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Who Benefits from a Multi-Cloud Market? A Trading Networks Based Analysis (2310.12666v1)

Published 19 Oct 2023 in cs.GT

Abstract: In enterprise cloud computing, there is a big and increasing investment to move to multi-cloud computing, which allows enterprises to seamlessly utilize IT resources from multiple cloud providers, so as to take advantage of different cloud providers' capabilities and costs. This investment raises several key questions: Will multi-cloud always be more beneficial to the cloud users? How will this impact the cloud providers? Is it possible to create a multi-cloud market that is beneficial to all participants? In this work, we begin addressing these questions by using the game theoretic model of trading networks and formally compare between the single and multi-cloud markets. This comparson a) provides a sufficient condition under which the multi-cloud network can be considered more efficient than the single cloud one in the sense that a centralized coordinator having full information can impose an outcome that is strongly Pareto-dominant for all players and b) shows a surprising result that without centralized coordination, settings are possible in which even the cloud buyers' utilities may decrease when moving from a single cloud to a multi-cloud network. As these two results emphasize the need for centralized coordination to ensure a Pareto-dominant outcome and as the aforementioned Pareto-dominant result requires truthful revelation of participant's private information, we provide an automated mechanism design (AMD) approach, which, in the Bayesian setting, finds mechanisms which result in expectation in such Pareto-dominant outcomes, and in which truthful revelation of the parties' private information is the dominant strategy. We also provide empirical analysis to show the validity of our AMD approach.

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References (27)
  1. Fixed and market pricing for cloud services. In 2012 Proceedings IEEE INFOCOM Workshops, 157–162.
  2. The Economics of the Cloud. ACM Trans. Model. Perform. Eval. Comput. Syst., 2(4).
  3. Truthful Online Scheduling of Cloud Workloads under Uncertainty. In Proceedings of the ACM Web Conference 2022, WWW ’22, 151–161. New York, NY, USA: Association for Computing Machinery. ISBN 9781450390965.
  4. Competitive Equilibrium and Trading Networks: A Network Flow Approach. Operations Research, 69(1): 114–147.
  5. The Sky Above The Clouds.
  6. Complexity of Mechanism Design. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence, UAI’02, 103–110. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc. ISBN 1558608974.
  7. “Auction Learning as a Two Player Game”: GANs (?) for Mechanism Design. In ICLR Blog Track. Https://iclr-blog-track.github.io/2022/03/25/two-player-auction-learning/.
  8. Optimal Auctions through Deep Learning. In Chaudhuri, K.; and Salakhutdinov, R., eds., Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, 1706–1715. PMLR.
  9. Groves, T. 1973. Incentives in Teams. Econometrica, 41(4): 617–631.
  10. Mean-Field Games for Resource Sharing in Cloud-Based Networks. IEEE/ACM Transactions on Networking, 24(1): 624–637.
  11. Stability and competitive equilibrium in matching markets with transfers. ACM SIGecom Exchanges, 10(3): 29–34.
  12. Matching in networks with bilateral contracts. American Economic Journal: Microeconomics, 4(1): 176–208.
  13. Stability and competitive equilibrium in trading networks. Journal of Political Economy, 121(5): 966–1005.
  14. Chain stability in trading networks. Available at SSRN 3180740.
  15. Supercloud: Opportunities and challenges. ACM SIGOPS Operating Systems Review, 49(1): 137–141.
  16. Economics of a Supercloud. In Proceedings of the 3rd Workshop on CrossCloud Infrastructures & Platforms, CrossCloud ’16. New York, NY, USA: Association for Computing Machinery. ISBN 9781450342940.
  17. Simple Pricing Schemes for the Cloud. ACM Trans. Econ. Comput., 7(2).
  18. Sky computing. IEEE Internet Computing, 13(5): 43–51.
  19. A Game-Theoretical Approach to the Benefits of Cloud Computing. In Vanmechelen, K.; Altmann, J.; and Rana, O. F., eds., Economics of Grids, Clouds, Systems, and Services, 148–160. Berlin, Heidelberg: Springer Berlin Heidelberg. ISBN 978-3-642-28675-9.
  20. Spot pricing in the Cloud ecosystem: A comparative investigation. Journal of Systems and Software, 114: 1–19.
  21. Learning Optimal Redistribution Mechanisms Through Neural Networks. In Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems, 345–353.
  22. Provisioning of ad-supported cloud services: The role of competition. Performance Evaluation, 120: 36–48.
  23. Learning Efficient Truthful Mechanisms for Trading Networks. In Elkind, E., ed., Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23, 2862–2869. International Joint Conferences on Artificial Intelligence Organization. Main Track.
  24. Ostrovsky, M. 2008. Stability in supply chain networks. American Economic Review, 98(3): 897–923.
  25. Auction Learning as a Two-Player Game. In International Conference on Learning Representations.
  26. Sandholm, T. 2003. Automated Mechanism Design: A New Application Area for Search Algorithms. In Proceedings of the 9th International Conference on Principles and Practice of Constraint Programming, CP’03, 19–36. Berlin, Heidelberg: Springer-Verlag. ISBN 9783540202028.
  27. On the Viability of a Cloud Virtual Service Provider. SIGMETRICS Perform. Eval. Rev., 44(1): 235–248.
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