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Pieskä, M., Rabitsch, A., Brunstrom, A., Kassler, A., Amend, M. & Bogenfeld, E. (2024). Low-delay cost-aware multipath scheduling over dynamic links for access traffic steering, switching, and splitting. Computer Networks, 241, Article ID 110186.
Open this publication in new window or tab >>Low-delay cost-aware multipath scheduling over dynamic links for access traffic steering, switching, and splitting
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2024 (English)In: Computer Networks, ISSN 1389-1286, E-ISSN 1872-7069, Vol. 241, article id 110186Article in journal (Refereed) Published
Abstract [en]

Bundling of multiple access technologies is currently being standardized by 3GPP in the 5G access traffic steering, switching and splitting (ATSSS) framework, with the goal to increase robustness, resiliency and capacity of wireless access. A key part of an ATSSS framework is the packet scheduler, which decides the access network over which each packet is to be transmitted. As wireless channels are highly dynamic, a challenge for any scheduler is to correctly estimate the capacity of each path, and thereby avoid congesting the paths. In this paper, we further develop a recent packet scheduler that exploits cross-layer information from the congestion control state of individual transport layer tunnels when making scheduling decisions. Our aim is to achieve good path utilization while keeping the congestion delay low. Extensive emulations show that our approach reduces the excess delay at the bottleneck to as little as 34%. We furthermore show that our approach improves the performance of end-to-end applications including WebRTC and YouTube compared to state-of-the art. 

Place, publisher, year, edition, pages
Elsevier, 2024
Keywords
Scheduling algorithms, Traffic congestion, 5g, Access traffic steering, switching and splitting, Heterogeneous wireless access, MP-DCCP, Multi-path transport layer tunneling, Multipath, Packet scheduling, Splittings, Transport layers, Unreliable traffic, Wireless access, 5G mobile communication systems
National Category
Communication Systems Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98641 (URN)10.1016/j.comnet.2024.110186 (DOI)2-s2.0-85183909966 (Scopus ID)
Funder
Knowledge Foundation, Dnr 20220072
Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-02-27Bibliographically approved
Chahed, H., Usman, M., Chatterjee, A., Bayram, F., Chaudhary, R., Brunstrom, A., . . . Kassler, A. (2023). AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications. Internet of Things: Engineering Cyber Physical Human Systems, 22, Article ID 100805.
Open this publication in new window or tab >>AIDA—Aholistic AI-driven networking and processing framework for industrial IoT applications
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2023 (English)In: Internet of Things: Engineering Cyber Physical Human Systems, E-ISSN 2542-6605, Vol. 22, article id 100805Article in journal (Refereed) Published
Abstract [en]

Industry 4.0 is characterized by digitalized production facilities, where a large volume of sensors collect a vast amount of data that is used to increase the sustainability of the production by e.g. optimizing process parameters, reducing machine downtime and material waste, and the like. However, making intelligent data-driven decisions under timeliness constraints requires the integration of time-sensitive networks with reliable data ingestion and processing infrastructure with plug-in support of Machine Learning (ML) pipelines. However, such integration is difficult due to the lack of frameworks that flexibly integrate and program the networking and computing infrastructures, while allowing ML pipelines to ingest the collected data and make trustworthy decisions in real time. In this paper, we present AIDA - a novel holistic AI-driven network and processing framework for reliable data-driven real-time industrial IoT applications. AIDA manages and configures Time-Sensitive networks (TSN) to enable real-time data ingestion into an observable AI-powered edge/cloud continuum. Pluggable and trustworthy ML components that make timely decisions for various industrial IoT applications and the infrastructure itself are an intrinsic part of AIDA. We introduce the AIDA architecture, demonstrate the building blocks of our framework and illustrate it with two use cases. 

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Edge/cloud computing, Internet of Things (IoT), Machine Learning, Time-Sensitive Networks (TSN)
National Category
Computer Engineering Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-94900 (URN)10.1016/j.iot.2023.100805 (DOI)001053228900001 ()2-s2.0-85159450974 (Scopus ID)
Funder
Knowledge Foundation, 20200067
Available from: 2023-05-29 Created: 2023-05-29 Last updated: 2024-02-07Bibliographically approved
Ma, Y., Younis, K., Ahmed, B. S., Kassler, A., Krakhmalev, P., Thore, A. & Lindback, H. (2023). Automated and Systematic Digital Twins Testing for Industrial Processes. In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023: . Paper presented at 16th IEEE International Conference on Software Testing, Verification and Validation Workshops, Dublin,Ireland, April 16-20, 2023. (pp. 149-158). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Automated and Systematic Digital Twins Testing for Industrial Processes
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2023 (English)In: Proceedings - 2023 IEEE 16th International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2023, Institute of Electrical and Electronics Engineers (IEEE), 2023, p. 149-158Conference paper, Published paper (Refereed)
Abstract [en]

