Assessing Teacher’s Performance Evaluation and Prediction Model Using Cloud Computing Over Multi-dimensional Dataset

2021 ◽  
Author(s):  
K. Kavitha
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Prediction Model

scholarly journals RVLBPNN: A Workload Forecasting Model for Smart Cloud Computing

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Yao Lu ◽  
John Panneerselvam ◽  
Lu Liu ◽  
Yan Wu
Keyword(s):  
Neural Network ◽  
Cloud Computing ◽  
Prediction Model ◽  
Prediction Accuracy ◽  
Service Providers ◽  
Cloud Service ◽  
Backpropagation Neural Network ◽  
Environmental Implications ◽  
Cloud Service Providers ◽  
Cloud Workloads

Given the increasing deployments of Cloud datacentres and the excessive usage of server resources, their associated energy and environmental implications are also increasing at an alarming rate. Cloud service providers are under immense pressure to significantly reduce both such implications for promoting green computing. Maintaining the desired level of Quality of Service (QoS) without violating the Service Level Agreement (SLA), whilst attempting to reduce the usage of the datacentre resources is an obvious challenge for the Cloud service providers. Scaling the level of active server resources in accordance with the predicted incoming workloads is one possible way of reducing the undesirable energy consumption of the active resources without affecting the performance quality. To this end, this paper analyzes the dynamic characteristics of the Cloud workloads and defines a hierarchy for the latency sensitivity levels of the Cloud workloads. Further, a novel workload prediction model for energy efficient Cloud Computing is proposed, named RVLBPNN (Rand Variable Learning Rate Backpropagation Neural Network) based on BPNN (Backpropagation Neural Network) algorithm. Experiments evaluating the prediction accuracy of the proposed prediction model demonstrate that RVLBPNN achieves an improved prediction accuracy compared to the HMM and Naïve Bayes Classifier models by a considerable margin.

QoS in the Mobile Cloud Computing Environment

2014 ◽  
pp. 200-217 ◽  
Author(s):  
Zhefu Shi ◽  
Cory Beard
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Mobile Cloud Computing ◽  
Current Status ◽  
Mobile Cloud ◽  
Loss Ratio ◽  
Important Research ◽  
Mobile Environment ◽  
Research Problems

Mobile Cloud Computing (MCC) integrates cloud computing into the mobile environment and overcomes obstacles related to performance (e.g., bandwidth, throughput) and environment (e.g., heterogeneity, scalability, and availability). Quality of Service (QoS), such as end-to-end delay, packet loss ratio, etc., is vital for MCC applications. In this chapter, several important approaches for performance evaluation in MCC are introduced. These approaches, such as Markov Processes, Scheduling, and Game Theory, are the most popular methodologies in current research about performance evaluation in MCC. QoS is special in MCC compared to other environments. Important QoS problems with details in MCC and corresponding designs and solutions are explained. This chapter covers the most important research problems and current status related to performance evaluation and QoS in MCC.

Healthcare SaaS Based on a Data Model With Built-In Security and Privacy

2019 ◽  
pp. 744-759 ◽  
Author(s):  
Ruchika Asija ◽  
Rajarathnam Nallusamy
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Model ◽  
Health Data ◽  
Software As A Service ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Access Controls ◽  
Software And Hardware ◽  
Security Levels

Cloud computing is a major technology enabler for providing efficient services at affordable costs by reducing the costs of traditional software and hardware licensing models. As it continues to evolve, it is widely being adopted by healthcare organisations. But hosting healthcare solutions on cloud is challenging in terms of security and privacy of health data. To address these challenges and to provide security and privacy to health data on the cloud, the authors present a Software-as-a-Service (SaaS) application with a data model with built-in security and privacy. This data model enhances security and privacy of the data by attaching security levels in the data itself expressed in the form of XML instead of relying entirely on application level access controls. They also present the performance evaluation of their application using this data model with different scaling indicators. To further investigate the adoption of IT and cloud computing in Indian healthcare industry they have done a survey of some major hospitals in India.

Sign in / Sign up

Export Citation Format

Share Document