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Seema, S., Theocharis, A. & Kassler, A. (2024). Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset. Karlstads universitet
Open this publication in new window or tab >>Evaluate Temporal and Spatio-Temporal Correlations for Different Prosumers Using Solar Power Generation Time Series Dataset
2024 (English)Report (Other academic)
Abstract [en]

This study investigates the temporal and spatio-temporal correlations of solar power generation among different prosumers of Uppsala and Halmstad, Sweden. Using solar power generation data from seven prosumer in Uppsala and five in Halmstad, we evaluate the correlation of solar power production generation at specific locations correlates with itself over different time lags (autocorrelation).  In addition, we examine the spatiotemporal correlations of solar power production at various locations over a range of lags using time shifted cross correlation. These spatio-temporal correlations can facilitate the development of synchronized demand response strategies and dynamic energy pricing. Moreover, the time-shifted cross-correlation analysis assists in improving forecasting models for solar power generation. By identifying significant correlations between solar generation data from different locations and applying time shifts to account for variations in weather and sunlight exposure, operators can enhance the accuracy of their predictions. This methodology enables them to fill in missing data points by leveraging correlated information from neighboring regions. Consequently, more robust forecasting models can be developed, leading to better resource allocation, improved energy management, and reduced operational uncertainties in the grid. This research highlights the evaluation and potential of utilizing spatio-temporal and temporal correlations in solar power data to enhance energy management and planning.

Place, publisher, year, edition, pages
Karlstads universitet, 2024. p. 16
Series
Karlstad University Studies, ISSN 1403-8099 ; 2024:20
Keywords
spatial-temporal correlation, auto-correlation, photovoltaic, time shifted cross correlation
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
urn:nbn:se:kau:diva-100707 (URN)10.59217/yjll7238 (DOI)978-91-7867-473-2 (ISBN)
Available from: 2024-06-26 Created: 2024-06-26 Last updated: 2024-07-03Bibliographically approved
Gokan Khan, M., Taheri, J., Kassler, A. & Boodaghian Asl, A. (2024). Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning Approach. In: Valenti M., Reed D., Torres M. (Ed.), Proceedings- IEEE International Conference on Communications: . Paper presented at IEEE International Conference on Communications (ICC), Denver, USA, June 9-13, 2024. (pp. 2913-2918). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning Approach
2024 (English)In: Proceedings- IEEE International Conference on Communications / [ed] Valenti M., Reed D., Torres M., Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 2913-2918Conference paper, Published paper (Refereed)
Abstract [en]

In the realm of cloud native environments, Ku-bernetes has emerged as the de facto orchestration system for containers, and the service mesh architecture, with its interconnected microservices, has become increasingly prominent. Efficient scheduling and resource allocation for these microservices play a pivotal role in achieving high performance and maintaining system reliability. In this paper, we introduce a novel approach for container scheduling within Kubernetes clusters, leveraging Graph Attention Networks (GATs) for representation learning. Our proposed method captures the intricate dependencies among containers and services by constructing a representation graph. The deep Q-learning algorithm is then employed to optimize scheduling decisions, focusing on container-to-node placements, CPU request-response allocation, and adherence to node affinity and anti-affinity rules. Our experiments demonstrate that our GATs-based method outperforms traditional scheduling strategies, leading to enhanced resource utilization, reduced service latency, and improved overall system throughput. The insights gleaned from this study pave the way for a new frontier in cloud native performance optimization and offer tangible benefits to industries adopting microservice-based architectures.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
component, formatting, insert, style, styling
National Category
Computer Sciences Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-97430 (URN)10.1109/ICC51166.2024.10622616 (DOI)2-s2.0-85202817543 (Scopus ID)978-1-7281-9055-6 (ISBN)978-1-7281-9054-9 (ISBN)
Conference
IEEE International Conference on Communications (ICC), Denver, USA, June 9-13, 2024.
Note

This article was included as a manuscript in the doctoral thesis entitled "Unchaining Microservice Chains: Machine Learning Driven Optimization in Cloud Native Systems" KUS 2023:35.

