云联盟中的虚拟机交易：个体利益与社会福利最大化
Virtual Machine Trading in a Federation of Clouds: Individual Profit and Social Welfare Maximization
摘要：
通过在不同的云提供商之间共享资源，联合云的范例利用了资源的时间可用性和运营成本的地理多样性，以实现高效的作业服务。虽然云联盟中跨不同云平台的互操作性问题已经得到了广泛的研究，但云经济学的基本问题仍然存在：云应该何时以及如何交易资源（例如，虚拟机），使其净利润在长期内最大化，同时还可以保证整个联邦接近最佳的社会福利？为了回答这个问题，一些重要的，相互关联的决策，包括作业调度，服务器供应和资源定价，应动态和联合，同时追求长期利润最优。在这项工作中，我们设计了有效的算法，云联盟中的云间虚拟机（VM）的交易和调度。对于云之间的虚拟机事务，我们设计了一个基于双重拍卖的机制，该机制是防策略的，个体理性的，事后预算平衡的，并且随着时间的推移有效地执行。与拍卖机制紧密结合的是一个动态的虚拟机交易和调度算法，它仔细决定拍卖中的虚拟机的真实估值，最优地调度具有不同SLA的随机作业到达虚拟机，并根据当前的电价明智地打开和关闭服务器。通过严格的分析，我们表明，每一个单独的云，通过执行动态算法在网上双重拍卖，可以实现时间平均利润任意接近离线最优。社会福利的渐近最优性也在同质云设置下实现。我们进行跟踪驱动的模拟，以检查我们的算法的有效性和异构云环境下可实现的社会福利。
英文摘要：
By sharing resources among different cloud providers, the paradigm of federated clouds exploits temporal availability of resources and geographical diversity of operational costs for efficient job service. While interoperability issues across different cloud platforms in a cloud federation have been extensively studied, fundamental questions on cloud economics remain: When and how should a cloud trade resources (e.g., virtual machines) with others, such that its net profit is maximized over the long run, while a close-to-optimal social welfare in the entire federation can also be guaranteed? To answer this question, a number of important, inter-related decisions, including job scheduling, server provisioning and resource pricing, should be dynamically and jointly made, while the long-term profit optimality is pursued. In this work, we design efficient algorithms for inter-cloud virtual machine (VM) trading and scheduling in a cloud federation. For VM transactions among clouds, we design a double-auction based mechanism that is strategyproof, individual rational, ex-post budget balanced, and efficient to execute over time. Closely combined with the auction mechanism is a dynamic VM trading and scheduling algorithm, which carefully decides the true valuations of VMs in the auction, optimally schedules stochastic job arrivals with different SLAs onto the VMs, and judiciously turns on and off servers based on the current electricity prices. Through rigorous analysis, we show that each individual cloud, by carrying out the dynamic algorithm in the online double auction, can achieve a time-averaged profit arbitrarily close to the offline optimum. Asymptotic optimality in social welfare is also achieved under homogeneous cloud settings. We carry out trace-driven simulations to examine the effectiveness of our algorithms and the achievable social welfare under heterogeneous cloud settings.
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