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Prof. Dr. Andreas J. Kassler

Professor

Lehrgebiet: Rechnernetze und Verteilte Systeme

  • Mobile Netze (5G, 6G, WiFi)
  • Echtzeitnetze (TSN)
  • Programmierbare Netze
  • Anwendung von Künstliche Intelligenz in Rechnernetzen
  • Intelligente vernetzte Systeme (e.g. Datenzenter, SmartGrid,..)

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Sortierung:
Beitrag in Sammelwerk/Tagungsband
  • Andreas Fischer
  • D. Bhamare
  • Andreas Kassler

On the Construction of Optimal Embedding Problems for Delay-Sensitive Service Function Chains.

pg. 1-10

(2019)

Beitrag in Sammelwerk/Tagungsband
  • Y. Sharma
  • M. Khan
  • J. Taheri
  • Andreas Kassler

Performance Benchmarking of Virtualized Network Functions to Correlate Key Performance Metrics with System Activity.

IEEE pg. 73-81

DOI: 10.1109/NoF50125.2020.9249199

(2020)

Zeitschriftenartikel
  • M. Khoshkholghi
  • M. Gokan Khan
  • Kyoomars Alizadeh Noghani
  • J. Taheri
  • Deval Bhamare
  • Andreas Kassler
  • Zhengzhe Xiang
  • S. Deng
  • Xiaoxian Yang

Service Function Chain Placement for Joint Cost and Latency Optimization.

In: Mobile Networks and Applications (vol. 25) , pg. 2191-2205

(2020)

DOI: 10.1007/s11036-020-01661-w

Beitrag in Sammelwerk/Tagungsband
  • M. Khan
  • J. Taheri
  • M. Khoshkholghi
  • Andreas Kassler
  • C. Cartwright
  • M. Darula
  • S. Deng

A Performance Modelling Approach for SLA-Aware Resource Recommendation in Cloud Native Network Functions.

  • Best Student Paper Award.
  • IEEE pg. 292-300

    DOI: 10.1109/NetSoft48620.2020.9165482

    (2020)

    Zeitschriftenartikel
    • J. Vestin
    • Andreas Kassler
    • S. Laki
    • G. Pongrácz

    Toward In-Network Event Detection and Filtering for Publish/Subscribe Communication Using Programmable Data Planes.

    In: IEEE Transactions on Network and Service Management (vol. 18) , pg. 415-428

    (2021)

    DOI: 10.1109/TNSM.2020.3040011

    Beitrag in Sammelwerk/Tagungsband
    • S. Laki
    • P. Vörös
    • G. Szabo
    • Andreas Kassler

    Revitalizing Industrial Networking with Programmable Data Planes.

    (2021)

    Beitrag in Sammelwerk/Tagungsband
    • Phil Aupke
    • Andreas Kassler
    • Andreas Theocharis
    • Magnus Nilsson
    • Michael Uelschen

    Quantifying Uncertainty for Predicting Renewable Energy Time Series Data Using Machine Learning.

    Basel, Switzerland: MDPI pg. 50

    DOI: 10.3390/engproc2021005050

    (2021)

    Beitrag in Sammelwerk/Tagungsband
    • R. Figueiredo
    • Andreas Kassler

    BNG-HAL: A Unified API for Disaggregated BNGs.

    IEEE pg. 116-119

    DOI: 10.1109/NFV-SDN53031.2021.9665122

    (2021)

    Zeitschriftenartikel
    • N. Skorin-Kapov
    • R. Santos
    • H. Ghazzai
    • Andreas Kassler

    A Randomized Greedy Heuristic for Steerable Wireless Backhaul Reconfiguration.

    In: Electronics (vol. 10) , pg. 434

    (2021)

    DOI: 10.3390/electronics10040434

    Beitrag in Sammelwerk/Tagungsband
    • C. Benet
    • Andreas Kassler
    • G. Antichi
    • T. Benson
    • G. Pongrácz

    Providing In-network Support to Coflow Scheduling.

    IEEE pg. 235-243

    DOI: 10.1109/NetSoft51509.2021.9492530

    (2021)

    Beitrag in Sammelwerk/Tagungsband
    • H. Chahed
    • Andreas Kassler

    Software-Defined Time Sensitive Networks Configuration and Management.