Available from: 2023-11-20 Created: 2023-11-20 Last updated: 2024-10-07Bibliographically approved
Chahed, H., Hallstrom, F., Alcaine, H. B. & Kassler, A. (2024). Linux-Based End-Station Design for Seamless TSN Plug-And-Play. In: Abdessattar Ben, Amor Safa Bhar Elayeb, Chekib Ghorbel, Salwa Elloumi, Khaled Nouri, Mohamed Saber Naceur, Omar Khouadja (Ed.), Proceedings - IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2024: . Paper presented at International Conference on Advanced Systems and Emergent Technologies, IC_ASET,Hammamet, Tunisia, April 27-29, 2024.. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Linux-Based End-Station Design for Seamless TSN Plug-And-Play
2024 (English)In: Proceedings - IEEE International Conference on Advanced Systems and Emergent Technologies, IC_ASET 2024 / [ed] Abdessattar Ben, Amor Safa Bhar Elayeb, Chekib Ghorbel, Salwa Elloumi, Khaled Nouri, Mohamed Saber Naceur, Omar Khouadja, Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]

In TSN networks, proper end-station configuration is essential to ensure the timely and reliable delivery of time-sensitive data, meeting strict end-To-end Quality of Service (QoS) criteria. However, the complexity of the configuration process requires a significant manual effort, which makes real-Time application development on standard Operating Systems such as Linux a challenge. In this paper, we propose a sim-ple yet functional approach to automate the configuration of Linux-based TSN end-stations within TSN networks by adding a TSN layer on top of the networking system services and defining a configuration protocol tailored for the centralized network/distributed user configuration mode. Evaluation results demonstrate minimal overhead during stream addition, achieving hundreds-of-millisecond-Ievel configuration times and enabling a hassle-free Plug-And-Play mode of operation. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Quality of service, Real time systems, Sensitive data, Configuration management, Configuration process, End stations, End-to-end quality of service, Functional approach, Plug-and-play, Real time application development, Reliable delivery, Sensitive datas, TSN, Linux
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-101419 (URN)10.1109/IC_ASET61847.2024.10596212 (DOI)2-s2.0-85200538803 (Scopus ID)979-8-3503-8490-1 (ISBN)979-8-3503-8489-5 (ISBN)
Conference
International Conference on Advanced Systems and Emergent Technologies, IC_ASET,Hammamet, Tunisia, April 27-29, 2024.
Funder
Knowledge Foundation
Available from: 2024-08-23 Created: 2024-08-23 Last updated: 2024-08-23Bibliographically approved
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)001173486200001 ()2-s2.0-85183909966 (Scopus ID)
Funder
Knowledge Foundation, Dnr 20220072
Available from: 2024-02-27 Created: 2024-02-27 Last updated: 2024-05-10Bibliographically approved
Nammouchi, A., Cuadrado, N., Ramaswamy, A. & Kassler, A. (2024). Multi-Objective Microgrid Control Using Deep Reinforcement Learning. Karlstads universitet
Open this publication in new window or tab >>Multi-Objective Microgrid Control Using Deep Reinforcement Learning
2024 (English)Report (Other academic)
Abstract [en]

Optimizing renewable energy usage in smart microgrids that contain photovoltaic production and battery storage is important due to the potential to reduce overall CO2 emissions and thus lead to more environmental friendly energy systems. However, due to the complex nature of energy management in smart grids and the volatile nature of energy production from PV systems, the problem is complex to solve. In this work we aim to optimize the energy in a microgrid comprising six houses using a digital twin based approach based on Deep Reinforcement Learning. We develop a Soft Actor-Critic (SAC) agent to address this intricate challenge, with the aim to simultaneously reduce emissions, maintain user comfort, while maximizing grid efficiency and resiliency to cope with spurious grid outages. We propose and evaluate different reward functions that guide the agent in finding its optimal policy. Furthermore, we discuss the implications of our results and outline potential future steps, envisioning ongoing refinement and advancements in our pursuit of optimal solutions for the complex interplay of severaal objectives in microgrid management.

