The rapidly growing cloud-centric focuses among organizations and enterprises have thrown into sharp relief the pressing need to progressively upgrade security within a cloud infrastructure. While a significant portion of threat perception emanates from malicious software that can infiltrate a cloud computing infrastructure, the risk of unlicensed software deployments in infrastructure-as-a-service model can expose an affected cloud service provider to a host of legal and statutory issues. The task of managing software licenses and conducting license validation checks can be an intense activity. The onus of tracking and managing licenses of software that have been deployed on an infrastructure-as-a-service cloud model is on both cloud service provider as well as its customers. However, the business of effectively managing software licenses is contingent upon the ease and use of an effective license auditing management tool to which auditors can connect through a secure conduit. In this paper, we propose a model that would allow cloud service providers to securely track software deployments and tally corresponding usage of appropriate licenses within an infrastructure-as-a-service model. Using the proposed model a cloud service provider can match all software licenses that have been deployed by a customer within the IaaS cloud space allotted. Further, a license auditor can securely connect to cloud service provider’s console and generate reports on license usage and software deployments from any modern browser. At the core of our model, lies a secure loopback connection framework that prevents access to any other web site once a secure connection to the primary console for monitoring and tracking software deployments has been established.
Different e-commerce companies try to maintain high importance for their customer satisfactions. It helps them to understand the performance of their products. Nowadays customers trust on the product reviews while shipping online. But it is a cumbersome task to handle millions of customer reviews within specific time period. Due to this problem consumers usually follow the set of reviews before taking decision for purchasing any products from online. Although, each consumer rates the product from 1 to 5 stars, these overall product rating judge products towards their customers satisfaction. Consumers also provide a text based summary as a review of their experiences and opinions about the products. Customer sentiment analysis is a method to process these customer reviews to summarize different products. In this manuscript, we analyzed the text summery of Amazon food products using NRC Emotion Lexicon to determine the overall responses of the products using eight emotions of the customers. Our result can be used to take further decision making for the future of the products.
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