    IEEE pg. 124-128

    DOI: 10.1109/NFV-SDN53031.2021.9665120

    (2021)

    Zeitschriftenartikel
    • M. Gokan Khan
    • J. Taheri
    • A. Al-Dulaimy
    • Andreas Kassler

    PerfSim: A Performance Simulator for Cloud Native Microservice Chains.

    In: IEEE Transactions on Cloud Computing , pg. 1-1

    (2021)

    DOI: 10.1109/TCC.2021.3135757

    Beitrag in Sammelwerk/Tagungsband
    • A. Nammouchi
    • Phil Aupke
    • Andreas Kassler
    • Andreas Theocharis
    • Viviana Raffa
    • Marco Di Felice

    Integration of AI, IoT and Edge-Computing for Smart Microgrid Energy Management.

    IEEE pg. 1-6

    DOI: 10.1109/EEEIC/ICPSEurope51590.2021.9584756

    (2021)

    Zeitschriftenartikel
    • K. Noghani
    • Andreas Kassler
    • J. Taheri
    • P. Ohlen
    • C. Curescu

    Multi-Objective Genetic Algorithm for Fast Service Function Chain Reconfiguration.

    In: IEEE Transactions on Network and Service Management , pg. 1-1

    (2022)

    DOI: 10.1109/TNSM.2022.3195820

    Zeitschriftenartikel
    • A. Mesodiakaki
    • E. Zola
    • Andreas Kassler

    Robust and energy-efficient user association and traffic routing in B5G HetNets.

    In: Computer Networks (vol. 217) , pg. 109305

    (2022)

    DOI: 10.1016/j.comnet.2022.109305

    Zeitschriftenartikel
    • A. Al-Dulaimy
    • J. Taheri
    • Andreas Kassler
    • M. Hoseiny Farahabady
    • S. Deng
    • Albert Zomaya

    MultiScaler: A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications.

    In: IEEE Transactions on Cloud Computing (vol. 10) , pg. 2769-2786

    (2022)

    DOI: 10.1109/TCC.2020.3031676

    Beitrag in Sammelwerk/Tagungsband
    • P. Aupke
    • Andreas Kassler
    • A. Theocharis
    • M. Nilsson
    • I. Myren Andersson

    Impact of Clustering Methods on Machine Learning based Solar Power Prediction Models.

    (2022)

    Beitrag in Sammelwerk/Tagungsband
    • R. Alfredsson
    • Andreas Kassler
    • J. Vestin
    • M. Pieska
    • M. Amend

    Accelerating a Transport Layer Based 5G Multi-Access Proxy on SmartNIC.

    (2022)

    Beitrag in Sammelwerk/Tagungsband
    • Y. Ma
    • Andreas Kassler
    • B. Ahmed
    • P. Krakhmalev
    • A. Thore
    • A. Toyser
    • H. Lindbäck

    Using Deep Reinforcement Learning for Zero Defect Smart Forging.

    (2022)

    Beitrag in Sammelwerk/Tagungsband
    • M. Amend
    • N. Moreno
    • M. Pieska
    • Andreas Kassler
    • A. Brunstrom
    • V. Rakocevic
    • S. Johnson

    In-network Support for Packet Reordering for Multiaccess Transport Layer Tunneling.

    IEEE pg. 1-6

    DOI: 10.23919/PEMWN56085.2022.9963814

    (2022)

    Zeitschriftenartikel
    • R. Santos
    • N. Skorin-Kapov
    • H. Ghazzai
    • Andreas Kassler
    • G. Tran

    Towards the optimal orchestration of steerable mmWave backhaul reconfiguration.

    In: Computer Networks (vol. 205) , pg. 108750

    (2022)

    DOI: 10.1016/j.comnet.2021.108750

    Zeitschriftenartikel
    • F. Bayram
    • B. Ahmed
    • Andreas Kassler

    From concept drift to model degradation: An overview on performance-aware drift detectors.

    In: Knowledge-Based Systems (vol. 245) , pg. 108632

    (2022)

    DOI: 10.1016/j.knosys.2022.108632

    Zeitschriftenartikel
    • S. Kumar Singh
    • C. Rothenberg
    • J. Langlet
    • Andreas Kassler
    • P. Voros
    • S. Laki
    • G. Pongrácz

    Hybrid P4 Programmable Pipelines for 5G gNodeB and User Plane Functions.

