasif, et al.
(författare)
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The MegaM@Rt2 ECSEL Project : MegaModelling at Runtime — Scalable Model-Based Framework for Continuous Development and Runtime Validation of Complex Systems
- 2017
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Ingår i: The 2017 Euromicro Conference on Digital System Design DSD'17. - 9781538621462
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Konferensbidrag (refereegranskat)abstract
- A major challenge for the European electronic industry is to enhance productivity while reducing costs and ensuring quality in development, integration and maintenance. Model-Driven Engineering (MDE) principles and techniques have already shown promising capabilities but still need to scale to support real-world scenarios implied by the full deployment and use of complex electronic components and systems. Moreover, maintaining efficient traceability, integration and communication between two fundamental system life-time phases (design time and runtime) is another challenge facing scalability of MDE. This paper presents an overview of the ECSEL project entitled "MegaModelling at runtime -- Scalable model-based framework for continuous development and runtime validation of complex systems" (MegaM@Rt2), whose aim is to address the above mentioned challenges facing MDE. Driven by both large and small industrial enterprises, with the support of research partners and technology providers, MegaM@Rt2 aims to deliver a framework of tools and methods for: 1) system engineering/design & continuous development, 2) related runtime analysis and 3) global model & traceability management, respectively. The diverse industrial use cases (covering domains such as aeronautics, railway, construction and telecommunications) will integrate and apply such a framework that shall demonstrate the validation of the MegaM@Rt2 solution.
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| 2. |
- Niknafs, Mina, et al.
(författare)
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Two-Phase Interarrival Time Prediction for Runtime Resource Management
- 2017
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Ingår i: 2017 EUROMICRO CONFERENCE ON DIGITAL SYSTEM DESIGN (DSD). - : IEEE. - 9781538621462 ; , s. 524-528
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Konferensbidrag (refereegranskat)abstract
- Platforms that are based on heterogeneous architectures require an intelligent resource manager. An intelligent resource manager should be able to accurately predict the future workload of the system at hand and take it into consideration. In this paper, we show that there exist patterns in the interarrival times of resource requests, and that these patterns can be used for modeling and prediction of the future arrivals. To this end, we develop a two-phase machine-learning-based framework and apply it to real data. First, in the offline phase of our framework, the interarrival times are clustered based on a number of extracted features, and then an adequate modeling and prediction method is selected for each detected cluster. It is shown that, due to the intricate and varied nature of interarrival times, a universal modeling and prediction method does not provide optimal results, and a customized method should be applied to each of the detected clusters. Second, in the runtime phase of our framework, the results provided from the offline phase are used to perform computationally cheap prediction. The experimental results show that our approach has a prediction error below 12% and provides an error reduction of more than 17% in comparison with a straightforward method.
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