{"file":[{"file_id":"19610","file_name":"ris_with_copyright.pdf","content_type":"application/pdf","access_level":"open_access","relation":"main_file","date_created":"2020-09-22T06:29:16Z","date_updated":"2020-09-22T06:36:00Z","file_size":642999,"creator":"stschn"}],"ddc":["006"],"file_date_updated":"2020-09-22T06:36:00Z","keyword":["self-driving networks","self-learning","network coordination","service coordination","reinforcement learning","deep learning","nfv"],"date_created":"2020-09-22T06:28:22Z","has_accepted_license":"1","project":[{"_id":"1","name":"SFB 901"},{"_id":"4","name":"SFB 901 - Project Area C"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"abstract":[{"text":"Modern services comprise interconnected components,\r\ne.g., microservices in a service mesh, that can scale and\r\nrun on multiple nodes across the network on demand. To process\r\nincoming traffic, service components have to be instantiated and\r\ntraffic assigned to these instances, taking capacities and changing\r\ndemands into account. This challenge is usually solved with\r\ncustom approaches designed by experts. While this typically\r\nworks well for the considered scenario, the models often rely\r\non unrealistic assumptions or on knowledge that is not available\r\nin practice (e.g., a priori knowledge).\r\n\r\nWe propose a novel deep reinforcement learning approach that\r\nlearns how to best coordinate services and is geared towards\r\nrealistic assumptions. It interacts with the network and relies on\r\navailable, possibly delayed monitoring information. Rather than\r\ndefining a complex model or an algorithm how to achieve an\r\nobjective, our model-free approach adapts to various objectives\r\nand traffic patterns. An agent is trained offline without expert\r\nknowledge and then applied online with minimal overhead. Compared\r\nto a state-of-the-art heuristic, it significantly improves flow\r\nthroughput and overall network utility on real-world network\r\ntopologies and traffic traces. It also learns to optimize different\r\nobjectives, generalizes to scenarios with unseen, stochastic traffic\r\npatterns, and scales to large real-world networks.","lang":"eng"}],"oa":"1","publication":"IEEE International Conference on Network and Service Management (CNSM)","type":"conference","date_updated":"2022-01-06T06:54:08Z","language":[{"iso":"eng"}],"department":[{"_id":"75"}],"_id":"19609","title":"Self-Driving Network and Service Coordination Using Deep Reinforcement Learning","citation":{"short":"S.B. Schneider, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, R. Khalili, A. Hecker, in: IEEE International Conference on Network and Service Management (CNSM), IEEE, 2020.","chicago":"Schneider, Stefan Balthasar, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, Ramin Khalili, and Artur Hecker. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE, 2020.","bibtex":"@inproceedings{Schneider_Manzoor_Qarawlus_Schellenberg_Karl_Khalili_Hecker_2020, title={Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Network and Service Management (CNSM)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}, year={2020} }","apa":"Schneider, S. B., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., Khalili, R., &#38; Hecker, A. (2020). Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE.","ieee":"S. B. Schneider <i>et al.</i>, “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Network and Service Management (CNSM)</i>, 2020.","mla":"Schneider, Stefan Balthasar, et al. “Self-Driving Network and Service Coordination Using Deep Reinforcement Learning.” <i>IEEE International Conference on Network and Service Management (CNSM)</i>, IEEE, 2020.","ama":"Schneider SB, Manzoor A, Qarawlus H, et al. Self-Driving Network and Service Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Network and Service Management (CNSM)</i>. IEEE; 2020."},"user_id":"35343","year":"2020","status":"public","publisher":"IEEE","author":[{"full_name":"Schneider, Stefan Balthasar","last_name":"Schneider","id":"35343","orcid":"0000-0001-8210-4011","first_name":"Stefan Balthasar"},{"first_name":"Adnan","full_name":"Manzoor, Adnan","last_name":"Manzoor"},{"last_name":"Qarawlus","full_name":"Qarawlus, Haydar","first_name":"Haydar"},{"first_name":"Rafael","full_name":"Schellenberg, Rafael","last_name":"Schellenberg"},{"id":"126","last_name":"Karl","full_name":"Karl, Holger","first_name":"Holger"},{"full_name":"Khalili, Ramin","last_name":"Khalili","first_name":"Ramin"},{"full_name":"Hecker, Artur","last_name":"Hecker","first_name":"Artur"}]}