The increasingly growing supply and demand for infrastructure as a service (IaaS) makes cloud trading possible in an open exchange (OCX) marketplace. The mechanisms based on economic principles show promise in addressing the problem of efficient cloud resource provisioning in such a marketplace, including resources allocation and pricing. Therefore, this article proposes effective and efficient economics-inspired double-sided mechanisms, focussing on attaining the allocative efficiency and truthfulness. Given non-deterministic polynomial time (NP) complexity of the considered problem and the computational tractability requirement for the practical solutions, we design and evaluate the approximation mechanisms for such markets. We propose a combinatorial greedy allocation mechanism to determine the distribution of cloud resources based on the sorted order of allocation candidates. We design the pricing mechanisms that derive the buyer prices based on critical-value, and the seller payments are determined via a mixed surplus-distribution rule that relies on direct and proportional-value payment. The theoretical analysis of the economic properties proves that the proposed mechanisms maintain Budget-Balance (BB), Individual Rationality (IR), Computational Tractability (CT), and achieve Truthfulness (T) for single-minded buyers. The experimental investigation of the approximation quality and the seller strategic manipulation reveal near-optimal allocation performance and near-truthful strategic incentive in our pricing mechanisms.

Migration to the cloud offers several benefits including reduced operational costs, flexibility, scalability, and a greater focus on business goals, but it also has a flip side reduced visibility. Organizations only have a blackbox view of cloud servers and while pricing and specification information is publicly available, there is limited information about cloud performance. This necessitates the need for tools that can provide greater visibility into cloud insfrastructure performance so that consumers can objectively compare and contrast the offerings from different providers. Smart CloudBench [1][2][3] is a system that allows users to run automated, ondemand, real-time and customized benchmark tests on cloud infrastructure. In this paper, we present Smart CloudMonitor - a performance monitoring tool that provides multi-layer performance monitoring capabilities to Smart CloudBench. It provides greater visibility and insight into cloud performance by monitoring both application performance as well as the corresponding resource consumption. Experiments conducted on cloud infrastructure using Smart CloudBench show the add value that Smart CloudMonitor provides to the process of cloud performance evaluation.

Cloud resource allocation and pricing is a significant and challenging problem for modern cloud providers, which needs to be addressed. In this work, we propose an adaptive greedy mechanism, which is a new type of greedy market mechanism for efficient cloud resource allocation. The mechanism is combinatorial and it is designed to be operated by a single cloud provider. We prove that our proposed market mechanism is truthful, i.e. the buyers do not have an incentive to lie about their true valuation for the resource. Our experimental investigation showed that the proposed mechanism outperforms the conventional (single-shot) approach for solving combinatorial auction in terms of generated social welfare and resource utilization.

As the rate of cloud computing adoption grows, so does the need for consumption assistance. Enterprises that are looking to migrate their IT systems to the cloud, would like to quickly identify providers that offer resources with the most appropriate pricing and performance levels to match their specific business needs. However, no two vendors offer the same resource configurations, pricing and provisioning models, making the task of selecting appropriate computing resources complex, time-consuming and expensive. In this paper, we present Smart CloudBench - a platform that automates the performance benchmarking of cloud infrastructure, helping potential consumers quickly identify the cloud providers that can deliver the most appropriate price/performance levels to meet their specific requirements. Users can estimate the actual performance of the different cloud platforms by testing representative benchmark applications under representative load conditions. Experimentation using the prototype implementation shows that higher price does not necessarily translate to better or more consistent performance, and benchmarking results can provide more information to help enterprises make better informed decisions.

As the rate of cloud computing adoption grows, so does the need for consumption assistance. Enterprises looking to migrate their IT systems to the cloud require assistance in identifying providers that offer resources with the most appropriate pricing and performance levels to match their specific business needs. In this paper, we present Smart Cloud Broker - a suite of software tools that allows cloud infrastructure consumers to evaluate and compare the performance of different Infrastructure as a Service (IaaS) offerings from competing cloud service providers, and consequently supports selection of the cloud configuration and provider with the specifications that best meet the user's requirements. Using Smart Cloud Broker, prospective cloud users can estimate the performance of the different cloud platforms by running live tests against representative benchmark applications under representative load conditions.