A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques

TL;DR

This paper introduces a regret minimization-based pricing strategy for cloud providers in competitive markets, demonstrating improved profits and strategy efficiency through simulations.

Contribution

It proposes a novel pricing policy using regret minimization in incomplete-information games for cloud markets, with extensive simulation analysis.

Findings

Providers' profits increase significantly with the proposed strategy.

Regret minimization techniques outperform other pricing policies.

The approach enhances return on investment for cloud providers.

Abstract

Cloud computing as a fairly new commercial paradigm, widely investigated by different researchers, already has a great range of challenges. Pricing is a major problem in Cloud computing marketplace; as providers are competing to attract more customers without knowing the pricing policies of each other. To overcome this lack of knowledge, we model their competition by an incomplete-information game. Considering the issue, this work proposes a pricing policy related to the regret minimization algorithm and applies it to the considered incomplete-information game. Based on the competition based marketplace of the Cloud, providers update the distribution of their strategies using the experienced regret. The idea of iteratively applying the algorithm for updating probabilities of strategies causes the regret get minimized faster. The experimental results show much more increase in profits of the providers in comparison with other pricing policies. Besides, the efficiency of a variety of regret minimization techniques in a simulated marketplace of Cloud are discussed which have not been observed in the studied literature. Moreover, return on investment of providers in considered organizations is studied and promising results appeared.