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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 nearoptimal allocations and has a low execution time.

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Cloud computing makes our life easy by delivering computing resources as utility like telephony, water and gas. In cloud computing users should pay only for what they consumed. Nowadays, cloud service providers deliver a huge number of cloud services with almost the same features which makes the cloud services discovery and selection process as a big challenge for the end consumers. Using existing search engines results in a lot of unrelated outcome which increases the cloud service discovery and selection process time and effort. In this paper, we present an enhanced cloud services marketplace framework to facility the cloud services trading between providers and consumers and to make could services more visible for all consumers. Proposed framework receives users' requests as a voice commands or flat-text then translates them based on Natural Language Understanding technologies. In additional, we enhanced the matching algorithm by adding different weights for attributes based on consumer preferences. Experiments showed an enhancement in the overall user experience and better matching for user request.

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