With the rapidly growing demand for the cloud services, a need for efficient methods to trade computing resources increases. Commonly used fixed-price model is not always the best approach for trading cloud resources, because of its inflexible and static nature. Dynamic trading systems, which make use of market mechanisms, show promise for more efficient resource allocation and pricing in the cloud. However, most of the existing mechanisms ignore the seller's costs of providing the resources. In order to address it, we propose a single-sided market mechanism for trading virtual machine instances in the cloud, where the cloud provider can express the reservation prices for traded cloud services. We investigate the theoretical properties of the proposed mechanism and prove that it is truthful, i.e. the buyers do not have an incentive to lie about their true valuation of the resources. We perform extensive experiments in order to investigate the impact of the reserve price on the market outcome. Our experiments show that the proposed mechanism yields near optimal allocations and has a low execution time.
This paper presents Cloud Bay, an online resource trading and leasing platform for multi-party resource sharing. Following a market-oriented design principle, Cloud Bay provides an abstraction of a shared virtual resource space across multiple administration domains, and features enhanced functionalities for scalable and automatic resource management and efficient service provisioning. Cloud Bay distinguishes itself from existing research and contributes in a number of aspects. First, it leverages scalable network virtualization and self-configurable virtual appliances to facilitate resource federation and parallel application deployment. Second, Cloud Bay adopts an eBay-style transaction model that supports differentiated services with different levels of job priorities. For cost-sensitive users, Cloud Bay implements an efficient matchmaking algorithm based on auction theory and enables opportunistic resource access through preemptive service scheduling. The proposed Cloud Bay platform stands between HPC service sellers and buyers, and offers a comprehensive solution for resource advertising and stitching, transaction management, and application-to-infrastructure mapping. In this paper, we present the design details of Cloud Bay, and discuss lessons and challenges encountered in the implementation process. The proof-of-concept prototype of Cloud Bay is justified through experiments across multiple sites and simulations. On account of the resource characteristics under cloud computing environment and the flexibility and availability of applying economic mechanism to resource allocation, a resource allocation model based on the limited English combinatorial auction under cloud computing environment is advanced. An improved periodical auction model is constructed, and then ribbon capacity is adopted to describe the special storage capacity for special users. Based on the above, the resource trading price between resource buyers and resource providers is determined based on the limited English combinatorial auction model. In the end, the optimal resource allocation solution is pursued based on genetic algorithm. Simulation results have shown that the proposed model is both feasible and effective, which can maximize the seller total trading amounts as well as reduce the executing time of winner determination.