Maximizing Liquidity in Cloud Markets through Standardization of Computational Resources

Abstract

Abstract-Low liquidity in cloud markets can result in market instability and inefficiency, preventing the successful implementation of ubiquitous computing on demand. To circumvent this issue, it has been suggested to channel demand and supply into a limited number of standardized services. These standardized services can even be automatically adapted to user requirements with the goal of continuously improving market performance. In this paper, we focus on answering how many standardized services should be placed in the market. This work is based on a new definition of liquidity for cloud resources, which in turn has been derived from liquidity definitions of financial markets. Using a simulation framework, we evaluate our method for estimating the optimal quantity of standardized services with respect to market liquidity and demonstrate the benefits of this approach in terms of increase in market efficiency and decrease in users' cost of participation in the market. The methods presented in this paper have the potential to be applied in other electronic markets as well.Keywords-Service level agreement, electronic markets, cloud economics, autonomic computing, standardized goods, market modeling, market liquidity, IT services I. INTRODUCTION Common to utility computing, cloud computing and grid computing is that they offer computational resources (e.g., software, hardware and computing platform services) in a manner similar to utilities, such as water, electricity and telephony, without regard to where the services are hosted or how they are delivered [4], [15]. However, the current cloud market is fragmented and static, hindering the paradigm's ability to fulfill its promise of ubiquitous computing on demand and as a commodity. In order to address this issue, electronic markets for trading and/or allocating computational resources (i.e., grid and cloud services) have been proposed [1], [38].In cloud markets, computational services are described by service parameters and quality of service (QoS) objectives. Each setting of a service, i.e., configuration of parameters, desired values of QoS objectives, and combinations of these elements, defines a new service (also called "computational resource"). Due to a great number of available settings, markets suffer from a vast diversity and heterogeneity of computational resources.In addition to this, computational resources are characterized by a high dynamism and a high fungibility. Resource dynamism is a result of a large resource variability, a dynamic user base, diverse user behavior in the market, and the

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