    In: IEEE Transactions on Mobile Computing , pg. 1-18

    (2022)

    DOI: 10.1109/TMC.2022.3201512

    Zeitschriftenartikel
    • D. Bhamare
    • Andreas Kassler
    • J. Vestin
    • M. Khoshkholghi
    • J. Taheri
    • T. Mahmoodi
    • P. Öhlén
    • C. Curescu

    IntOpt: In-band Network Telemetry optimization framework to monitor network slices using P4.

    In: Computer Networks (vol. 216) , pg. 109214

    (2022)

    DOI: 10.1016/j.comnet.2022.109214

    Beitrag in Sammelwerk/Tagungsband
    • A. Nammouchi
    • M. Stentani
    • S. Paoletti
    • Andreas Kassler
    • A. Theocharis

    Robust Operation of Energy Communities in the Italian Incentive System.

    • In:
    • Université Grenoble Alpes
    • IEEE Power & Energy Society

    DOI: 10.1109/ISGTEUROPE56780.2023.10408430

    (2023)

    Beitrag in Sammelwerk/Tagungsband
    • A. Nammouchi
    • Andreas Kassler
    • A. Theocharis

    Quantum Machine Learning in Climate Change and Sustainability: a Short Review.

    DOI: 10.1609/aaaiss.v2i1.27657

    (2023)

    Beitrag in Sammelwerk/Tagungsband
    • R. Figueiredo
    • Andreas Kassler
    • H. Karl

    Performance Measurements of Broadband Network Gateways.

    • In:
    • G. Javanmardi
    • L. Almeida
    • T. Gonçalves
    • N. Fidalgo

    pg. 16-17

    (2023)

    Zeitschriftenartikel
    • A. Nammouchi
    • P. Aupke
    • F. D’Andreagiovanni
    • H. Ghazzai
    • A. Theocharis
    • Andreas Kassler

    Robust opportunistic optimal energy management of a mixed microgrid under asymmetrical uncertainties.

    In: Sustainable Energy, Grids and Networks (vol. 36) , pg. 101184

    (2023)

    DOI: 10.1016/j.segan.2023.101184

    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.
    Zeitschriftenartikel
    • H. Chahed
    • Andreas Kassler

    TSN Network Scheduling—Challenges and Approaches.

    In: Network (vol. 3) , pg. 585-624

    (2023)

    DOI: 10.3390/network3040026

    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.
    Beitrag in Sammelwerk/Tagungsband
    • Y. Ma
    • K. Younis
    • B. Ahmed
    • Andreas Kassler
    • P. Krakhmalev
    • A. Thore
    • H. Lindbäck

    Automated and Systematic Digital Twins Testing for Industrial Processes.

    IEEE pg. 149-158

    DOI: 10.1109/ICSTW58534.2023.00037

    (2023)

    Beitrag in Sammelwerk/Tagungsband
    • P. Aupke
    • Seema
    • A. Theocharis
    • Andreas Kassler
    • D.-E. Archer

    PV Power Production and Consumption Estimation with Uncertainty bounds in Smart Energy Grids.

    IEEE pg. 1-6

    DOI: 10.1109/EEEIC/ICPSEurope57605.2023.10194894

    (2023)

    Zeitschriftenartikel
    • F. Bayram
    • P. Aupke
    • B. Ahmed
    • Andreas Kassler
    • A. Theocharis
    • J. Forsman

    DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks.

    In: Engineering Applications of Artificial Intelligence (vol. 123, Part C) , pg. 106480

    (2023)

    DOI: 10.1016/j.engappai.2023.106480

    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.
    Beitrag in Sammelwerk/Tagungsband
    • F. Brisch
    • Andreas Kassler
    • J. Vestin
    • M. Pieska
    • M. Amend

    Accelerating Transport Layer Multipath Packet Scheduling for 5G-ATSSS.

    (2023)

    Zeitschriftenartikel
    • H. Chahed
    • M. Usman
    • A. Chatterjee
    • F. Bayram
    • R. Chaudhary
    • A. Brunstrom
    • J. Taheri
    • B. Ahmed
    • Andreas Kassler

    AIDA—A holistic AI-driven networking and processing framework for industrial IoT applications.

