Auction designs have recently been adopted for static and dynamic resource provisioning in IaaS clouds, such as Microsoft Azure and Amazon EC2. However, the existing mechanisms are mostly restricted to simple auctions, single-objective, offline setting, one-sided interactions either among cloud users or cloud service providers (CSPs), and possible misreports of cloud user’s private information. This paper proposes a more realistic scenario of online auctioning for IaaS clouds, with the unique characteristics of elasticity for time-varying arrival of cloud user requests under the time-based server maintenance in cloud data centers. We propose an online truthful double auction technique for balancing the multi-objective trade-offs between energy, revenue, and performance in IaaS clouds, consisting of a weighted bipartite matching based winning-bid determination algorithm for resource allocation and a Vickrey–Clarke–Groves (VCG) driven algorithm for payment calculation of winning bids. Through rigorous theoretical analysis and extensive trace-driven simulation studies exploiting Google cluster workload traces, we demonstrate that our mechanism significantly improves the performance while promising truthfulness, heterogeneity, economic efficiency, individual rationality, and has a polynomial-time computational complexity.

最近，拍卖设计已被用于 IaaS 云中的静态和动态资源配置，例如 Microsoft Azure 和 Amazon EC2。然而，现有机制大多局限于简单的拍卖、单一目标、离线设置、云用户或云服务提供商（CSP）之间的单方面交互，以及可能误报云用户的私人信息。本文提出了一种更现实的 IaaS 云在线拍卖场景，在云数据中心基于时间的服务器维护下，具有云用户请求时变到达的弹性的独特特性。我们提出了一种在线真实双重拍卖技术，用于平衡 IaaS 云中能源、收入和性能之间的多目标权衡，由用于资源分配的基于加权二分匹配的中标确定算法和用于中标支付计算的 Vickrey-Clarke-Groves (VCG) 驱动算法组成。通过严格的理论分析和利用 Google 集群工作负载跟踪的广泛跟踪驱动的模拟研究，我们证明了我们的机制显着提高了性能，同时保证了真实性、异质性、经济效率、个体理性，并且具有多项式时间的计算复杂性。