Abstract
Background/Objective: To provide an efficient predictive technique to foresee future workload as well as to handle the resources efficiently by performing hybrid auto scaling for Cloud applications. Cloud applications might expertise completely different workload at different times, automatic provisioning has to work with efficiency at any point of time. Auto scaling is a feature of cloud computing that potentially scale the resources in line on demand. Considering this expectation, they are generally categorized into Reactive scaling which adds or reduces resources based on a fixed threshold value. The predictive scaling is used provide necessary scaling actions beforehand. Methods/Statistical Analysis: To perform the hybrid auto scaling (reactive plus predictive auto scaling), a time series technique should be used. Auto-regressive Moving Average (ARMA) model, the Exponential Smoothing (ES) model, the Autoregressive model (AR), the Moving Average model (MA) and the Trend- Adjusted Exponential Smoothing (TAES), Auto Regressive Integrated Moving Average (ARIMA) Time-series model, NaΓ―ve bayes algorithm, Recurrent Neural Network- Long Short Term Memory (RNN-LSTM), Independent Recurrent Neural Network (IndRNN) are time series techniques used to foresee the future workload. To find the effectiveness of predictive techniques, Mean Absolute Error (MAE), Mean Squared Error (MSE) and Mean Absolute Percentage Error (MAPE) performance metrics are evaluated. Findings: Based on the evaluation, IndRNN gives the minimum error rate. IndRNN is used to predict the future resource requisites in order to ascertain adequate resource are available ahead of time. Application: The predicted result from IndRNN method is integrated on private cloud to autoscale the resources for cloud applications. Keywords: Abnormality, Cloud, Health Monitoring, Raspberry Pi, Sensor, Smart Mirror, Two Way Mirror
Highlights
There are different techniques for health monitoring
This paper introduces an effective health monitoring system using mirrors which is a device used by all kind of people irrespective of their ages
In3 tells about smart mirror which can detect and monitor facial signs over time correlating them with cardio-metabolic risk and thereby providing guidance to users on how to improve their habits
Summary
There are different techniques for health monitoring. Different types of sensors are used for such purposes. The technology changed to which the individuals can become aware of their physiological functions using biomedical sensors with the help of computer systems. The old aged people who are unaware of using such devices need more such health monitoring systems. This paper introduces an effective health monitoring system using mirrors which is a device used by all kind of people irrespective of their ages. The biomedical sensors prxesent within the mirror will collect the physiological features and the collected data will be sent to the doctors for further details about the health condition. Because of this technique the doctors can monitor their patientβs health condition remotely
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