Place, publisher, year, edition, pages
Karlstads universitet, 2024
Series
Karlstad University Studies, ISSN 1403-8099 ; 2024:21
Keywords
Renewable Energy Communities, Deep Reinforcement Learning, Energy Management
National Category
Computer Sciences Energy Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-100855 (URN)10.59217/dghi3273 (DOI)978-91-7867-474-9 (ISBN)
Projects
AI4ENERGY
Funder
Swedish Energy Agency, 50246-1
Available from: 2024-07-01 Created: 2024-07-01 Last updated: 2024-08-07
Chahed, H. & Kassler, A. (2024). Optimizing TSN Routing, Scheduling, and Task Placement in Virtualized Edge-Compute Platforms. In: Chemouil P., Martini B., Machuca C.M., Papadimitriou P., Borsatti D., Rovedakis S. (Ed.), Proceedings of the 27th Conference on Innovation in Clouds, Internet and Networks, ICIN 2024: . Paper presented at the 27th Conference on Innovation in Clouds, Internet and Networks, ICIN, Paris, France, March 11-14, 2024. (pp. 153-157). Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Optimizing TSN Routing, Scheduling, and Task Placement in Virtualized Edge-Compute Platforms
2024 (English)In: Proceedings of the 27th Conference on Innovation in Clouds, Internet and Networks, ICIN 2024 / [ed] Chemouil P., Martini B., Machuca C.M., Papadimitriou P., Borsatti D., Rovedakis S., Institute of Electrical and Electronics Engineers (IEEE), 2024, p. 153-157Conference paper, Published paper (Refereed)
Abstract [en]

Configuring TSN network elements involves solving a (joint) routing and scheduling problem, where traditionally the TSN endpoints (i.e. talkers and listeners) are already deployed inside fixed industrial computers. However, with the emergence of edge computing on the shop floor, PLCs are becoming virtualized and can flexibly be deployed in containers or virtual machines. This additional flexibility could enhance the network configuration. In this paper, we propose GenTSN, a hybrid genetic algorithm designed to jointly optimize TSN routing, scheduling, and placement of TSN tasks (i.e. talkers and listeners) in virtualized Edge-Compute Platforms. We evaluate GenTSN, showing its efficiency compared to state of the art scheduling algorithms. In particular, we demonstrate that additional degrees of freedom to flexibly place TSN tasks or to flexibly route the traffic leads to better performance. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
genetic algorithm, optimization, routing, scheduling, task placement, Time-Sensitive Networking, TSN
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-99738 (URN)10.1109/ICIN60470.2024.10494455 (DOI)2-s2.0-85191242210 (Scopus ID)979-8-3503-9377-4 (ISBN)979-8-3503-9376-7 (ISBN)
Conference
the 27th Conference on Innovation in Clouds, Internet and Networks, ICIN, Paris, France, March 11-14, 2024.
Funder
Knowledge Foundation
Available from: 2024-06-03 Created: 2024-06-03 Last updated: 2024-06-03Bibliographically approved
Pieskä, M., Kassler, A., Brunstrom, A., Rakocevic, V. & Amend, M. (2024). Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling. Future Internet, 16(7), Article ID 233.
Open this publication in new window or tab >>Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling
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2024 (English)In: Future Internet, E-ISSN 1999-5903, Vol. 16, no 7, article id 233Article in journal (Refereed) Published
Abstract [en]

