Cloudlet Bazaar面向小型云的动态市场
The Cloudlet Bazaar Dynamic Markets for the Small Cloud
摘要：
最近出现的小型云（SC），无论是在概念上还是在实践中，主要是由与商业云提供商的服务成本和复杂性相关的问题驱动的（例如，亚马逊）采用大规模数据中心。然而，供应链由于其相对稀缺的资源（例如，虚拟机）可能导致客户QoS的潜在降级和收入损失。针对这个问题提出的解决方案建议在竞争的供应链之间共享资源，以减轻可能出现的资源缺乏弹性的问题[1]。基于这一想法，最近的一项工作（[2]）提出了SC-Share，这是一种针对竞争性小型云环境的性能驱动的静态市场模型，可实现有效的市场均衡，共同优化客户QoS满意度和SC收入。然而，一个重要的非显而易见的问题仍然有待回答，没有这个供应链共享市场可能无法保证长期持续-它仍然有可能实现一个稳定的市场有效状态时，供应链资源的供应是动态的性质，并有一个变化的客户需求随着时间的推移？本文研究动态环境下供应链资源共享的有效市场设计问题。我们通过使用经济学中的Arrow和Hurwicz的非均衡过程[3]，[4]以及博弈论中的梯度博弈技术（允许我们迭代收敛于有效和稳定的市场均衡）来肯定地回答我们之前的问题
英文摘要：
The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem faced by the SCs due to their relatively scarce resources (e.g., virtual machines) might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise [1]. Based on this idea, a recent effort ([2]) proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an efficient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important non-obvious question still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market efficient state when the supply of SC resources is dynamic in nature and there is a variation of customer demand over time? In this paper, we address the problem of efficient market design for SC resource sharing in dynamic environments. We answer our previous question in the affirmative through the use of Arrow and Hurwicz's disequilibrium process [3], [4] in economics, and the gradient play technique in game theory that allows us to iteratively converge upon efficient and stable market equilibria
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
