@mastersthesis{MTMT:33981197, title = {Erőforrás-menedzsment algoritmusok teljesítményelemzési vizsgálata felhő környezetben. Tézisfüzet}, url = {https://m2.mtmt.hu/api/publication/33981197}, author = {Rotter, Csaba}, unique-id = {33981197}, year = {2023}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @mastersthesis{MTMT:33801372, title = {Performance Analysis of Cloud Resource Management Algorithms}, url = {https://m2.mtmt.hu/api/publication/33801372}, author = {Rotter, Csaba}, publisher = {Budapest University of Technology and Economics}, unique-id = {33801372}, year = {2023}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @article{MTMT:33733394, title = {Efficient Multi-UAV Assisted Data Gathering Schemes for Maximizing the Operation Time of Wireless Sensor Networks in Precision Farming}, url = {https://m2.mtmt.hu/api/publication/33733394}, author = {Nguyen, Khanh-Van and Nguyen, Chi-Hieu and Do, Van Tien and Rotter, Csaba}, doi = {10.1109/TII.2023.3248616}, journal-iso = {IEEE T IND INFORM}, journal = {IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS}, volume = {19}, unique-id = {33733394}, issn = {1551-3203}, year = {2023}, eissn = {1941-0050}, pages = {11664-11674}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @book{MTMT:32606939, title = {Method for testing network services}, url = {https://m2.mtmt.hu/api/publication/32606939}, author = {Manual, Maritinez and Rotter, Csaba and Csaba, Szalai and Zsombor, Kiss}, unique-id = {32606939}, year = {2021}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @article{MTMT:32532803, title = {Scaling UPF Instances in 5G/6G Core with Deep Reinforcement Learning}, url = {https://m2.mtmt.hu/api/publication/32532803}, author = {Nguyen, Tuan Hai and Do, Van Tien and Rotter, Csaba}, doi = {10.1109/ACCESS.2021.3135315}, journal-iso = {IEEE ACCESS}, journal = {IEEE ACCESS}, volume = {9}, unique-id = {32532803}, issn = {2169-3536}, abstract = {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.}, year = {2021}, eissn = {2169-3536}, pages = {165892-165906}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @article{MTMT:32073418, title = {Optimizing the resource usage of actor-based systems}, url = {https://m2.mtmt.hu/api/publication/32073418}, author = {Nguyen, Tuan Hai and Do, Van Tien and Rotter, Csaba}, doi = {10.1016/j.jnca.2021.103143}, journal-iso = {J NETW COMPUT APPL}, journal = {JOURNAL OF NETWORK AND COMPUTER APPLICATIONS}, volume = {190}, unique-id = {32073418}, issn = {1084-8045}, abstract = {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.}, keywords = {reinforcement learning; resource management; IoT; actor}, year = {2021}, eissn = {1095-8592}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @article{MTMT:32055132, title = {A Queueing Model for Threshold-based Scaling of UPF Instances in 5G Core}, url = {https://m2.mtmt.hu/api/publication/32055132}, author = {Rotter, Csaba and Do, Van Tien}, doi = {10.1109/ACCESS.2021.3085955}, journal-iso = {IEEE ACCESS}, journal = {IEEE ACCESS}, volume = {9}, unique-id = {32055132}, issn = {2169-3536}, year = {2021}, eissn = {2169-3536}, pages = {81443-81453}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @{MTMT:31965395, title = {Towards Actualizing Network Autonomy}, url = {https://m2.mtmt.hu/api/publication/31965395}, author = {Mwanje, Stephen S. and Goerge, Jürgen and Ali‐Tolppa, Janne and Hatonen, Kimmo and Bender, Harald and Rotter, Csaba and Malanchini, Ilaria and Sanneck, Henning}, booktitle = {Towards Cognitive Autonomous Networks}, doi = {10.1002/9781119586449.ch12}, unique-id = {31965395}, year = {2020}, pages = {469-515}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} } @CONFERENCE{MTMT:31660490, title = {Modeling Resource Allocations in Cloud Deployment with P Colonies}, url = {https://m2.mtmt.hu/api/publication/31660490}, author = {Fülöp, Endre and Pataki, Norbert and Rotter, Csaba}, booktitle = {Collection of Abstracts}, unique-id = {31660490}, year = {2020}, pages = {70-71}, orcid-numbers = {Pataki, Norbert/0000-0002-7519-3367; Rotter, Csaba/0000-0001-5527-1674} } @article{MTMT:31039517, title = {Provisioning Input and Output Data Rates in Data Processing Frameworks}, url = {https://m2.mtmt.hu/api/publication/31039517}, author = {Do, Hoai Nam and Do, Van Tien and Farkas, Lóránt and Rotter, Csaba}, doi = {10.1007/s10723-020-09508-0}, journal-iso = {J GRID COMPUT}, journal = {JOURNAL OF GRID COMPUTING}, volume = {18}, unique-id = {31039517}, issn = {1570-7873}, year = {2020}, eissn = {1572-9184}, pages = {491-506}, orcid-numbers = {Rotter, Csaba/0000-0001-5527-1674} }