Pricing the Cloud: An Auction Approach

Cloud computing has changed the processing and service modes of information communication technology and has affected the transformation, upgrading and innovation of the IT-related industry systems. The rapid development of cloud computing in business practice has spawned a whole new field of interdisciplinary, providing opportunities and challenges for business management research. One of the critical factors impacting cloud computing is how to price cloud services. An appropriate pricing strategy has important practical means to stakeholders, especially to providers and customers. This study addressed and discussed research findings on cloud computing pricing strategies, such as fixed pricing, bidding pricing, and dynamic pricing. Another key factor for cloud computing is Quality of Service (QoS), such as availability, reliability, latency, security, throughput, capacity, scalability, elasticity, etc. Cloud providers seek to improve QoS to attract more potential customers; while, customers intend to find QoS matching services that do not exceed their budget constraints. Based on the existing study, a hybrid QoS-based pricing mechanism, which consists of subscription and dynamic auction design, is proposed and illustrated to cloud services. The results indicate that our hybrid pricing mechanism has potential to better allocate available cloud resources, aiming at increasing revenues for providers and reducing expenses for customers in practice

Double-sided market mechanism for trading cloud resources

The increasingly growing supply and demand of infrastructure as a service (IaaS) makes cloud trading possible and desirable in open cloud exchange (OCX) marketplaces. The automation of cloud services trading in such marketplaces is an essential next step in the cloud market evolution. It can be realised with automated agent-based marketplaces with proper market mechanisms to effectively and efficiently trade IaaS. Due to the high problem complexity, associated with complex cloud services configurations, the research conducted so far has not resulted in an efficient and effective market allocation schemes for trading large public clouds. In this paper, we address this problem by designing and evaluating the market mechanisms for trading cloud resources. We propose a combinatorial greedy allocation scheme which operates based on sorted order of candidates for allocation. We design the buyers' pricing mechanism which derives the prices based on critical-value and prove that it is truthful. We propose and analyse two mechanisms for seller pricing: proportional-value pricing and buyer-based pricing. We perform extensive experiments in order to investigate the allocative performance and strategic manipulation opportunity of the proposed mechanisms. The results reveal near-optimal allocation quality in majority of market scenarios. Furthermore, we discover that the buyer-based pricing almost completely eliminates strategic misreporting while the proportional-value pricing can be vulnerable to the seller's price overstating strategy