{"keyword":["reinforcement learning","admission control","wireless sensor networks"],"file_date_updated":"2021-10-04T10:43:19Z","project":[{"name":"Akustische Sensornetzwerke - Teilprojekt \"Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke","_id":"27"}],"has_accepted_license":"1","author":[{"full_name":"Afifi, Haitham","last_name":"Afifi","first_name":"Haitham","id":"65718"},{"full_name":"Sauer, Fabian Jakob","last_name":"Sauer","first_name":"Fabian Jakob"},{"full_name":"Karl, Holger","id":"126","last_name":"Karl","first_name":"Holger"}],"status":"public","type":"conference","user_id":"65718","title":"Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding","date_updated":"2022-01-06T06:56:58Z","file":[{"success":1,"file_id":"25279","date_updated":"2021-10-04T10:43:19Z","file_size":534737,"access_level":"closed","relation":"main_file","creator":"hafifi","date_created":"2021-10-04T10:43:19Z","content_type":"application/pdf","file_name":"Preprint___Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_Wireless_Networks.pdf"}],"language":[{"iso":"eng"}],"abstract":[{"text":"Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.","lang":"eng"}],"citation":{"bibtex":"@inproceedings{Afifi_Sauer_Karl_2021, place={Hyderabad, India}, title={Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding}, booktitle={2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)}, author={Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger}, year={2021} }","apa":"Afifi, H., Sauer, F. J., &#38; Karl, H. (2021). Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding. <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>.","ama":"Afifi H, Sauer FJ, Karl H. Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding. In: <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>. ; 2021.","short":"H. Afifi, F.J. Sauer, H. Karl, in: 2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21), Hyderabad, India, 2021.","ieee":"H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding,” 2021.","mla":"Afifi, Haitham, et al. “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding.” <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>, 2021.","chicago":"Afifi, Haitham, Fabian Jakob Sauer, and Holger Karl. “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding.” In <i>2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS’21)</i>. Hyderabad, India, 2021."},"year":"2021","place":"Hyderabad, India","_id":"25278","publication":"2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21)","ddc":["000"],"date_created":"2021-10-04T10:42:20Z"}