A Fast Graph Neural Network-Based Method for Winner Determination in
Multi-Unit Combinatorial Auctions
- URL: http://arxiv.org/abs/2009.13697v2
- Date: Mon, 21 Dec 2020 15:31:41 GMT
- Title: A Fast Graph Neural Network-Based Method for Winner Determination in
Multi-Unit Combinatorial Auctions
- Authors: Mengyuan Lee, Seyyedali Hosseinalipour, Christopher G. Brinton,
Guanding Yu, and Huaiyu Dai
- Abstract summary: Auction (CA) is an efficient mechanism for resource allocation in different fields, including cloud computing.
The problem of allocating items among the bidders to maximize the auctioneers" revenue is NP-complete to solve and inapproximable.
We propose leveraging machine learning (ML) techniques to develop a novel low-complexity algorithm for solving this problem with negligible revenue loss.
- Score: 44.14410999484577
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The combinatorial auction (CA) is an efficient mechanism for resource
allocation in different fields, including cloud computing. It can obtain high
economic efficiency and user flexibility by allowing bidders to submit bids for
combinations of different items instead of only for individual items. However,
the problem of allocating items among the bidders to maximize the auctioneers"
revenue, i.e., the winner determination problem (WDP), is NP-complete to solve
and inapproximable. Existing works for WDPs are generally based on mathematical
optimization techniques and most of them focus on the single-unit WDP, where
each item only has one unit. On the contrary, few works consider the multi-unit
WDP in which each item may have multiple units. Given that the multi-unit WDP
is more complicated but prevalent in cloud computing, we propose leveraging
machine learning (ML) techniques to develop a novel low-complexity algorithm
for solving this problem with negligible revenue loss. Specifically, we model
the multi-unit WDP as an augmented bipartite bid-item graph and use a graph
neural network (GNN) with half-convolution operations to learn the probability
of each bid belonging to the optimal allocation. To improve the sample
generation efficiency and decrease the number of needed labeled instances, we
propose two different sample generation processes. We also develop two novel
graph-based post-processing algorithms to transform the outputs of the GNN into
feasible solutions. Through simulations on both synthetic instances and a
specific virtual machine (VM) allocation problem in a cloud computing platform,
we validate that our proposed method can approach optimal performance with low
complexity and has good generalization ability in terms of problem size and
user-type distribution.
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