[{"date_created":"2022-03-10T18:28:14Z","publisher":"IEEE","title":"mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks","quality_controlled":"1","year":"2022","language":[{"iso":"eng"}],"keyword":["wireless mobile networks","network management","continuous control","cognitive networks","autonomous coordination","reinforcement learning","gym environment","simulation","open source"],"ddc":["004"],"publication":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","file":[{"content_type":"application/pdf","relation":"main_file","date_updated":"2022-03-10T18:25:41Z","date_created":"2022-03-10T18:25:41Z","creator":"stschn","file_size":223412,"access_level":"open_access","file_id":"30237","file_name":"author_version.pdf"}],"abstract":[{"text":"Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive\r\nresults. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.\r\n\r\nTo this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of\r\nwireless mobile networks.","lang":"eng"}],"author":[{"id":"35343","full_name":"Schneider, Stefan Balthasar","last_name":"Schneider","orcid":"0000-0001-8210-4011","first_name":"Stefan Balthasar"},{"first_name":"Stefan","last_name":"Werner","full_name":"Werner, Stefan"},{"last_name":"Khalili","full_name":"Khalili, Ramin","first_name":"Ramin"},{"last_name":"Hecker","full_name":"Hecker, Artur","first_name":"Artur"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"date_updated":"2022-03-10T18:28:19Z","oa":"1","conference":{"start_date":"2022-04-25","name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","location":"Budapest","end_date":"2022-04-29"},"has_accepted_license":"1","citation":{"chicago":"Schneider, Stefan Balthasar, Stefan Werner, Ramin Khalili, Artur Hecker, and Holger Karl. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” In <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE, 2022.","ieee":"S. B. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.","ama":"Schneider SB, Werner S, Khalili R, Hecker A, Karl H. mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. In: <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE; 2022.","apa":"Schneider, S. B., Werner, S., Khalili, R., Hecker, A., &#38; Karl, H. (2022). mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks. <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.","short":"S.B. Schneider, S. Werner, R. Khalili, A. Hecker, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.","mla":"Schneider, Stefan Balthasar, et al. “Mobile-Env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks.” <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>, IEEE, 2022.","bibtex":"@inproceedings{Schneider_Werner_Khalili_Hecker_Karl_2022, title={mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}, year={2022} }"},"department":[{"_id":"75"}],"user_id":"35343","_id":"30236","project":[{"_id":"1","name":"SFB 901: SFB 901"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"file_date_updated":"2022-03-10T18:25:41Z","type":"conference","status":"public"},{"publisher":"IEEE","date_created":"2022-01-11T08:43:26Z","title":"Use What You Know: Network and Service Coordination Beyond Certainty","quality_controlled":"1","year":"2022","keyword":["network management","service management","AI","Monte Carlo Tree Search","model-based","QoS"],"ddc":["004"],"language":[{"iso":"eng"}],"publication":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","abstract":[{"text":"Modern services often comprise several components, such as chained virtual network functions, microservices, or\r\nmachine learning functions. Providing such services requires to decide how often to instantiate each component, where to place these instances in the network, how to chain them and route traffic through them. \r\nTo overcome limitations of conventional, hardwired heuristics, deep reinforcement learning (DRL) approaches for self-learning network and service management have emerged recently. These model-free DRL approaches are more flexible but typically learn tabula rasa, i.e., disregard existing understanding of networks, services, and their coordination. \r\n\r\nInstead, we propose FutureCoord, a novel model-based AI approach that leverages existing understanding of networks and services for more efficient and effective coordination without time-intensive training. FutureCoord combines Monte Carlo Tree Search with a stochastic traffic model. This allows FutureCoord to estimate the impact of future incoming traffic and effectively optimize long-term effects, taking fluctuating demand and Quality of Service (QoS) requirements into account. Our extensive evaluation based on real-world network