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. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Automation, E-learning, Industry 4.0, Reinforcement learning, Software reliability, Industrial processs, Machine-learning, Modelling capabilities, Physical behaviors, Physical process, Physical world, Production automation, Reinforcement learnings, Simulation and modeling, Software testings, Software testing
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-96056 (URN)10.1109/ICSTW58534.2023.00037 (DOI)2-s2.0-85163093915 (Scopus ID)979-8-3503-3335-0 (ISBN)
Conference
16th IEEE International Conference on Software Testing, Verification and Validation Workshops, Dublin,Ireland, April 16-20, 2023.
Funder
Knowledge FoundationVinnova
Available from: 2023-07-07 Created: 2023-07-07 Last updated: 2023-08-07Bibliographically approved
Bayram, F., Aupke, P., Ahmed, B. S., Kassler, A., Theocharis, A. & Forsman, J. (2023). DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks. Engineering applications of artificial intelligence, 123, Article ID 106480.
Open this publication in new window or tab >>DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
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2023 (English)In: Engineering applications of artificial intelligence, ISSN 0952-1976, E-ISSN 1873-6769, Vol. 123, article id 106480Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Concept drift Change-point detection Dynamic drift adaptation Adaptive LSTM Interval load forecasting
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-95022 (URN)10.1016/j.engappai.2023.106480 (DOI)001013639800001 ()2-s2.0-85160615665 (Scopus ID)
Funder
Knowledge Foundation, 20200067Swedish Energy Agency, 50246-1; 52693-1
Available from: 2023-06-02 Created: 2023-06-02 Last updated: 2023-07-06Bibliographically approved
Alizadeh Noghani, K., Kassler, A., Taheri, J., Ohlen, P. & Curescu, C. (2023). Multi-Objective genetic algorithm for fast service function chain reconfiguration. IEEE Transactions on Network and Service Management, 20(3), 3501-3522
Open this publication in new window or tab >>Multi-Objective genetic algorithm for fast service function chain reconfiguration
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2023 (English)In: IEEE Transactions on Network and Service Management, ISSN 1932-4537, E-ISSN 1932-4537, Vol. 20, no 3, p. 3501-3522Article in journal (Refereed) Published
Abstract [en]

The optimal placement of virtual network functions (VNFs) improves the overall performance of servicefunction chains (SFCs) and decreases the operational costs formobile network operators. To cope with changes in demands,VNF instances may be added or removed dynamically, resourceallocations may be adjusted, and servers may be consolidated.To maintain an optimal placement of SFCs when conditionschange, SFC reconfiguration is required, including the migration of VNFs and the rerouting of service-flows. However, suchreconfigurations may lead to stress on the VNF infrastructure,which may cause service degradation. On the other hand, notchanging the placement may lead to suboptimal operation,and servers and links may become congested or underutilized,leading to high operational costs. In this paper, we investigatethe trade-off between the reconfiguration of SFCs and theoptimality of their new placement and service-flow routing. Wedevelop a multi-objective genetic algorithm that explores thePareto front by balancing the optimality of the new placementand the cost to achieve it. Our numerical evaluations show thata small number of reconfigurations can significantly reduce theoperational cost of the VNF infrastructure. In contrast, toomuch reconfiguration may not pay off due to high costs. Webelieve that our work provides an important tool that helpsnetwork providers to plan a good reconfiguration strategy fortheir service chains.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Cloud computing, Containers, Cost engineering, Transfer functions, Cloud-computing, Migration strategy, Multi-objectives genetic algorithms, Network functions, Networks reconfiguration, Optimisations, Resource management; Virtual network function, Virtual networks, VNF migration strategy, Genetic algorithms
National Category
Communication Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-91583 (URN)10.1109/TNSM.2022.3195820 (DOI)001142524900006 ()2-s2.0-85135744199 (Scopus ID)
Funder
Knowledge Foundation
Available from: 2022-08-24 Created: 2022-08-24 Last updated: 2024-02-16Bibliographically approved
Gokan Khan, M., Taheri, J., Al-Dulaimy, A. & Kassler, A. (2023). PerfSim: A Performance Simulator for Cloud Native Microservice Chains. IEEE Transactions on Cloud Computing (2), 1395-1413
Open this publication in new window or tab >>PerfSim: A Performance Simulator for Cloud Native Microservice Chains
2023 (English)In: IEEE Transactions on Cloud Computing, ISSN 2168-7161, no 2, p. 1395-1413Article in journal (Refereed) Published
Abstract [en]

