Cloud computing emerging environment attracts many applications providers to deploy web applications on cloud data centers. The primary area of attraction is elasticity, which allows to auto-scale the resources on-demand. However, web applications usually have dynamic workload and hard to predict. Cloud service providers and researchers are working to reduce the cost while maintaining the Quality of Service (QoS). One of the key challenges for web application in cloud computing is auto-scaling. The auto-scaling in cloud computing is still in infancy and required detail investigation of taxonomy, approach and types of resources mapped to the current research. In this article, we presented the literature survey for auto-scaling techniques of web applications in cloud computing. This survey supports the research community to find the requirements in auto-scaling techniques. We present a taxonomy of reviewed articles with parameters such as auto-scaling techniques, approach, resources, monitoring tool, experiment, workload, and metric, etc. Based on the analysis, we proposed the new areas of research in this direction.
Cardiovascular disease is a term used to describe a variety of heart diseases, illnesses, and events that impact the heart and circulatory system. A clinician uses several sources of data and tests to make a diagnostic impression but it is not necessary that all the tests are useful for the diagnosis of a heart disease. The objective of our work is to reduce the number of attributes used in heart disease diagnosis that will automatically reduce the number of tests which are required to be taken by a patient. Our work also aims at increasing the efficiency of the proposed system. The observations illustrated that Decision Tree and Naive Bayes using fuzzy logic has outplayed over other data mining techniques.
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