Seminar on Internet Technologies (Summer 2018): Difference between revisions
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{{CourseDetails | {{CourseDetails | ||
|credits= | |credits=5 ECTS (BSc/MSc AI); 4 (ITIS) | ||
|lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] | |lecturer=[http://user.informatik.uni-goettingen.de/~fu Prof. Xiaoming Fu] | ||
|ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao] | |ta=[http://www.net.informatik.uni-goettingen.de/people/Tao_Zhao Tao Zhao] | ||
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| '''Reinforcement Mechanism Design''' | | '''Reinforcement Mechanism Design''' | ||
Mechanism design is a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Reinforcement mechanism design is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. The framework can be applied on many key application scenarios, such as Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, the framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, the frame-work automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that the solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios. | | Mechanism design is a modeling and algorithmic framework to design and optimize mechanisms in dynamic industrial environments where a designer can make use of the data generated in the process to automatically improve future design. Reinforcement mechanism design is rooted in game theory but incorporates recent AI techniques to get rid of nonrealistic modeling assumptions and to make automated optimization feasible. The framework can be applied on many key application scenarios, such as Baidu and Taobao, two of the largest mobile app companies in China. For the Taobao case, the framework automatically designs mechanisms that allocate buyer impressions for the e-commerce website; for the Baidu case, the frame-work automatically designs dynamic reserve pricing schemes of advertisement auctions of the search engine. Experiments show that the solutions outperform the state-of-the-art alternatives and those currently deployed, under both scenarios. | ||
| Basic knowledge of machine learning, deep learning and big data analysis. Familar with mechanism design. | | Basic knowledge of machine learning, deep learning and big data analysis. Familar with mechanism design. | ||
|Bo Zhao (bo.zhao@gwdg.de) | |Bo Zhao (bo.zhao@gwdg.de) | ||
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| '''Deep Learning and Smart City''' | | '''Deep Learning and Smart City (assigned to Jihan Munkar)''' | ||
| Smart city is a hot topic in recent years. And deep learning is another hot topic. However, the application of DL in smart city areas is largely overlooked by the reserach community. The student picking this topic need to read several recent papers about connecting DL to smart city questions. In this process, you will know basic concept, general problems and important approahces in this field. | | Smart city is a hot topic in recent years. And deep learning is another hot topic. However, the application of DL in smart city areas is largely overlooked by the reserach community. The student picking this topic need to read several recent papers about connecting DL to smart city questions. In this process, you will know basic concept, general problems and important approahces in this field. | ||
| Basic machine learning knowledge | | Basic machine learning knowledge | ||
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|'''NFV Frameworks - Insight and Assessment''' | |||
| Focus of this topic is to present a comprehensive study and evaluate at least 2 of the NFV platforms (either the Industry and academic works targeted towards deployment of NFV in Telecommunications, Data Center and Enterprise networks). Understand and Analyze the key aspects of the predominant NFV frameworks, and characterize them in terms of the adopted standards, resource requirements, deployment factors and constraints, performance metrics, support for service function chaining, etc. for deployment of Middleboxes and Network Functions in Telco/ISP/Data Center Networks. Evaluate the key deployment metrics accounting both qualitative and quantitative aspects in deploying and building the NFV solutions. | |||
| Familiarity with SDN, NFV and DPDK (students who have already taken SDN course are preferred). Must have Basic networking knowledge, C/C++ programming, Linux administration. | |||
| [http://www.net.informatik.uni-goettingen.de/people/sameer_kulkarni Sameer Kulkarni] | |||
| [https://www.opnfv.org/] [https://www.usenix.org/system/files/conference/nsdi14/nsdi14-paper-martins.pdf] [http://dl.acm.org/ft_gateway.cfm?id=2940155&ftid=1754642&dwn=1&CFID=919487200&CFTOKEN=31286219] [http://superfluidity.eu/about/research-description] [https://fd.io/] | |||
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| '''Online Pricing Mechanisms for Cloud and Edge Computing''' | |||
| The goal is to present at least two online pricing schemes in cloud computing. Currently, cloud resources are billed hourly basis. These online pricing schemes charge customers based on supply demand at current time instance. | |||
| Familiarity with basic optimization (linear programming) and probability. | |||
| [https://www.net.informatik.uni-goettingen.de/people/abhinandan_s_prasad Abhinandan S Prasad] | |||
|[https://dl.acm.org/citation.cfm?id=3086574] [https://dl.acm.org/citation.cfm?id=3084460] | |||
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