The on-demand provisions of cloud services create a service market, where users can dynamically select services based on such attractive criteria as price and quality. An intuitive model of a service market is a reverse auction. In the first price auction, however, a service that is cheaper and provides better quality is not necessarily selected. This causes undesirable outcomes both for users and service providers. A possible solution is the Vickrey-Clarke-Groves (VCG) mechanism, where the dominant strategy for a service provider is to report the true cost of his service. In spite of this desirable property, implementing the VCG mechanism for service composition suffers from computational cost. The calculation of payments to service providers based on the VCG mechanism requires iterative service selection, even though each service selection can be NP-hard. Approximation algorithms cannot be applied because approximate solutions do not assure the desirable property of the VCG mechanism. Thus, we model VCG payments for service markets and propose a dynamic programming (DP)-based algorithm for service selection and VCG payment calculation. Our proposed algorithm solves service selection in quasi-polynomial time and gives an exact solution. Moreover, we extend it and focus on the iterative service selection process for VCG payment calculation to improve its performance. Our series of experiments show that our proposed algorithm solves practical scale service composition.
['Masahiro Tanaka', 'Yohei Murakami']
Strategy-Proof Pricing for Cloud Service Composition