Paper Info
Title
Online Combinatorial Auctions for Resource Allocation With Supply Costs and Capacity Limits
Abstract
We study a general online combinatorial auction problem in algorithmic mechanism design. A provider allocates multiple types of capacity-limited resources to customers that arrive in a sequential and arbitrary manner. Each customer has a private valuation function on bundles of resources that she can purchase (e.g., a combination of different resources such as CPU and RAM in cloud computing). The provider charges payment from customers who purchase a bundle of resources and incurs an increasing supply cost with respect to the totality of resources allocated. The goal is to maximize the social welfare, namely, the total valuation of customers for their purchased bundles, minus the total supply cost of the provider for all the resources that have been allocated. We adopt the competitive analysis framework and provide posted-price mechanisms with optimal competitive ratios. Our pricing mechanism is <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">optimal</italic> in the sense that no other online algorithms can achieve a better competitive ratio. We validate the theoretic results via empirical studies of online resource allocation in cloud computing. Our numerical results demonstrate that the proposed pricing mechanism is competitive and robust against system uncertainties and outperforms existing benchmarks.
Year
DOI
Venue
2020
10.1109/JSAC.2020.2971810
IEEE Journal on Selected Areas in Communications
Keywords
DocType
Volume
Combinatorial auctions,posted price,resource allocation,mechanism design,online algorithms
Journal
38
Issue
ISSN
Citations 
4
0733-8716
2
PageRank 
References 
Authors
0.38
0
4
Name
Order
Citations
PageRank
Xiaoqi Tan19114.79
Alberto Leon-Garcia21718264.79
Yuan Wu353861.11
Danny H. K. Tsang494595.24