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Nowadays, cloud computing becomes emerging technology, where any kind of data can be shared. Software and resources can be offered based on the requirement of customers. Effective resource allocation is a challengeable task in a Cloud environment. Cloud resource providers have sufficient resources which should be efficiently allocated. Otherwise, a problem will occur in underutilization devices. The main difficulty is to identify an appropriate cloud resource provider for each cloud user who could sell various resources in minimum possible cost according to their requirements and simultaneously find suitable cloud user for a cloud service provider who values his resources at the most. To overcome the above issues, Efficient Resource Utilization Auction Method is designed to solve resource allocation issues by using dynamic cost model and effective resource utilization for cost resources. The method offers one type of safe auction process between resource provider and user to grow their business mutually in win-win condition. Where, cloud user looks to get actual requirement with an affordable cost; simultaneously cloud resource provider also looks to generate good revenue and meaningful profit. The method fascinates the cloud resource provider to decide the resource allocation for the submitted virtual machine (VM) to the physical machine (PMs). The proposed method works based on a dynamic economic auction model. Based on the experimental evaluation, proposed Efficient Resource Utilization Auction Method improves the 18% resource utilization (RU), and reduces 20 seconds task completion time (TCT) compare than conventional methodologies. [ABSTRACT FROM AUTHOR]

The growth of IT industry technology is absorbed by the cloud service technology, which leads to secure connectivity and availability of services to cloud users. This paper proposes an e-health data classification system that gives the services to e-health cloud user for their disease prediction requirements. The tree bagging method is suitable to select better weighted attributes and the careful seeding of K-means ++ algorithm improves the accuracy and speed of clustering. Most of the tree classification methods use only information gain as the strategy to select suitable attributes for classification. We used information gain in bagging technique to improve accuracy. In this research article, we proposed a method to Coalescing Decision tree classification algorithm, bagging technique, and K-means++ algorithm to build a better classifier for the e-health cloud users. It was evaluated with the standard data sets such as Diabetes, breast cancer, liver disorders and cardiotocography. [ABSTRACT FROM AUTHOR]