An Online Auction Framework for Dynamic Resource Provisioning in Cloud Computing

Auction mechanisms have recently attracted substantial attention as an efficient approach to pricing and allocating resources in cloud computing. This work, to the authors' knowledge, represents the first online combinatorial auction designed for the cloud computing paradigm, which is general and expressive enough to both: 1) optimize system efficiency across the temporal domain instead of at an isolated time point; and 2) model dynamic provisioning of heterogeneous virtual machine (VM) types in practice. The final result is an online auction framework that is truthful, computationally efficient, and guarantees a competitive ratio ≈ 3.30 in social welfare in typical scenarios. The framework consists of three main steps: 1) a tailored primal-dual algorithm that decomposes the long-term optimization into a series of independent one-shot optimization problems, with a small additive loss in competitive ratio; 2) a randomized subframework that applies primal-dual optimization for translating a centralized cooperative social welfare approximation algorithm into an auction mechanism, retaining the competitive ratio while adding truthfulness; and 3) a primal-dual algorithm for approximating the one-shot optimization with a ratio close to e. We also propose two extensions: 1) a binary search algorithm that improves the average-case performance; 2) an improvement to the online auction framework when a minimum budget spending fraction is guaranteed, which produces a better competitive ratio. The efficacy of the online auction framework is validated through theoretical analysis and trace-driven simulation studies. We are also in the hope that the framework can be instructive in auction design for other related problems.