Issue No. 02 - April (2017 vol. 25)
Ruiting Zhou , University of Calgary, Calgary, Canada
Zongpeng Li , University of Calgary, Calgary, Canada
Chuan Wu , University of Hong Kong, Hong Kong
Zhiyi Huang , University of Hong Kong, Hong Kong
This paper studies the cloud market for computing jobs with completion deadlines, and designs efficient online auctions for cloud resource provisioning. A cloud user bids for future cloud resources to execute its job. Each bid includes: 1) a utility, reflecting the amount that the user is willing to pay for executing its job and 2) a soft deadline, specifying the preferred finish time of the job, as well as a penalty function that characterizes the cost of violating the deadline. We target cloud job auctions that executes in an online fashion, runs in polynomial time, provides truthfulness guarantee, and achieves optimal social welfare for the cloud ecosystem. Towards these goals, we leverage the following classic and new auction design techniques. First, we adapt the posted pricing auction framework for eliciting truthful online bids. Second, we address the challenge posed by soft deadline constraints through a new technique of compact exponential-size LPs coupled with dual separation oracles. Third, we develop efficient social welfare approximation algorithms using the classic primal-dual framework based on both LP duals and Fenchel duals. Empirical studies driven by real-world traces verify the efficacy of our online auction design.
Cloud computing, Algorithm design and analysis, Pricing, Processor scheduling, Resource management, IEEE transactions, Decision making
R. Zhou, Z. Li, C. Wu and Z. Huang, "An Efficient Cloud Market Mechanism for Computing Jobs With Soft Deadlines," in IEEE/ACM Transactions on Networking, vol. 25, no. 2, pp. 793-805, 2017.