Cloud native computing paradigm allows microservice-based applications to take advantage of cloud infrastructure in a scalable, reusable, and interoperable way. However, in a cloud native system, the vast number of configuration parameters and highly granular resource allocation policies can significantly impact the performance and deployment cost of such applications. For understanding and analyzing these implications in an easy, quick, and cost-effective way, we present PerfSim, a discrete-event simulator for approximating and predicting the performance of cloud native service chains in user-defined scenarios. To this end, we proposed a systematic approach for modeling the performance of microservices endpoint functions by collecting and analyzing their performance and network traces. With a combination of the extracted models and user-defined scenarios, PerfSim can simulate the performance behavior of service chains over a given period and provides an approximation for system KPIs, such as requests' average response time. Using the processing power of a single laptop, we evaluated both simulation accuracy and speed of PerfSim in 104 prevalent scenarios and compared the simulation results with the identical deployment in a real Kubernetes cluster. We achieved ~81-99% simulation accuracy in approximating the average response time of incoming requests and ~16-1200 times speed-up factor for the simulation.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
performance simulator, performance modeling, cloud native computing, service chains, simulation platform
National Category
Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-83686 (URN)10.1109/TCC.2021.3135757 (DOI)001004238600023 ()2-s2.0-85121842188 (Scopus ID)
Funder
Knowledge Foundation, 20200067
Note

Article published as manuscript entitled "PerfSim: A Performance Simulator for Cloud Native Computing" in Gokan Khan's (2021) licentiate thesis: Performance Modelling and Simulation of Service Chains for Telecom Clouds

Available from: 2021-04-16 Created: 2021-04-16 Last updated: 2023-11-14Bibliographically approved
Aupke, P., Seema, ., Theocharis, A., Kassler, A. & Archer, D.-E. (2023). PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids. In: 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe): . Paper presented at 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, 6-9 June 2023.. IEEE
Open this publication in new window or tab >>PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids
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2023 (English)In: 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), IEEE, 2023Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE, 2023
Keywords
Machine Learning, Smart Energy Grids, Uncertainty Bounds, Digital storage, Forecasting, Smart power grids, Uncertainty analysis, Energy exchanges, Energy grids, Machine learning models, Machine-learning, Photovoltaic power, Power production, Production and consumption, Smart energies, Smart energy grid
National Category
Energy Systems Energy Engineering
Research subject
Electrical Engineering; Computer Science
Identifiers
urn:nbn:se:kau:diva-96594 (URN)10.1109/EEEIC/ICPSEurope57605.2023.10194894 (DOI)2-s2.0-85168697748 (Scopus ID)979-8-3503-4743-2 (ISBN)979-8-3503-4744-9 (ISBN)
Conference
2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, 6-9 June 2023.
Available from: 2023-09-04 Created: 2023-09-04 Last updated: 2024-02-07Bibliographically approved
Nammouchi, A., Aupke, P., D’Andreagiovanni, F., Ghazzai, H., Theocharis, A. & Kassler, A. (2023). Robust opportunistic optimal energy management of a mixed microgrid under asymmetrical uncertainties. Sustainable Energy, Grids and Networks, 36, Article ID 101184.
Open this publication in new window or tab >>Robust opportunistic optimal energy management of a mixed microgrid under asymmetrical uncertainties
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2023 (English)In: Sustainable Energy, Grids and Networks, E-ISSN 2352-4677, Vol. 36, article id 101184Article in journal (Refereed) Published
Abstract [en]