topologies, services, and traffic traces indicates that FutureCoord clearly outperforms state-of-the-art model-free and model-based approaches with up to 51% higher flow success ratios.","lang":"eng"}],"file":[{"content_type":"application/pdf","relation":"main_file","date_created":"2022-01-11T08:39:57Z","creator":"stschn","date_updated":"2022-01-11T08:39:57Z","access_level":"open_access","file_id":"29222","file_name":"author_version.pdf","file_size":528653}],"date_updated":"2022-01-11T08:44:04Z","oa":"1","author":[{"last_name":"Werner","full_name":"Werner, Stefan","first_name":"Stefan"},{"orcid":"0000-0001-8210-4011","last_name":"Schneider","full_name":"Schneider, Stefan Balthasar","id":"35343","first_name":"Stefan Balthasar"},{"first_name":"Holger","last_name":"Karl","id":"126","full_name":"Karl, Holger"}],"conference":{"start_date":"2022-04-25","name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","location":"Budapest","end_date":"2022-04-29"},"has_accepted_license":"1","citation":{"mla":"Werner, Stefan, et al. “Use What You Know: Network and Service Coordination Beyond Certainty.” <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>, IEEE, 2022.","bibtex":"@inproceedings{Werner_Schneider_Karl_2022, title={Use What You Know: Network and Service Coordination Beyond Certainty}, booktitle={IEEE/IFIP Network Operations and Management Symposium (NOMS)}, publisher={IEEE}, author={Werner, Stefan and Schneider, Stefan Balthasar and Karl, Holger}, year={2022} }","short":"S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022.","apa":"Werner, S., Schneider, S. B., &#38; Karl, H. (2022). Use What You Know: Network and Service Coordination Beyond Certainty. <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest.","chicago":"Werner, Stefan, Stefan Balthasar Schneider, and Holger Karl. “Use What You Know: Network and Service Coordination Beyond Certainty.” In <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE, 2022.","ieee":"S. Werner, S. B. Schneider, and H. Karl, “Use What You Know: Network and Service Coordination Beyond Certainty,” presented at the IEEE/IFIP Network Operations and Management Symposium (NOMS), Budapest, 2022.","ama":"Werner S, Schneider SB, Karl H. Use What You Know: Network and Service Coordination Beyond Certainty. In: <i>IEEE/IFIP Network Operations and Management Symposium (NOMS)</i>. IEEE; 2022."},"_id":"29220","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"_id":"4","name":"SFB 901 - C: SFB 901 - Project Area C"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"department":[{"_id":"75"}],"user_id":"35343","file_date_updated":"2022-01-11T08:39:57Z","type":"conference","status":"public"},{"abstract":[{"text":"Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks.\r\n\r\nTo address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).","lang":"eng"}],"file":[{"date_updated":"2021-03-18T17:12:56Z","creator":"stschn","date_created":"2021-03-18T17:12:56Z","title":"Distributed Online Service Coordination Using Deep Reinforcement Learning","file_size":606321,"file_id":"21544","file_name":"public_author_version.pdf","access_level":"open_access","content_type":"application/pdf","relation":"main_file"}],"publication":"IEEE International Conference on Distributed Computing Systems (ICDCS)","keyword":["network management","service management","coordination","reinforcement learning","distributed"],"ddc":["000"],"language":[{"iso":"eng"}],"year":"2021","title":"Distributed Online Service Coordination Using Deep Reinforcement Learning","publisher":"IEEE","date_created":"2021-03-18T17:15:47Z","status":"public","type":"conference","file_date_updated":"2021-03-18T17:12:56Z","_id":"21543","project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"department":[{"_id":"75"}],"user_id":"35343","citation":{"apa":"Schneider, S. B., Qarawlus, H., &#38; Karl, H. (2021). Distributed Online Service Coordination Using Deep Reinforcement Learning. In <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. Washington, DC, USA: IEEE.","short":"S.B. Schneider, H. Qarawlus, H. Karl, in: IEEE International Conference on Distributed Computing Systems (ICDCS), IEEE, 2021.","bibtex":"@inproceedings{Schneider_Qarawlus_Karl_2021, title={Distributed Online Service Coordination Using Deep Reinforcement Learning}, booktitle={IEEE International Conference on Distributed Computing Systems (ICDCS)}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}, year={2021} }","mla":"Schneider, Stefan Balthasar, et al. