TY - THES AU - Rotter, Csaba TI - Erőforrás-menedzsment algoritmusok teljesítményelemzési vizsgálata felhő környezetben. Tézisfüzet TS - Tézisfüzet PY - 2023 SP - 17 UR - https://m2.mtmt.hu/api/publication/33981197 ID - 33981197 LA - Hungarian DB - MTMT ER - TY - THES AU - Rotter, Csaba TI - Performance Analysis of Cloud Resource Management Algorithms PB - Budapesti Műszaki és Gazdaságtudományi Egyetem PY - 2023 SP - 88 UR - https://m2.mtmt.hu/api/publication/33801372 ID - 33801372 LA - English DB - MTMT ER - TY - JOUR AU - Nguyen, Khanh-Van AU - Nguyen, Chi-Hieu AU - Do, Van Tien AU - Rotter, Csaba TI - Efficient Multi-UAV Assisted Data Gathering Schemes for Maximizing the Operation Time of Wireless Sensor Networks in Precision Farming JF - IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS J2 - IEEE T IND INFORM VL - 19 PY - 2023 IS - 12 SP - 11664 EP - 11674 PG - 11 SN - 1551-3203 DO - 10.1109/TII.2023.3248616 UR - https://m2.mtmt.hu/api/publication/33733394 ID - 33733394 N1 - Hanoi University of Science and Technology Ha Noi, Viet Nam Department of Networked Systems and Services, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary Nokia Bell Labs Hungary, Budapest, Hungary LA - English DB - MTMT ER - TY - PAT AU - Manual, Maritinez AU - Rotter, Csaba AU - Csaba, Szalai AU - Zsombor, Kiss TI - Method for testing network services PY - 2021 UR - https://m2.mtmt.hu/api/publication/32606939 ID - 32606939 LA - English DB - MTMT ER - TY - JOUR AU - Nguyen, Tuan Hai AU - Do, Van Tien AU - Rotter, Csaba TI - Scaling UPF Instances in 5G/6G Core with Deep Reinforcement Learning JF - IEEE ACCESS J2 - IEEE ACCESS VL - 9 PY - 2021 SP - 165892 EP - 165906 PG - 15 SN - 2169-3536 DO - 10.1109/ACCESS.2021.3135315 UR - https://m2.mtmt.hu/api/publication/32532803 ID - 32532803 N1 - Export Date: 1 March 2022 Correspondence Address: Van Do, T.; Department of Networked Systems and Services, Hungary; email: do@hit.bme.hu AB - In the 5G core and the upcoming 6G core, the User Plane Function (UPF) is responsible for the transportation of data from and to subscribers in Protocol Data Unit (PDU) sessions. The UPF is generally implemented in software and packed into either a virtual machine or container that can be launched as a UPF instance with a specific resource requirement in a cluster. To save resource consumption needed for UPF instances, the number of initiated UPF instances should depend on the number of PDU sessions required by customers, which is often controlled by a scaling algorithm. In this paper, we investigate the application of Deep Reinforcement Learning (DRL) for scaling UPF instances that are packed in the containers of the Kubernetes container-orchestration framework. We propose an approach with the formulation of a threshold-based reward function and adapt the proximal policy optimization (PPO) algorithm. Also, we apply a support vector machine (SVM) classifier to cope with a problem when the agent suggests an unwanted action due to the stochastic policy. Extensive numerical results show that our approach outperforms Kubernetes’s built-in Horizontal Pod Autoscaler (HPA). DRL could save 2.7–3.8% of the average number of Pods, while SVM could achieve 0.7–4.5% saving compared to HPA. LA - English DB - MTMT ER - TY - JOUR AU - Nguyen, Tuan Hai AU - Do, Van Tien AU - Rotter, Csaba TI - Optimizing the resource usage of actor-based systems JF - JOURNAL OF NETWORK AND COMPUTER APPLICATIONS J2 - J NETW COMPUT APPL VL - 190 PY - 2021 PG - 12 SN - 1084-8045 DO - 10.1016/j.jnca.2021.103143 UR - https://m2.mtmt.hu/api/publication/32073418 ID - 32073418 N1 - Analysis, Design and Development of ICT systems (AddICT) Laboratory, Department of Networked Systems and Services, Budapest University of Technology and Economics, H-1117, Magyar tudósok körútja 2., Budapest, Hungary