Multipath wireless access aims to seamlessly aggregate multiple access networks to increase data rates and decrease latency. It is currently being standardized through the ATSSS architectural framework as part of the fifth-generation (5G) cellular networks. However, facilitating efficient multi-access communication in next-generation wireless networks poses several challenges due to the complex interplay between congestion control (CC) and packet scheduling. Given that enhanced ATSSS steering functions for traffic splitting advocate the utilization of multi-access tunnels using congestion-controlled multipath network protocols between user equipment and a proxy, addressing the issue of nested CC becomes imperative. In this paper, we evaluate the impact of such nested congestion control loops on throughput over multi-access tunnels using the recently introduced Multipath DCCP (MP-DCCP) tunneling framework. We evaluate different combinations of endpoint and tunnel CC algorithms, including BBR, BBRv2, CUBIC, and NewReno. Using the Cheapest Path First scheduler, we quantify and analyze the impact of the following on the performance of tunnel-based multipath: (1) the location of the multi-access proxy relative to the user; (2) the bottleneck buffer size, and (3) the choice of the congestion control algorithms. Furthermore, our findings demonstrate the superior performance of BBRv2 as a tunnel CC algorithm.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
5G mobile communication systems, Network protocols, Traffic congestion, 5g, ATSSS, Heterogeneous wireless access, Multipath, Multipath DCCP, Realtime traffic, Transport layers, Unreliable traffic, Wireless access, Wireless networks
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-101316 (URN)10.3390/fi16070233 (DOI)001277441200001 ()2-s2.0-85199633101 (Scopus ID)
Funder
Knowledge Foundation, 20220072-H-01
Available from: 2024-08-12 Created: 2024-08-12 Last updated: 2024-08-12Bibliographically approved
Figueiredo, R., Woesner, H., Kassler, A. & Karl, H. (2024). Quality of Service Performance of Multi-Core Broadband Network Gateways. In: Proccedings of the 8th Network Traffic Measurement and Analysis Conference (TMA): . Paper presented at TMA 2024 - 8th Network Traffic Measurement and Analysis Conference. IEEE
Open this publication in new window or tab >>Quality of Service Performance of Multi-Core Broadband Network Gateways
2024 (English)In: Proccedings of the 8th Network Traffic Measurement and Analysis Conference (TMA), IEEE, 2024Conference paper, Published paper (Refereed)
Abstract [en]

Broadband network access is typically managed by Broadband Network Gateways (BNGs), which can be implemented as a Virtual Network Function (VNF). This paradigm shift is caused by network softwarization and allows the BNG to be deployed on commodity hardware, significantly reducing capital expenditure (CAPEX). But packet processing operations and complex Quality of Service (QoS) policies make it difficult to provide low and predictable latency at scale for a large number of subscribers. To improve performance, parallel queues at the Network Interface Card (NIC) and multiple dedicated CPU cores for packet processing are used, processing 50 million packets per second on commodity x86 hardware. How to guarantee latency, however, remains unclear. In this study, we conducted testbed-based experiments on a VPP/DPDK implementation of the BNG to benchmark its performance. Our findings reveal how latency and its variation increase with background traffic, and we analyze a parameter that contributes to a trade-off between throughput and latency. We also examine the ability of the multi-core architecture to guarantee latency, at a cost of reduced port utilization. These observations influence the design goal of isolating subscriber traffic and highlight the suitability of software BNG for guaranteeing performance.

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
benchmarking, BNG, DPDK, Performance measurements, Quality of Service, VPP, Broadband networks, Computer architecture, Cost reduction, Economic and social effects, Gateways (computer networks), Broadband network access, Broadband network gateway, Multi-cores, Packet processing, Performance, Quality of service performance, Quality-of-service, Virtual networks
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-101212 (URN)10.23919/TMA62044.2024.10559123 (DOI)2-s2.0-85197893635 (Scopus ID)9783903176645 (ISBN)
Conference
TMA 2024 - 8th Network Traffic Measurement and Analysis Conference
Available from: 2024-07-22 Created: 2024-07-22 Last updated: 2024-07-22Bibliographically approved
Aupke, P., Nakao, A. & Kassler, A. (2024). Uncertainty-Aware Forecasting of Computational Load in MECs Using Distributed Machine Learning: A Tokyo Case Study. In: Proceedings - International Conference on Computer Communications and Networks, ICCCN: . Paper presented at 33rd International Conference on Computer Communications and Networks (ICCCN), Big Island, USA, July 29-31, 2024.. Institute of Electrical and Electronics Engineers (IEEE)
Open this publication in new window or tab >>Uncertainty-Aware Forecasting of Computational Load in MECs Using Distributed Machine Learning: A Tokyo Case Study
2024 (English)In: Proceedings - International Conference on Computer Communications and Networks, ICCCN, Institute of Electrical and Electronics Engineers (IEEE), 2024Conference paper, Published paper (Refereed)
Abstract [en]

