Authors:
Pankaj Mishra
1
;
2
and
Ahmed Moustafa
1
Affiliations:
1
Department of International Collaborative Informatics, Nagoya Institute of Technology, Gokiso, Naogya, Japan
;
2
School of Computing and Information Technology, University of Wollongong, Wollongong, Australia
Keyword(s):
Resource Matching, Reinforcement Learning, Fairness, Online Markets, Contest Success Function.
Abstract:
Designing a resource matching policy in an open market paradigm is a challenging and complex problem. The
complexity is mainly due to the conflicting objectives of the independent resource providers and dynamically
arriving online buyers. In specific, providers aim to maximise their revenue, whereas buyers aim to minimise
their resource costs. Therefore, to address this complex problem, there is an immense need for a fair matching
platform. In specific, the platform must optimise the pricing rule on behalf of resource providers to maximise
their revenue at one end. Then, on the other hand, the broker must fairly match the resource request on behalf
of buyers. Owing to this we propose a two-step unbiased broker based resource matching mechanism in
the auction paradigm. In the first step, the broker computes optimal trade prices on behalf of the providers
using a novel reinforcement learning algorithm. Then, in the second step appropriate provider is matched
with the buyer’s r
equest based on a novel multi-criteria winner determination strategy. Towards the end, we
compare our online resource matching approach with two existing state-of-the-art algorithms. Then, from the
experimental results, we show that the novel matching algorithm outperforms the other two baselines.
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