    In: Internet of Things (vol. 22) , pg. 100805

    (2023)

    DOI: 10.1016/j.iot.2023.100805

    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.
    Vortrag
    • Andreas Kassler
    • M. Amend

    Explorative Journey on 5G and beyond – Dos and Don't’s. Interactive Session.

    Würzburg 28.-30.06.2023.

    (2023)

    Vortrag
    • F. Brisch
    • Andreas Kassler
    • J. Vestin
    • M. Pieska
    • M. Amend

    Accelerating Transport Layer Multipath Packet Scheduling for 5G-ATSSS.

    Würzburg 28.-30.06.2023.

    (2023)

    Beitrag in Sammelwerk/Tagungsband
    • M. Memarian
    • Andreas Kassler
    • K.-J. Grinnemo
    • S. Laki
    • G. Pongrácz
    • J. Forsman

    Utilizing Hybrid P4 Solutions to Enhance 5G gNB with Data Plane Programmability.

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • R. Figueiredo
    • H. Woesner
    • Andreas Kassler
    • H. Karl

    Quality of Service Performance of Multi-Core Broadband Network Gateways.

    IEEE pg. 1-10

    DOI: 10.23919/TMA62044.2024.10559123

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • F. Brisch
    • Andreas Kassler
    • S. Laki
    • P. Hudoba

    P4-MTAGG - a Framework for Multi-Tenant P4 Network Devices.

    IEEE pg. 1-3

    DOI: 10.23919/CNSM62983.2024.10814635

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • H. Chahed
    • Andreas Kassler

    Optimizing TSN Routing, Scheduling, and Task Placement in Virtualized Edge-Compute Platforms.

    IEEE pg. 153-157

    DOI: 10.1109/ICIN60470.2024.10494455

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • P. Aupke
    • A. Nakao
    • Andreas Kassler

    Uncertainty-Aware Forecasting of Computational Load in MECs Using Distributed Machine Learning: A Tokyo Case Study.

    IEEE pg. 1-6

    DOI: 10.1109/ICCCN61486.2024.10637613

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • A. Nammouchi
    • C. Chaabani
    • A. Theocharis
    • Andreas Kassler

    Towards Explainable Renewable Energy Communities Operations Using Generative AI.

    • In:
    • M. Zidar
    • T. Baškarad
    • N. Holjevac
    • I. Kuzle

    pg. 110

    DOI: 10.1109/ISGTEUROPE62998.2024.10863790

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • H. Chahed
    • F. Hallström
    • H. Alcaine
    • Andreas Kassler

    Linux-Based End-Station Design for Seamless TSN Plug -and - Play.

    IEEE pg. 1-6

    DOI: 10.1109/IC_ASET61847.2024.10596212

    (2024)

    Beitrag in Sammelwerk/Tagungsband
    • F. Brisch
    • Andreas Kassler
    • S. Laki
    • P. Hudoba
    • G. Pongrácz

    P4-MTAGG - a Framework for Multi-Tenant P4 Network Devices.

    (2024)

    Zeitschriftenartikel
    • M. Pieska
    • Andreas Kassler
    • A. Brunstrom
    • V. Rakocevic
    • M. Amend

    Performance Impact of Nested Congestion Control on Transport-Layer Multipath Tunneling.

    In: Future Internet (vol. 16) , pg. 233

    (2024)

    DOI: 10.3390/fi16070233

    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.
    Beitrag in Sammelwerk/Tagungsband
    • M. Khan
    • J. Taheri
    • Andreas Kassler
    • A. Asl

    Graph Attention Networks and Deep Q-Learning for Service Mesh Optimization: A Digital Twinning Approach.

    IEEE pg. 2913-2918

    DOI: 10.1109/ICC51166.2024.10622616

    (2024)

    Zeitschriftenartikel
    • M. Pieska
    • A. Rabitsch
    • A. Brunstrom
    • Andreas Kassler
    • M. Amend
    • E. Bogenfeld

    Low-delay cost-aware multipath scheduling over dynamic links for access traffic steering, switching, and splitting.

