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Game theory-based optimization of distributed idle computing resources in cloud environments

机译：基于云环境的博弈论优化云环境中分布式空闲计算资源

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With the rapid cloud technological advancement and economic growth, more and more organizations have purchased cloud resources for daily business operations besides building their own IT infrastructures. Thus, it is very important to understand the economy of cloud computing. In this paper, we mainly focus on examining the private idle computing resources owned by various organizations who are willing to form a network of ad hoc cloud provider and sell the services to cloud users. In such a case, the organizations cannot only meet their own demands, but also sell their idle computing resources in the form of ad hoc cloud. Naturally, the organizations, as provider, aim at maximizing their own profit through adjusting business costs and sale prices. Due to the uncertainty of the amount of idle computing resources, dynamic pricing is challenging. We approach the problem from the perspective of game theory and formulate it as a non-cooperative game among multiple organizations, i.e., the game player. For each player, a utility function is used to represent its profits. The players choose request strategies and sales service strategies to maximize the utility function. This paper has proved that there exists Nash equilibrium for this game problem. We proposed an iterative proximal algorithm (IPA) for calculating the Nash equilibrium. After analyzing the convergence of the IPA, we found that the algorithm converges to the Nash equilibrium solution when reasonable conditions are satisfied and conforms to the theoretical proof. Experimental results show that our proposed algorithm can quickly converge to a stable state, and by calculating the appropriate service (resource) request strategies and selling service strategies for all organizations, organizations' profit are increased compared to without IPA algorithm. (C) 2019 Elsevier B.V. All rights reserved.
机译：随着云技术进步和经济增长的快速，越来越多的组织除了构建自己的IT基础架构外，还购买了日常业务运营的云资源。因此，了解云计算的经济非常重要。在本文中，我们主要专注于检查愿意形成临时云提供商网络的各种组织所拥有的私人空闲计算资源，并将服务销售给云用户。在这种情况下，组织不能满足自己的需求，而且还以ad hoc云的形式出售他们的空闲计算资源。当然，组织为提供商，旨在通过调整业务成本和销售价格来最大化自己的利润。由于空闲计算资源量的不确定性，动态定价是具有挑战性的。我们从博弈论的角度致力于解决问题，并将其作为多个组织中的非合作游戏，即，游戏玩家。对于每个玩家，使用实用程序函数来表示其利润。玩家选择请求策略和销售服务策略，以最大限度地提高实用程序功能。本文证明了这种游戏问题存在纳什均衡。我们提出了一种迭代近端算法（IPA），用于计算纳什均衡。在分析IPA的收敛之后，我们发现当满足合理条件并符合理论证明时，该算法会聚到纳什均衡解决方案。实验结果表明，我们所提出的算法可以迅速收敛到稳定状态，通过计算适当的服务（资源）要求策略和销售所有组织的服务策略，而组织的利润与没有IPA算法相比增加。 （c）2019 Elsevier B.v.保留所有权利。

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