| 1. |
- Alizadeh Noghani, Kyoomars
(författare)
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Service Migration in Virtualized Data Centers
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Modern virtualized Data Centers (DCs) require efficient management techniques to guarantee high quality services while reducing their economical cost. The ability to live migrate virtual instances, e.g., Virtual Machines (VMs), both inside and among DCs is a key operation for the majority of DC management tasks that brings significant flexibility into the DC infrastructure. However, live migration introduces new challenges as it ought to be fast and seamless while at the same time imposing a minimum overhead on the network. In this thesis, we study the networking problems of live service migration in modern DCs when services are deployed in virtualized environments, e.g., VMs and containers. In particular, this thesis has the following main objectives: (1) improving the live VM migration in Software-Defined Network (SDN) enabled DCs by addressing networking challenges of live VM migration, and (2) investigating the trade-off between the reconfiguration cost and optimality of the Service Function Chains (SFCs) placement after the reconfiguration has been applied when SFCs are composed of stateful Virtual Network Functions (VNFs).To achieve the first objective, in this thesis, we use distinctive characteristics of SDN architectures such as their centralized control over the network to accelerate the network convergence time and address suboptimal routing problem. Consequently, we enhance the quality of intra- and inter-DC live migrations. Furthermore, we develop an SDN-based framework to improve the inter-DC live VM migration by automating the deployment, improving the management, enhancing the performance, and increasing the scalability of interconnections among DCs.To accomplish the second objective, we investigate the overhead of dynamic reconfiguration of stateful VNFs. Dynamic reconfiguration of VNFs is frequently required in various circumstances, and live migration of VNFs is an integral part of this operation. By mathematically formulating the reconfiguration costs of stateful VNFs and developing a multi-objective heuristic solution, we explore the trade-off between the reconfiguration cost required to improve a given placement and the degree of optimality achieved after the reconfiguration is performed. Results show that the cost of performing the reconfiguration operations required to realize an optimal VNF placement might hamper the gain that could be achieved.
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| 2. |
- Aupke, Phil, et al.
(författare)
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Impact of Clustering Methods on Machine Learning-based Solar Power Prediction Models
- 2022
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Ingår i: 2022 IEEE International Smart Cities Conference (ISC2). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781665485616
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Konferensbidrag (refereegranskat)abstract
- Prediction of solar power generation is important in order to optimize energy exchanges in future micro-grids that integrate a large amount of photovoltaics. However, an accurate prediction is difficult due to the uncertainty of weather phenomena that impact produced power. In this paper, we evaluate the impact of different clustering methods on the forecast accuracy for predicting hourly ahead solar power when using machine learning based prediction approaches trained on weather and generated power features. In particular, we compare clustering methods using clearness index and K-means clustering, where we use both euclidian distance and dynamic time-warping. For evaluating prediction accuracy, we develop and compare different prediction models for each of the clusters using production data from a swedish SmartGrid. We demonstrate that proper tuning of thresholds for the clearness index improves prediction accuracy by 20.19% but results in worse performance than using K-means with all weather features as input to the clustering.
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| 3. |
- Aupke, Phil, et al.
(författare)
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PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids
- 2023
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Ingår i: 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe). - : IEEE. - 9798350347432 - 9798350347449
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Konferensbidrag (refereegranskat)abstract
- For efficient energy exchanges in smart energy grids under the presence of renewables, predictions of energy production and consumption are required. For robust energy scheduling, prediction of uncertainty bounds of Photovoltaic (PV) power production and consumption is essential. In this paper, we apply several Machine Learning (ML) models that can predict the power generation of PV and consumption of households in a smart energy grid, while also assessing the uncertainty of their predictions by providing quantile values as uncertainty bounds. We evaluate our algorithms on a dataset from Swedish households having PV installations and battery storage. Our findings reveal that a Mean Absolute Error (MAE) of 16.12W for power production and 16.34W for consumption for a residential installation can be achieved with uncertainty bounds having quantile loss values below 5W. Furthermore, we show that the accuracy of the ML models can be affected by the characteristics of the household being studied. Different households may have different data distributions, which can cause prediction models to perform poorly when applied to untrained households. However, our study found that models built directly for individual homes, even when trained with smaller datasets, offer the best outcomes. This suggests that the development of personalized ML models may be a promising avenue for improving the accuracy of predictions in the future.
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| 4. |
- Bayram, Firas, et al.
