[{"has_accepted_license":"1","year":"2021","place":"Hyderabad, India","citation":{"ieee":"H. Afifi, F. J. Sauer, and H. Karl, “Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding,” 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.","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.","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>.","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.","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.","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} }"},"date_updated":"2022-01-06T06:56:58Z","date_created":"2021-10-04T10:42:20Z","author":[{"first_name":"Haitham","id":"65718","full_name":"Afifi, Haitham","last_name":"Afifi"},{"first_name":"Fabian Jakob","full_name":"Sauer, Fabian Jakob","last_name":"Sauer"},{"first_name":"Holger","full_name":"Karl, Holger","id":"126","last_name":"Karl"}],"title":"Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding","type":"conference","publication":"2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21)","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"}],"file":[{"content_type":"application/pdf","success":1,"relation":"main_file","date_updated":"2021-10-04T10:43:19Z","creator":"hafifi","date_created":"2021-10-04T10:43:19Z","file_size":534737,"access_level":"closed","file_name":"Preprint___Reinforcement_Learning_for_Dynamic_Resource_Allocation_in_Wireless_Networks.pdf","file_id":"25279"}],"status":"public","project":[{"_id":"27","name":"Akustische Sensornetzwerke - Teilprojekt \"Verteilte akustische Signalverarbeitung über funkbasierte Sensornetzwerke"}],"_id":"25278","user_id":"65718","ddc":["000"],"keyword":["reinforcement learning","admission control","wireless sensor networks"],"file_date_updated":"2021-10-04T10:43:19Z","language":[{"iso":"eng"}]},{"keyword":["admission control","informational uncertainty","revenue management","cloud computing"],"ddc":["000"],"language":[{"iso":"eng"}],"abstract":[{"text":"Cloud computing promises the flexible delivery of computing services in a pay-as-you-go manner. It allows customers to easily scale their infrastructure and save on the overall cost of operation. However Cloud service offerings can only thrive if customers are satisfied with service performance. Allow-ing instantaneous access and flexible scaling while maintaining the service levels and offering competitive prices poses a significant challenge to Cloud Computing providers. Furthermore services will remain available in the long run only if this business generates a stable revenue stream. To address these challenges we introduce novel policy-based service admission control mod-els that aim at maximizing the revenue of Cloud providers while taking in-formational uncertainty regarding resource requirements into account. Our evaluation shows that policy-based approaches statistically significantly out-perform first come first serve approaches, which are still state of the art. Furthermore the results give insights in how and to what extent uncertainty has a negative impact on revenue.","lang":"eng"}],"file":[{"relation":"main_file","content_type":"application/pdf","file_size":1270024,"file_name":"ELSEVIER_JOURNAL_VERSION.pdf","file_id":"6036","access_level":"open_access","date_updated":"2018-12-13T15:09:12Z","date_created":"2018-12-07T11:44:10Z","creator":"hsiemes"}],"publication":"European Journal of Operational Research","title":"Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty","publisher":"Elsevier","date_created":"2018-11-14T15:40:13Z","year":"2015","issue":"2","file_date_updated":"2018-12-13T15:09:12Z","extern":"1","_id":"5679","department":[{"_id":"277"}],"user_id":"61579","status":"public","type":"journal_article","oa":"1","date_updated":"2022-01-06T07:02:30Z","volume":244,"author":[{"last_name":"Püschel","full_name":"Püschel, Tim","first_name":"Tim"},{"last_name":"Schryen","id":"72850","full_name":"Schryen, Guido","first_name":"Guido"},{"full_name":"Hristova, Diana","last_name":"Hristova","first_name":"Diana"},{"first_name":"Dirk","full_name":"Neumann, Dirk","last_name":"Neumann"}],"page":"637-647","intvolume":"       244","citation":{"chicago":"Püschel, Tim, Guido Schryen, Diana Hristova, and Dirk Neumann. “Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-Probabilistic Uncertainty.” <i>European Journal of Operational Research</i> 244, no. 2 (2015): 637–47.","ieee":"T. Püschel, G. Schryen, D. Hristova, and D. Neumann, “Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty,” <i>European Journal of Operational Research</i>, vol. 244, no. 2, pp. 637–647, 2015.","ama":"Püschel T, Schryen G, Hristova D, Neumann D. Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty. <i>European Journal of Operational Research</i>. 2015;244(2):637-647.","mla":"Püschel, Tim, et al. “Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-Probabilistic Uncertainty.” <i>European Journal of Operational Research</i>, vol. 244, no. 2, Elsevier, 2015, pp. 637–47.","short":"T. Püschel, G. Schryen, D. Hristova, D. Neumann, European Journal of Operational Research 244 (2015) 637–647.","bibtex":"@article{Püschel_Schryen_Hristova_Neumann_2015, title={Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty}, volume={244}, number={2}, journal={European Journal of Operational Research}, publisher={Elsevier}, author={Püschel, Tim and Schryen, Guido and Hristova, Diana and Neumann, Dirk}, year={2015}, pages={637–647} }","apa":"Püschel, T., Schryen, G., Hristova, D., &#38; Neumann, D. (2015). Revenue Management for Cloud Computing Providers: Decision Models for Service Admission Control under Non-probabilistic Uncertainty. <i>European Journal of Operational Research</i>, <i>244</i>(2), 637–647."},"has_accepted_license":"1"}]