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>, IEEE, 2021.","ama":"Schneider SB, Qarawlus H, Karl H. Distributed Online Service Coordination Using Deep Reinforcement Learning. In: <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. IEEE; 2021.","ieee":"S. B. Schneider, H. Qarawlus, and H. Karl, “Distributed Online Service Coordination Using Deep Reinforcement Learning,” in <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>, Washington, DC, USA, 2021.","chicago":"Schneider, Stefan Balthasar, Haydar Qarawlus, and Holger Karl. “Distributed Online Service Coordination Using Deep Reinforcement Learning.” In <i>IEEE International Conference on Distributed Computing Systems (ICDCS)</i>. IEEE, 2021."},"has_accepted_license":"1","related_material":{"link":[{"url":"https://github.com/ RealVNF/distributed-drl-coordination","relation":"software"}]},"conference":{"location":"Washington, DC, USA","name":"IEEE International Conference on Distributed Computing Systems (ICDCS)"},"oa":"1","date_updated":"2022-01-06T06:55:04Z","author":[{"first_name":"Stefan Balthasar","id":"35343","full_name":"Schneider, Stefan Balthasar","orcid":"0000-0001-8210-4011","last_name":"Schneider"},{"last_name":"Qarawlus","full_name":"Qarawlus, Haydar","first_name":"Haydar"},{"first_name":"Holger","full_name":"Karl, Holger","id":"126","last_name":"Karl"}]},{"project":[{"name":"SFB 901","_id":"1"},{"name":"SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - Subproject C4","_id":"16"}],"_id":"20693","user_id":"35343","department":[{"_id":"75"}],"file_date_updated":"2020-12-11T08:37:37Z","type":"conference","status":"public","oa":"1","date_updated":"2022-01-06T06:54:32Z","author":[{"first_name":"Stefan Balthasar","full_name":"Schneider, Stefan Balthasar","id":"35343","orcid":"0000-0001-8210-4011","last_name":"Schneider"},{"full_name":"Jürgens, Mirko","last_name":"Jürgens","first_name":"Mirko"},{"last_name":"Karl","id":"126","full_name":"Karl, Holger","first_name":"Holger"}],"conference":{"location":"Bordeaux, France","name":"IFIP/IEEE International Symposium on Integrated Network Management (IM)"},"has_accepted_license":"1","citation":{"apa":"Schneider, S. B., Jürgens, M., &#38; Karl, H. (2021). Divide and Conquer: Hierarchical Network and Service Coordination. In <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. Bordeaux, France: IFIP/IEEE.","mla":"Schneider, Stefan Balthasar, et al. “Divide and Conquer: Hierarchical Network and Service Coordination.” <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>, IFIP/IEEE, 2021.","bibtex":"@inproceedings{Schneider_Jürgens_Karl_2021, title={Divide and Conquer: Hierarchical Network and Service Coordination}, booktitle={IFIP/IEEE International Symposium on Integrated Network Management (IM)}, publisher={IFIP/IEEE}, author={Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}, year={2021} }","short":"S.B. Schneider, M. Jürgens, H. Karl, in: IFIP/IEEE International Symposium on Integrated Network Management (IM), IFIP/IEEE, 2021.","chicago":"Schneider, Stefan Balthasar, Mirko Jürgens, and Holger Karl. “Divide and Conquer: Hierarchical Network and Service Coordination.” In <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE, 2021.","ieee":"S. B. Schneider, M. Jürgens, and H. Karl, “Divide and Conquer: Hierarchical Network and Service Coordination,” in <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>, Bordeaux, France, 2021.","ama":"Schneider SB, Jürgens M, Karl H. Divide and Conquer: Hierarchical Network and Service Coordination. In: <i>IFIP/IEEE International Symposium on Integrated Network Management (IM)</i>. IFIP/IEEE; 2021."},"ddc":["006"],"keyword":["network management","service management","coordination","hierarchical","scalability","nfv"],"language":[{"iso":"eng"}],"publication":"IFIP/IEEE International Symposium on Integrated Network Management (IM)","abstract":[{"lang":"eng","text":"In practical, large-scale networks, services are requested\r\nby users across the globe, e.g., for video streaming.\r\nServices consist of multiple interconnected components such as\r\nmicroservices in a service mesh. Coordinating these services\r\nrequires scaling them according to continuously changing user\r\ndemand, deploying instances at the edge close to their users,\r\nand routing traffic efficiently between users and connected instances.\r\nNetwork and service coordination is commonly addressed\r\nthrough centralized approaches, where a single coordinator\r\nknows everything and coordinates the entire network globally.\r\nWhile such centralized approaches can reach global optima, they\r\ndo not scale to large, realistic networks. In contrast, distributed\r\napproaches scale well, but sacrifice solution quality due to their\r\nlimited scope of knowledge and coordination decisions.