Revenue sharing in edge-cloud systems: A Game-theoretic perspective

Abstract We explore the design of revenue sharing mechanisms for an Edge-Cloud computing system from a game-theoretic perspective. Different from traditional cloud computing, the providers in an Edge-Cloud system are independent and self-interested. The system adopts a task distribution mechanism to maximize the total revenue received from clients and employs a revenue sharing mechanism to split the received revenue among service providers. Under system-level mechanisms, service providers game with the system in order to maximize their own utilities by strategically allocating their resources. We introduce a game-theoretic framework to model the competition among the service providers as a non-cooperative game. We show the existence of Nash equilibrium in the game theoretically and experimentally. We find that at Nash equilibria, the revenue sharing design based directly on actual contributions of service providers results in significantly worse system-level performance than revenue sharing mechanisms based on marginal contributions. We find that the reason for this seemingly counter-intuitive result is that revenue sharing mechanisms based on marginal contributions discourage providers with less powerful resources from contributing resources to the system at equilibrium state. Our framework offers an economics approach to the understanding of Edge-Cloud systems and provides fundamental insights into their revenue sharing design.

[1]  Rajkumar Buyya,et al.  Fog Computing: A Taxonomy, Survey and Future Directions , 2016, Internet of Everything.

[2]  Honggang Zhang,et al.  Incentive mechanism for proximity-based Mobile Crowd Service systems , 2016, IEEE INFOCOM 2016 - The 35th Annual IEEE International Conference on Computer Communications.

[3]  K. Michael Ortmann,et al.  The proportional value for positive cooperative games , 2000, Math. Methods Oper. Res..

[4]  Stratis Ioannidis,et al.  Incentivizing peer-assisted services: a fluid shapley value approach , 2010, SIGMETRICS '10.

[5]  Qun Li,et al.  A Survey of Fog Computing: Concepts, Applications and Issues , 2015, Mobidata@MobiHoc.

[6]  Zheng Chang,et al.  Adaptive Service Offloading for Revenue Maximization in Mobile Edge Computing With Delay-Constraint , 2019, IEEE Internet of Things Journal.

[7]  Artur Czumaj,et al.  Multi-player Approximate Nash Equilibria , 2017, AAMAS.

[8]  J. Nash.  NON-COOPERATIVE GAMES , 1951, Classics in Game Theory.

[9]  Dipankar Raychaudhuri,et al.  Scalability and Performance Evaluation of Edge Cloud Systems for Latency Constrained Applications , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[10]  Lei Shu,et al.  Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges , 2018, IEEE Communications Surveys & Tutorials.

[11]  Honggang Zhang,et al.  A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System , 2017, 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC).

[12]  Byoungheon Shin,et al.  Application-Aware IoT Camera Virtualization for Video Analytics Edge Computing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[13]  Roger B. Myerson,et al.  Graphs and Cooperation in Games , 1977, Math. Oper. Res..

[14]  Benyuan Liu,et al.  Pricing and revenue sharing mechanism for secondary redistribution of data service for mobile devices , 2014, 2014 IEEE 33rd International Performance Computing and Communications Conference (IPCCC).

[15]  Aviad Rubinstein,et al.  Settling the Complexity of Computing Approximate Two-Player Nash Equilibria , 2016, 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).

[16]  Peng Liu,et al.  Low-Cost Video Transcoding at the Wireless Edge , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[17]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[18]  Sateesh Addepalli,et al.  Fog computing and its role in the internet of things , 2012, MCC '12.

[19]  Peng Liu,et al.  ParaDrop: Enabling Lightweight Multi-tenancy at the Network’s Extreme Edge , 2016, 2016 IEEE/ACM Symposium on Edge Computing (SEC).

[20]  Zhenyu Wen,et al.  ApproxIoT: Approximate Analytics for Edge Computing , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[21]  Donald F. Towsley,et al.  TCP connection game: a study on the selfish behavior of TCP users , 2005, 13TH IEEE International Conference on Network Protocols (ICNP'05).

[22]  T. Başar,et al.  Dynamic Noncooperative Game Theory , 1982 .

[23]  John N. Tsitsiklis,et al.  Efficiency loss in a network resource allocation game: the case of elastic supply , 2004, IEEE Transactions on Automatic Control.

[24]  Zhuo Chen,et al.  Bandwidth-Efficient Live Video Analytics for Drones Via Edge Computing , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[25]  Vishal Misra,et al.  Internet economics: the use of Shapley value for ISP settlement , 2010, TNET.

[26]  Aakanksha Chowdhery,et al.  The Design and Implementation of a Wireless Video Surveillance System , 2015, MobiCom.

[27]  Aniruddha S. Gokhale,et al.  Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[28]  L. Shapley.  A Value for n-person Games , 1988 .

[29]  Weisong Shi,et al.  Edge Computing: Vision and Challenges , 2016, IEEE Internet of Things Journal.

[30]  Max Mühlhäuser,et al.  From Cell Towers to Smart Street Lamps: Placing Cloudlets on Existing Urban Infrastructures , 2018, 2018 IEEE/ACM Symposium on Edge Computing (SEC).

[31]  Paul W. Goldberg,et al.  The Complexity of Computing a Nash Equilibrium , 2009, SIAM J. Comput..

[32]  Mahadev Satyanarayanan,et al.  Towards wearable cognitive assistance , 2014, MobiSys.

[33]  Xiaotie Deng,et al.  Settling the complexity of computing two-player Nash equilibria , 2007, JACM.

[34]  H. Vincent Poor,et al.  An Optimal Auction Mechanism for Mobile Edge Caching , 2018, 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS).

[35]  Aviad Rubinstein.  Inapproximability of Nash Equilibrium , 2018, SIAM J. Comput..