[{"date_created":"2022-01-11T08:43:26Z","file":[{"creator":"stschn","date_created":"2022-01-11T08:39:57Z","file_name":"author_version.pdf","access_level":"open_access","file_size":528653,"relation":"main_file","date_updated":"2022-01-11T08:39:57Z","file_id":"29222","content_type":"application/pdf"}],"department":[{"_id":"75"}],"type":"conference","keyword":["network management","service management","AI","Monte Carlo Tree Search","model-based","QoS"],"publication":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","abstract":[{"lang":"eng","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."}],"language":[{"iso":"eng"}],"author":[{"full_name":"Werner, Stefan","first_name":"Stefan","last_name":"Werner"},{"full_name":"Schneider, Stefan Balthasar","first_name":"Stefan Balthasar","last_name":"Schneider","orcid":"0000-0001-8210-4011","id":"35343"},{"first_name":"Holger","last_name":"Karl","full_name":"Karl, Holger","id":"126"}],"year":"2022","title":"Use What You Know: Network and Service Coordination Beyond Certainty","date_updated":"2022-01-11T08:44:04Z","oa":"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} }","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.","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.","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.","short":"S. Werner, S.B. Schneider, H. Karl, in: IEEE/IFIP Network Operations and Management Symposium (NOMS), IEEE, 2022."},"file_date_updated":"2022-01-11T08:39:57Z","project":[{"name":"SFB 901: SFB 901","_id":"1"},{"name":"SFB 901 - C: SFB 901 - Project Area C","_id":"4"},{"name":"SFB 901 - C4: SFB 901 - Subproject C4","_id":"16"}],"quality_controlled":"1","publisher":"IEEE","_id":"29220","user_id":"35343","ddc":["004"],"conference":{"start_date":"2022-04-25","name":"IEEE/IFIP Network Operations and Management Symposium (NOMS)","location":"Budapest","end_date":"2022-04-29"},"status":"public","has_accepted_license":"1"},{"date_created":"2019-07-10T09:21:38Z","keyword":["Approximate computing","High-level synthesis","Accuracy","Monte-Carlo tree search","Circuit simulation"],"type":"conference","department":[{"_id":"78"}],"publication":"26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)","citation":{"chicago":"Awais, Muhammad, Hassan Ghasemzadeh Mohammadi, and Marco Platzner. “An MCTS-Based Framework for Synthesis of Approximate Circuits.” In <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 219–24, 2018. <a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">https://doi.org/10.1109/VLSI-SoC.2018.8645026</a>.","ama":"Awais M, Ghasemzadeh Mohammadi H, Platzner M. An MCTS-based Framework for Synthesis of Approximate Circuits. In: <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>. ; 2018:219-224. doi:<a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">10.1109/VLSI-SoC.2018.8645026</a>","short":"M. Awais, H. Ghasemzadeh Mohammadi, M. Platzner, in: 26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC), 2018, pp. 219–224.","bibtex":"@inproceedings{Awais_Ghasemzadeh Mohammadi_Platzner_2018, title={An MCTS-based Framework for Synthesis of Approximate Circuits}, DOI={<a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">10.1109/VLSI-SoC.2018.8645026</a>}, booktitle={26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)}, author={Awais, Muhammad and Ghasemzadeh Mohammadi, Hassan and Platzner, Marco}, year={2018}, pages={219–224} }","mla":"Awais, Muhammad, et al. “An MCTS-Based Framework for Synthesis of Approximate Circuits.” <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 2018, pp. 219–24, doi:<a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">10.1109/VLSI-SoC.2018.8645026</a>.","apa":"Awais, M., Ghasemzadeh Mohammadi, H., &#38; Platzner, M. (2018). An MCTS-based Framework for Synthesis of Approximate Circuits. In <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i> (pp. 219–224). <a href=\"https://doi.org/10.1109/VLSI-SoC.2018.8645026\">https://doi.org/10.1109/VLSI-SoC.2018.8645026</a>","ieee":"M. Awais, H. Ghasemzadeh Mohammadi, and M. Platzner, “An MCTS-based Framework for Synthesis of Approximate Circuits,” in <i>26th IFIP/IEEE International Conference on Very Large Scale Integration (VLSI-SoC)</i>, 2018, pp. 219–224."},"abstract":[{"lang":"eng","text":"Approximate computing has become a very popular design\r\nstrategy that exploits error resilient computations to achieve higher\r\nperformance and energy efficiency. Automated synthesis of approximate\r\ncircuits is performed via functional approximation, in which various\r\nparts of the target circuit are extensively examined with a library\r\nof approximate components/transformations to trade off the functional\r\naccuracy and computational budget (i.e., power). However, as the number\r\nof possible approximate transformations increases, traditional search\r\ntechniques suffer from a combinatorial explosion due to the large\r\nbranching factor. In this work, we present a comprehensive framework\r\nfor automated synthesis of approximate circuits from either structural\r\nor behavioral descriptions. We adapt the Monte Carlo Tree Search\r\n(MCTS), as a stochastic search technique, to deal with the large design\r\nspace exploration, which enables a broader range of potential possible\r\napproximations through lightweight random simulations. The proposed\r\nframework is able to recognize the design Pareto set even with low\r\ncomputational budgets. Experimental results highlight the capabilities of\r\nthe proposed synthesis framework by resulting in up to 61.69% energy\r\nsaving while maintaining the predefined quality constraints."}],"page":"219-224","_id":"10598","language":[{"iso":"eng"}],"doi":"10.1109/VLSI-SoC.2018.8645026","user_id":"64665","year":"2018","title":"An MCTS-based Framework for Synthesis of Approximate Circuits","status":"public","author":[{"id":"64665","first_name":"Muhammad","orcid":"https://orcid.org/0000-0003-4148-2969","last_name":"Awais","full_name":"Awais, Muhammad"},{"id":"61186","full_name":"Ghasemzadeh Mohammadi, Hassan","first_name":"Hassan","last_name":"Ghasemzadeh Mohammadi"},{"id":"398","last_name":"Platzner","first_name":"Marco","full_name":"Platzner, Marco"}],"date_updated":"2022-01-06T06:50:46Z"}]