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In today’s world migration of people from rural areas to urban areas is quite common. Health care services are one of the most challenging aspect that is must require to the people with abnormal health. Advancements in the technologies lead to build the smart homes, which contains various sensor or smart meter devices to automate the process of other electronic device. Additionally these smart meters can be able to capture the daily activities of the patients and also monitor the health conditions of the patients by mining the frequent patterns and association rules generated from the smart meters. In this work we proposed a model that is able to monitor the activities of the patients in home and can send the daily activities to the corresponding doctor. We can extract the frequent patterns and association rules from the log data and can predict the health conditions of the patients and can give the suggestions according to the prediction. Our work is divided in to three stages. Firstly, we used to record the daily activities of the patient using a specific time period at three regular intervals. Secondly we applied the frequent pattern growth for extracting the association rules from the log file. Finally, we applied kmeans clustering for the input and applied Bayesian network model to predict the health behavior of the patient and precautions will be given accordingly.

Keywords

Bayesian Networks, Cluster Analysis, FP Pattern, Human Activity Prediction.

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Optimal Resource Allocation and Reservation using DAR in Large Scale Applications

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Authors

G. Rama Subba Reddy ¹

Affiliations
1 Dept. of CSE, Mother Theresa Institute of Engineering & Technology, Palamaner, Andhra Pradesh, IN

Source

International Journal of Advanced Networking and Applications, Vol 10, No 2 (2018), Pagination: 3822-3828

Abstract

In the current IT industry, big data analytics and Cloud Computing are the two most basic advancements. Amazingly these two innovations are come up together to give the best outcomes for different multinational business companies. In the former case, it requires huge amount of resources such as memory or hardware to store, process and other kinds of big data analytics. The cost to store this data is greatly expanded and requires innovative algorithms to reduce this complexity and this will be easy to process less information using machine learning algorithms. Distributed applications are using cloud service providers (i.e. Amazon AWS) to host and process this data with different cost to meet service level agreements. However, the customers are interested in reliable SLAs with minimized cost to store and process their data. The data centers maintained at different locations throughout the world are giving services with different get/put latencies. Allocation of data to multiple data centers and resource reservation are the two primary issues and yet to be solved. In this work, we proposed a method to reduce the cost by meeting the SLOs with integer programming. Also, we proposed an efficient method to store the data files by the optimal selection by minimizing the cost along with resource reservation. Our experimental study shows that our technique is giving the best result by selecting the optimal selection of data center along with resource reservation and its effective utilization.

Keywords

Big Data, CSP, Resource Reservation, Optimal Selection, SLOs.

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