Nokia Bell Labs, H-1083 Budapest, Bókay János utca 36-42, Budapest, Hungary Cited By :2 Export Date: 9 June 2022 Correspondence Address: Do, T.V.; Analysis, H-1117, Magyar tudósok körútja 2., Hungary; email: do@hit.bme.hu Funding details: Hungarian Scientific Research Fund, OTKA, K-123914 Funding details: Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, NKFIH Funding details: Innovációs és Technológiai Minisztérium Funding text 1: The work of H. Nguyen is partially supported by the National Research Development and Innovation Office of Hungary through the OTKA K-123914 project. The research reported in this paper and carried out at the Budapest University of Technology and Economics has been partially supported by the National Research Development and Innovation Fund based on the charter of bolster issued by the National Research Development and Innovation Office under the auspices of the Ministry for Innovation and Technology. AB - Runtime environments for IoT data processing systems based on the actor model often apply a thread pool to serve data streams. In this paper, we propose an approach based on Reinforcement Learning (RL) to find a trade-off between the the resource (thread pool in server machines) usage and the quality of service for data streams. We compare our approach and the Thread Pool Executor of Akka, an open-source software toolkit. Simulation results show that our approach outperforms ThreadPoolExecutor with the timeout rule when the thread start times are not negligible. Furthermore, the tuning of our approach is not tedious as the application of the timeout rule requires. LA - English DB - MTMT ER - TY - JOUR AU - Rotter, Csaba AU - Do, Van Tien TI - A Queueing Model for Threshold-based Scaling of UPF Instances in 5G Core JF - IEEE ACCESS J2 - IEEE ACCESS VL - 9 PY - 2021 SP - 81443 EP - 81453 PG - 11 SN - 2169-3536 DO - 10.1109/ACCESS.2021.3085955 UR - https://m2.mtmt.hu/api/publication/32055132 ID - 32055132 N1 - Cited By :1 Export Date: 28 February 2022 Correspondence Address: Van Do, T.; Department of Networked Systems and Services, Hungary; email: do@hit.bme.hu LA - English DB - MTMT ER - TY - CHAP AU - Mwanje, Stephen S. AU - Goerge, Jürgen AU - Ali‐Tolppa, Janne AU - Hatonen, Kimmo AU - Bender, Harald AU - Rotter, Csaba AU - Malanchini, Ilaria AU - Sanneck, Henning ED - Mannweiler, Christian ED - Mwanje, Stephen TI - Towards Actualizing Network Autonomy T2 - Towards Cognitive Autonomous Networks PB - Wiley SN - 9781119586388 PY - 2020 SP - 469 EP - 515 PG - 47 DO - 10.1002/9781119586449.ch12 UR - https://m2.mtmt.hu/api/publication/31965395 ID - 31965395 LA - English DB - MTMT ER - TY - CONF AU - Fülöp, Endre AU - Pataki, Norbert AU - Rotter, Csaba ED - Horváth, Zoltán ED - Adrian, Petruşel TI - Modeling Resource Allocations in Cloud Deployment with P Colonies T2 - Collection of Abstracts PB - Babes-Bolyai Tudományegyetem C1 - Budapest PY - 2020 SP - 70 EP - 71 PG - 2 UR - https://m2.mtmt.hu/api/publication/31660490 ID - 31660490 LA - English DB - MTMT ER - TY - JOUR AU - Do, Hoai Nam AU - Do, Van Tien AU - Farkas, Lóránt AU - Rotter, Csaba TI - Provisioning Input and Output Data Rates in Data Processing Frameworks JF - JOURNAL OF GRID COMPUTING J2 - J GRID COMPUT VL - 18 PY - 2020 SP - 491 EP - 506 PG - 16 SN - 1570-7873 DO - 10.1007/s10723-020-09508-0 UR - https://m2.mtmt.hu/api/publication/31039517 ID - 31039517 N1 - Baoji University of Arts and Sciences, Shaanxi, China Department of Networked Systems and Services, Budapest University of Technology and Economics, Magyar tudósok körútja 2., Budapest, Hungary Nokia Bell Labs Hungary, Bókay János utca 36 - 42, Budapest, Hungary Export Date: 1 March 2022 Correspondence Address: Van Do, T.; Baoji University of Arts and SciencesChina; email: do@hit.bme.hu LA - English DB - MTMT ER -