A Comparison Between Mechanisms for Sequential Compute Resource Auctions

This paper describes simulations designed to test the relative efficiency of two di®erent sequential auction mechanisms for allocating compute resources between users in a shared data-center. Specifically we model the environment of a data center dedicated to CGI rendering in which animators delegate responsibility for acquiring adequate compute resources to bidding agents that automously bid on their behalf. For each of two possible auction types we apply a genetic algorithm to a broad class of bidding strategies to determine a near-optimal bidding strategy for a specified auction type, and use statistics of the performance of these strategies to determine the most suitable auction type for this domain

E-Business Oriented Optimal Online Auction Design

Online auctions, in the absence of spatial, temporal and geographic constraints, provide an alternative supply chain channel for the distribution of goods and services. This channel differs from the common posted-price mechanism that is typically used in the retail sector. In consumer-oriented markets, buyers can now experience the thrill of ‘winning’ a product, potentially at a bargain, as opposed to the typically more tedious notion of ‘buying’ it. Sellers, on the other hand, have an additional channel to distribute their goods, and the opportunity to liquidate rapidly aging goods at greater than salvage values. The primary facilitator of this phenomenon is the widespread adoption of electronic commerce over an open-source, ubiquitous Internet Protocol (IP) based network. In this paper, we derive an optimal bidding strategy in sequential auctions that incorporates option value assessment. Furthermore, we establish that our optimal bidding strategy is tractable since it is independent of the bidding strategies of other bidders in the current auction and is only dependent on the option value assessmen

Learning optimization models in the presence of unknown relations

In a sequential auction with multiple bidding agents, it is highly challenging to determine the ordering of the items to sell in order to maximize the revenue due to the fact that the autonomy and private information of the agents heavily influence the outcome of the auction. The main contribution of this paper is two-fold. First, we demonstrate how to apply machine learning techniques to solve the optimal ordering problem in sequential auctions. We learn regression models from historical auctions, which are subsequently used to predict the expected value of orderings for new auctions. Given the learned models, we propose two types of optimization methods: a black-box best-first search approach, and a novel white-box approach that maps learned models to integer linear programs (ILP) which can then be solved by any ILP-solver. Although the studied auction design problem is hard, our proposed optimization methods obtain good orderings with high revenues. Our second main contribution is the insight that the internal structure of regression models can be efficiently evaluated inside an ILP solver for optimization purposes. To this end, we provide efficient encodings of regression trees and linear regression models as ILP constraints. This new way of using learned models for optimization is promising. As the experimental results show, it significantly outperforms the black-box best-first search in nearly all settings.Comment: 37 pages. Working pape