Q-Strategy: Automated Bidding and Convergence in Computational Markets

Agents and market mechanisms are widely elaborated and applied to automate interaction and decision processes among others in robotics, for decentralized control in sensor networks and by algorithmic traders in financial markets. Currently there is a high demand of efficient mechanisms for the provisioning, usage and allocation of distributed services in the Cloud. Such mechanisms and processes are not manually manageable and require decisions taken in quasi real-time. Thus agent decisions should automatically adapt to changing conditions and converge to optimal values. This paper presents a bidding strategy, which is capable of automating the bid generation and utility maximization processes of consumers and providers by the interaction with markets as well as to converge to optimal values. The bidding strategy is applied to the consumer side against benchmark bidding strategies and its behavior and convergence are evaluated in two market mechanisms, a centralized and a decentralized one.