\r\n\r\nTo this end, we propose a hierarchical coordination approach\r\nthat combines the good solution quality of centralized approaches\r\nwith the scalability of distributed approaches. In doing so, we divide\r\nthe network into multiple hierarchical domains and optimize\r\ncoordination in a top-down manner. We compare our hierarchical\r\nwith a centralized approach in an extensive evaluation on a real-world\r\nnetwork topology. Our results indicate that hierarchical\r\ncoordination can find close-to-optimal solutions in a fraction of\r\nthe runtime of centralized approaches."}],"file":[{"title":"Divide and Conquer: Hierarchical Network and Service Coordination","file_size":7979772,"access_level":"open_access","file_id":"20694","file_name":"preprint_with_header.pdf","date_updated":"2020-12-11T08:37:37Z","creator":"stschn","date_created":"2020-12-11T08:37:37Z","relation":"main_file","content_type":"application/pdf"}],"publisher":"IFIP/IEEE","date_created":"2020-12-11T08:39:47Z","title":"Divide and Conquer: Hierarchical Network and Service Coordination","quality_controlled":"1","year":"2021"},{"publication":"Transactions on Network and Service Management","type":"journal_article","status":"public","file":[{"date_created":"2021-04-27T08:01:26Z","creator":"stschn","date_updated":"2021-04-27T08:01:26Z","file_name":"ris-accepted-version.pdf","access_level":"open_access","file_id":"21809","description":"Author version of the accepted paper","file_size":4172270,"content_type":"application/pdf","relation":"main_file"}],"abstract":[{"text":"Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge).\r\n\r\nWe propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.","lang":"eng"}],"department":[{"_id":"75"}],"user_id":"35343","_id":"21808","project":[{"_id":"1","name":"SFB 901"},{"name":"SFB 901 - Project Area C","_id":"4"},{"_id":"16","name":"SFB 901 - Subproject C4"}],"file_date_updated":"2021-04-27T08:01:26Z","language":[{"iso":"eng"}],"keyword":["network management","service management","coordination","reinforcement learning","self-learning","self-adaptation","multi-objective"],"ddc":["000"],"article_type":"original","has_accepted_license":"1","citation":{"short":"S.B. Schneider, R. Khalili, A. Manzoor, H. Qarawlus, R. Schellenberg, H. Karl, A. Hecker, Transactions on Network and Service Management (2021).","mla":"Schneider, Stefan Balthasar, et al. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, IEEE, 2021, doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>.","bibtex":"@article{Schneider_Khalili_Manzoor_Qarawlus_Schellenberg_Karl_Hecker_2021, title={Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}, DOI={<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>}, journal={Transactions on Network and Service Management}, publisher={IEEE}, author={Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}, year={2021} }","apa":"Schneider, S. B., Khalili, R., Manzoor, A., Qarawlus, H., Schellenberg, R., Karl, H., &#38; Hecker, A. (2021). Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>","ieee":"S. B. Schneider <i>et al.</i>, “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning,” <i>Transactions on Network and Service Management</i>, 2021.","chicago":"Schneider, Stefan Balthasar, Ramin Khalili, Adnan Manzoor, Haydar Qarawlus, Rafael Schellenberg, Holger Karl, and Artur Hecker. “Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning.” <i>Transactions on Network and Service Management</i>, 2021. <a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">https://doi.org/10.1109/TNSM.2021.3076503</a>.","ama":"Schneider SB, Khalili R, Manzoor A, et al. Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning. <i>Transactions on Network and Service Management</i>. 2021. doi:<a href=\"https://doi.org/10.1109/TNSM.2021.3076503\">10.1109/TNSM.2021.3076503</a>"},"year":"2021","author":[{"last_name":"Schneider","orcid":"0000-0001-8210-4011","full_name":"Schneider, Stefan Balthasar","id":"35343","first_name":"Stefan Balthasar"},{"last_name":"Khalili","full_name":"Khalili, Ramin","first_name":"Ramin"},{"first_name":"Adnan","last_name":"Manzoor","full_name":"Manzoor, Adnan"},{"first_name":"Haydar","full_name":"Qarawlus, Haydar","last_name":"Qarawlus"},{"first_name":"Rafael","full_name":"Schellenberg, Rafael","last_name":"Schellenberg"},{"last_name":"Karl","full_name":"Karl, Holger","id":"126","first_name":"Holger"},{"full_name":"Hecker, Artur","last_name":"Hecker","first_name":"Artur"}],"date_created":"2021-04-27T08:04:16Z","publisher":"IEEE","date_updated":"2022-01-06T06:55:15Z","oa":"1","doi":"10.1109/TNSM.2021.3076503","title":"Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning"}]