Online Combinatorial Auctions for Resource Allocation with Supply Costs and Capacity Limits

TL;DR

This paper introduces an optimal online pricing mechanism for resource allocation in cloud computing, balancing customer valuations and supply costs, with proven competitive ratios and validated empirical performance.

Contribution

It develops a novel online posted-price mechanism with optimal competitive ratios for combinatorial resource auctions considering supply costs and capacity limits.

Findings

The proposed mechanism achieves the best possible competitive ratio.

Empirical results show robustness and superior performance over benchmarks.

Mechanism effectively balances social welfare and supply costs.

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 optimal 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.