(författare)
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DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
- 2023
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Ingår i: Engineering applications of artificial intelligence. - : Elsevier. - 0952-1976 .- 1873-6769. ; 123
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Tidskriftsartikel (refereegranskat)abstract
- Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.
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| 5. |
- Brunström, Anna, et al.
(författare)
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NEWCOM++ DR11.3: Final report on the activities and results of WPR11
- 2010
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- This document is the last deliverable of WPR.11 and presents an overview of the final activities carried out within the NEWCOM++ Workpackage WPR.11 during the last 18 months. We provide a description of the most consolidated Joint Research Activities (JRAs) and the main results so far obtained. We also address some considerations on the future activities which are expected to continue at the end of NEWCOM++
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| 6. |
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| 7. |
- Ma, Yunpeng, et al.
(författare)
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Automated and Systematic Digital Twins Testing for Industrial Processes
- 2023
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Ingår i: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023. - : Institute of Electrical and Electronics Engineers Inc.. - 9798350333350 ; , s. 149-158
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Konferensbidrag (refereegranskat)abstract
- Digital twins (DT) of industrial processes have become increasingly important. They aim to digitally represent the physical world to help evaluate, optimize, and predict physical processes and behaviors. Therefore, DT is a vital tool to improve production automation through digitalization and becomes more sophisticated due to rapidly evolving simulation and modeling capabilities, integration of IoT sensors with DT, and high-capacity cloud/edge computing infrastructure. However, the fidelity and reliability of DT software are essential to represent the physical world. This paper shows an automated and systematic test architecture for DT that correlates DT states with real-time sensor data from a production line in the forging industry. Our evaluation shows that the architecture can significantly accelerate the automatic DT testing process and improve its reliability. A systematic online DT testing method can significantly detect the performance shift and continuously improve the DT's fidelity. The snapshot creation methodology and testing agent architecture can be an inspiration and can be generally applicable to other industrial processes that use DT to generalize their automated testing.
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| 8. |
- Ma, Yunpeng, et al.
(författare)
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Using Deep Reinforcement Learning for Zero Defect Smart Forging
- 2022
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Ingår i: Advances in Transdisciplinary Engineering. - : IOS Press. - 9781643682686 - 9781643682693 ; , s. 701-712
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Konferensbidrag (refereegranskat)abstract
- Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces revenue and negatively impacts sustainability and the environment. An essential reason for material waste is a low degree of automation, especially in industries that currently have a low degree of digitalization, such as steel forging. Those industries typically rely on heavy and old machinery such as large induction ovens that are mostly controlled manually or using well-known recipes created by experts. However, standard recipes may fail when unforeseen events happen, such as an unplanned stop in production, which may lead to overheating and thus material degradation during the forging process. In this paper, we develop a digital twin-based optimization strategy for the heating process for a forging line to automate the development of an optimal control policy that adjusts the power for the heating coils in an induction oven based on temperature data observed from pyrometers. We design a digital twin-based deep reinforcement learning (DTRL) framework and train two different deep reinforcement learning (DRL) models for the heating phase using a digital twin of the forging line. The twin is based on a simulator that contains a heating transfer and movement model, which is used as an environment for the DRL training. Our evaluation shows that both models significantly reduce the temperature unevenness and can help to automate the traditional heating process.
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| 9. |
- Nammouchi, Amal, et al.
(författare)
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Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management
- 2021
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Ingår i: 2021 21St Ieee International Conference On Environment And Electrical Engineering And 2021 5Th Ieee Industrial And Commercial Power Systems Europe (Eeeic/I&Cps Europe). - : IEEE. - 9781665436137
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Konferensbidrag (refereegranskat)abstract
- Towards zero CO2 emissions society, large shares of renewable energy sources and storage systems are integrated into microgrids as part of the electrical grids for energy exchange aiming to effectively reduce the stress from the transmission grid. However, energy management within and across microgrids is complicated due to many uncertainties such as imprecise knowledge on energy production and demand, which makes energy optimization challenging. In this paper, we present an open architecture that uses machine learning algorithms at the edge to predict energy consumption and production for energy management in smart microgrids. Such predictions are aggregated across different prosumers at a centralized marketplace in the Cloud using Kafka Streams and OpenSource IoT platforms. Using pluggable optimization algorithms, different microgrids can implement different strategies for real-time optimal energy schedules. The proposed architecture is evaluated in terms of scalability and accuracy of predictions. Our heuristics can effectively optimize medium-sized microgrids.
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