Mobile Edge Clouds (MECs) address the critical needs of bandwidth-intensive, latency-sensitive mobile applications by positioning computing and storage resources at the network’s edge in Edge Data Centers (EDCs). However, the diverse, dynamic nature of EDCs’ resource capacities and user mobility poses significant challenges for resource allocation and management. Efficient EDC operation requires accurate forecasting of computational load to ensure optimal scaling, service placement, and migration within the MEC infrastructure. This task is complicated by the temporal and spatial fluctuations of computational load.We develop a novel MEC computational demand forecasting method using Federated Learning (FL). Our approach leverages FL’s distributed processing to enhance data security and prediction accuracy within MEC infrastructure. By incorporating uncertainty bounds, we improve load scheduling robustness. Evaluations on a Tokyo dataset show significant improvements in forecast accuracy compared to traditional methods, with a 42.04% reduction in Mean Absolute Error (MAE) using LightGBM and a 34.93% improvement with CatBoost, while maintaining minimal networking overhead for model transmission. 

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2024
Keywords
Mobile edge computing, Computational loads, CPU-load prediction, Datacenter, Edge clouds, Edge data, Edge data center, Load predictions, Machine-learning, Mobile edge cloud, Resource management, Federated learning
National Category
Computer Sciences Computer Systems
Research subject
Computer Science; Computer Science
Identifiers
urn:nbn:se:kau:diva-101906 (URN)10.1109/ICCCN61486.2024.10637613 (DOI)2-s2.0-85203239086 (Scopus ID)979-8-3503-4843-9 (ISBN)979-8-3503-8461-1 (ISBN)
Conference
33rd International Conference on Computer Communications and Networks (ICCCN), Big Island, USA, July 29-31, 2024.
Funder
Swedish Energy Agency, 50246-1, 52693-1
Available from: 2024-10-07 Created: 2024-10-07 Last updated: 2024-10-07Bibliographically approved
Memarian, M., Kassler, A., Grinnemo, K.-J., Laki, S., Pongracz, G. & Forsman, J. (2024). Utilizing Hybrid P4 Solutions to Enhance 5G gNB with Data Plane Programmability. In: Fazio P., Calafate C., Amendola D., Tsiropoulou E.E., Diamanti M., Mannone M. (Ed.), Proceedings of the 2024 15th IFIP Wireless and Mobile Networking Conference: . Paper presented at The 15th IFIP Wireless and Mobile Networking Conference (WMNC), Venice, Italy, November 11-12, 2024. (pp. 47-54). IEEE
Open this publication in new window or tab >>Utilizing Hybrid P4 Solutions to Enhance 5G gNB with Data Plane Programmability
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2024 (English)In: Proceedings of the 2024 15th IFIP Wireless and Mobile Networking Conference / [ed] Fazio P., Calafate C., Amendola D., Tsiropoulou E.E., Diamanti M., Mannone M., IEEE, 2024, p. 47-54Conference paper, Published paper (Refereed)
Abstract [en]

The typical approach to data plane programming involves deploying a single P4 program to a single target. However, different targets have different capabilities, functionalities, and support for various programming languages apart from P4. Consequently, disaggregating a single data plane program into multiple subprograms that run on different targets can take advantage of the strengths of each target, which is particularly important in the context of 5G, as certain data plane processing functions, like buffering and retransmission for RLC processing, cannot effectively be expressed in P4. This paper explores the disaggregation of a 5G gNB across a P4-programmable Smart- NIC and an x86 server using DPDK-based processing, leveraging the strengths of each target. We assess the performance of our hybrid approach by varying which parts of the pipeline run on the SmartNIC and the x86, as well as the number of cores allocated on the host for the non-P4 part of the pipeline.

Place, publisher, year, edition, pages
IEEE, 2024
Keywords
5G, gNB, P4, Data Plane, SmartNIC
National Category
Telecommunications
Research subject
Computer Science
Identifiers
urn:nbn:se:kau:diva-102030 (URN)2-s2.0-85213699751 (Scopus ID)978-3-903176-68-3 (ISBN)979-8-3315-4245-0 (ISBN)
Conference
The 15th IFIP Wireless and Mobile Networking Conference (WMNC), Venice, Italy, November 11-12, 2024.
Projects
Data-driven Latency-sensitive Mobile Services for a Digitalized Society (DRIVE)
Funder
Knowledge Foundation
Available from: 2024-10-18 Created: 2024-10-18 Last updated: 2025-01-23Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-9446-8143

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