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

Place, publisher, year, edition, pages
Elsevier, 2023
Keywords
Decision making, Economics, Forecasting, Machine learning, Optimization, Smart power grids, System stability, Energy exchanges, Machine learning and AI, Machine-learning, Microgrid, Optimality, Renewable energies, Robust energy, Robust optimization, Smart grid, Uncertainty, Energy management
National Category
Energy Systems Energy Engineering Control Engineering
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-97466 (URN)10.1016/j.segan.2023.101184 (DOI)001098634800001 ()2-s2.0-85173811342 (Scopus ID)
Funder
Swedish Energy Agency, 50246-1, 52693-1
Available from: 2023-11-22 Created: 2023-11-22 Last updated: 2024-02-07Bibliographically approved
Chahed, H. & Kassler, A. (2023). TSN Network Scheduling—Challenges and Approaches. Network, 3(4), 585-624
Open this publication in new window or tab >>TSN Network Scheduling—Challenges and Approaches
2023 (English)In: Network, E-ISSN 2673-8732, Vol. 3, no 4, p. 585-624Article in journal (Refereed) Published
Abstract [en]

Time-Sensitive Networking (TSN) is a set of Ethernet standards aimed to improve determinism in packet delivery for converged networks. The main goal is to provide mechanisms that enable low and predictable transmission latency and high availability for demanding applications such as real-time audio/video streaming, automotive, and industrial control. To provide the required guarantees, TSN integrates different traffic shaping mechanisms including 802.1Qbv, 802.1Qch, and 802.1Qcr, allowing for the coexistence of different traffic classes with different priorities on the same network. Achieving the required quality of service (QoS) level needs proper selection and configuration of shaping mechanisms, which is difficult due to the diversity in the requirements of the coexisting streams under the presence of potential end-system-induced jitter. This paper discusses the suitability of the TSN traffic shaping mechanisms for the different traffic types, analyzes the TSN network configuration problem, i.e., finds the optimal path and shaper configurations for all TSN elements in the network to provide the required QoS, discusses the goals, constraints, and challenges of time-aware scheduling, and elaborates on the evaluation criteria of both the network-wide schedules and the scheduling algorithms that derive the configurations to present a common ground for comparison between the different approaches. Finally, we analyze the evolution of the scheduling task, identify shortcomings, and suggest future research directions.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
Time-Sensitive Networking (TSN), scheduling, shaping, network configuration, Time-Aware Shaper (TAS), Cyclic Queuing and Forwarding (CQF), asynchronous traffic shaping (ATS)
National Category
Computer and Information Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-98183 (URN)10.3390/network3040026 (DOI)
Projects
AIDA
Funder
Knowledge Foundation
Available from: 2024-01-25 Created: 2024-01-25 Last updated: 2024-02-05Bibliographically approved
Alfredsson, R., Kassler, A., Vestin, J., Pieskä, M. & Amend, M. (2022). Accelerating a Transport Layer based 5G Multi-Access Proxy on SmartNIC. In: Würzburg Workshop on Next-Generation Communication Networks (WueWoWas'22): . Paper presented at Würzburg Workshop on Next-Generation Communication Networks, Würzburg, 11-13 July 2022 (pp. 4). Würzburgs universitet
Open this publication in new window or tab >>Accelerating a Transport Layer based 5G Multi-Access Proxy on SmartNIC
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2022 (English)In: Würzburg Workshop on Next-Generation Communication Networks (WueWoWas'22), Würzburgs universitet , 2022, p. 4-Conference paper, Published paper (Other academic)
Abstract [en]

Utilizing multiple access technologies such as 5G,4G, and Wi-Fi within a coherent framework is currentlystandardized by 3GPP within 5G ATSSS. Indeed, distributingpackets over multiple networks can lead to increased robustness,resiliency and capacity. A key part of such a framework isthe multi-access proxy, which transparently distributes packetsover multiple paths. As the proxy needs to serve thousands ofcustomers, scalability and performance are crucial for operatordeployments. In this paper, we leverage recent advancementsin data plane programming, implement a multi-access proxybased on the MP-DCCP tunneling approach in P4 and hardwareaccelerate it by deploying the pipeline on a smartNIC. Thisis challenging due to the complex scheduling and congestioncontrol operations involved. We present our pipeline and datastructures design for congestion control and packet schedulingstate management. Initial measurements in our testbed showthat packet latency is in the range of 25 μs demonstrating thefeasibility of our approach.

Place, publisher, year, edition, pages
Würzburgs universitet, 2022
Keywords
Multipath, MP-DCCP, 5G-ATSSS, networking, dataplane programming, P4
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-94019 (URN)10.25972/OPUS-28079 (DOI)
Conference
Würzburg Workshop on Next-Generation Communication Networks, Würzburg, 11-13 July 2022
Available from: 2023-03-24 Created: 2023-03-24 Last updated: 2023-04-05Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9446-8143

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