    In: Computer Networks (vol. 241) , pg. 110186

    (2024)

    DOI: 10.1016/j.comnet.2024.110186

    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.
    Beitrag in Sammelwerk/Tagungsband
    • M. Memarian
    • Andreas Kassler
    • K.-J. Grinnemo
    • S. Laki
    • G. Pongrácz
    • J. Forsman

    Utilizing Hybrid P4 Solutions to Enhance 5G gNB with Data Plane Programmability.

    (2024)

    Zeitschriftenartikel
    • Zineddine Bettouche
    • Khalid Ali
    • Andreas Fischer
    • Andreas Kassler

    Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for 5G Traffic Forecasting.

    In: Online-Publikationssystem der Universität Tübingen

    (2025)

    DOI: 10.15496/publikation-105099

    Accurate spatiotemporal traffic forecasting is vital for optimizing 5G networks. Traditional LSTM models struggle with capturing complex spatiotemporal dependencies, limiting predictive performance. To address this, we propose an enhanced Spatiotemporal Network (STN) integrating Scalar LSTM (sLSTM), a more efficient variant designed to improve temporal modeling while reducing computational complexity. Our dualpath STN processes the input through an sLSTM for sequential feature extraction and a three-layer Conv3D path for spatial feature learning, with both outputs fused in a dedicated fusion layer for enhanced spatiotemporal representation. By incorporating sLSTM, our model stabilizes gradients, accelerates convergence, and enhances accuracy. Experiments on real-world mobile traffic datasets show a 23% MAE reduction over ConvLSTM, with a 30% improvement on unseen data, demonstrating superior generalization for 5G traffic prediction.
    Bericht/Report
    • Khalid Ali
    • Zineddine Bettouche
    • Andreas Kassler
    • Andreas Fischer

    Enhancing Spatiotemporal Networks with xLSTM: A Scalar LSTM Approach for Cellular Traffic Forecasting. 16th International Conference on Network of the Future (NoF 2025). arXiv preprint.

    (2025)

    Monographie
    • Özgür Kaynak
    • Andreas Kassler
    • Andreas Fischer
    • O. Dobrijevic
    • H. Chahed

    TSN Scheduling Robust to Wireless Performance Uncertainties: A Problem and Model Definition.

    Würzburg: Universität Würzburg

    (2025)

    Bericht/Report
    • Zineddine Bettouche
    • Khalid Ali
    • Andreas Fischer
    • Andreas Kassler

    HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting. 3rd International Workshop on Machine Learning in Networking (MaLeNe 2025). arXiv preprint.

    (2025)

    Projekte

    Spitzenprofessur der HighTech Agenda


    Labore

    Intelligent Networks and Systems (Head)


    Kernkompetenzen

    • Software Defined Networking
    • Programmable Dataplanes
    • Network (Function) Virtualization
    • Autonomic Networking
    • Future Internet
    • Wireless Networks
    • Network Optimization


    Forschungs- und Lehrgebiete

    Andreas J. Kassler is Professor of Computer Science at Deggendorf Institute of Technology, Germany (since 2023) and Karlstads Universitet, Karlstad, Sweden (since 2005). From 2003 to 2004, Dr. Andreas J. Kassler was Assistant Professor at the School of Computer Engineering, Nanyang Technological University, Singapore. At Degegndorf, he is leading the Intelligent Network and Systems Lab. He maintains an active research program in the fields of networking and cloud computing with main research focus on Software Defined Networking, Future Internet, Datacenter Networking and, Quality of Service.

    Dr. Andreas J. Kassler received the Docent title (Habilitation) in Computer Science from Karlstads Universitet in 2007 and the Ph.D. degree in Computer Science from Universität Ulm, Germany, in 2002. He received the M. Sc. degree in Mathematics/Computer Science in 1995 from Universität Augsburg, Germany.

    He is co-author of around 55 peer reviewed journal articles and book chapters, 195 peer reviewed conference and workshop publications, 7 European or international patents and 11 IETF and ISO standardization contributions. He is also co-editor of a book published in the LNCS book series of Springer. He is the area editor of the Elsevier Computer Networks Journal, served as a guest editor of a feature topic in EURASIP Wireless Communications and Networking Journal, and served as Associate Editor on the editorial boards of some refereed international journals, such as: Journal of Internet Engineering, International Journal On Advances in Networks and Services.