SLA based healthcare big data analysis and computing in cloud network

Sahoo, Prasan Kumar; Mohapatra, Suvendu Kumar; Wu, Shih‐Lin

doi:10.1016/j.jpdc.2018.04.006

Cited by 40 publications

(19 citation statements)

References 24 publications

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“…Sahoo et al [42] proposed a Service Level Agreement (SLA) based Healthcare Big Data Analytic (SLA-HBDA) architecture to perform the ranking of patients data, which improves its pro-cessing speed. Further, an efficient data distribution technique is developed to allocate batch and streaming data using Spark platform to predict the health status of the patient.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

495

248

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Cloud computing provides resources over the Internet and allows a plethora of applications to be deployed to provide services for different industries. The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute environments. The main reason for this is that latency-sensitive applications like health monitoring and surveillance systems now require computation over large amounts of data (Big Data) transferred to centralized database and from database to cloud data centers which leads to drop in performance of such systems. The new paradigms of fog and edge computing provide innovative solutions by bringing resources closer to the user and provide low latency and energy efficient solutions for data processing compared to cloud domains. Still, the current fog models have many limitations and focus from a limited perspective on either accuracy of results or reduced response time but not both. We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. HealthFog delivers healthcare as a fog service using IoT devices and efficiently manages the data of heart patients, which comes as user requests. Fog-enabled cloud framework, FogBus is used to deploy and test the performance of the proposed model in terms of power consumption, network bandwidth, latency, jitter, accuracy and execution time. HealthFog is configurable to various operation modes which provide the best Quality of Service or prediction accuracy, as required, in diverse fog computation scenarios and for different user requirements.

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Section: Related Workmentioning

confidence: 99%

“…The performance of CBFA is evaluated using iFogSim simulator [44] in terms of only latency. Research work [39,41,42,43] developed general healthcare applications at small scale and none of the work focused on heart patientbased healthcare application to diagnose the health status of heart patients.…”

Section: Related Workmentioning

confidence: 99%

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Tuli

Basumatary

Gill

et al. 2020

Future Generation Computer Systems

495

248

View full text Add to dashboard Cite

show abstract

“…The foremost focus of the proposed framework is to ensure that there is a minimization of SLA violation in the IoT-cloud based health care system. The performance of the proposed framework is compared with the existing Parallel Semi Naïve Bayes model (PSNB) [21] based on the priorities of jobs which can have low, medium and high categories. The Tab.…”

Section: Sla Violationsmentioning

confidence: 99%

Service Level Agreement Based Secured Data Analytics Framework for Healthcare Systems

Benila¹,

Bhanu²

2022

Intelligent Automation &Amp; Soft Computing

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“…These predictions are based on SLOs, but this later project is limited by the lack of dynamic control of the SLA. In addition, the research project [5] indicated that: 'SLA based healthcare big data analysis and computing in cloud network' is proposed by combining big data analysis techniques with data collected from patients'. SLA according to Sahoo [5]: 'A PSNB method is proposed for big data analysis of healthcare and a CAM algorithm is also designed for dimension reduction to improve the accuracy of the PSNB algorithm.…”

Section: Related Workmentioning

confidence: 99%

Design an Adaptive Mechanism Model for Supporting the Prevention of SLAs Violation in a Context of COPD Patient Monitoring

Kouamé¹,

Mcheick²,

Ajami³

2020

Preprint

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During recent decades, contextual computing applications have emerged in the field of healthcare and particularly in the field of telemonitoring of patients suffering from chronic obstructive pulmonary disease (COPD). According to WHO rankings, chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Various research works are therefore carried out to improve the health of patients and the monitoring of patients in the comfort of their home environment. To this end, several telemonitoring systems are designed to COPD patients. These systems are connected to health center. Emergency physicians follow the patients subscribed to these systems remotely. These systems focus mainly on prediction, decision-making and the requirements of the healthcare profession, and do not address the quality control aspects of services or QoS based on service level agreements (SLAs). This situation can be dangerous for patients in case of extreme exacerbation of COPD patients. For example, the unavailability of the monitoring system can lead to the death of the patient because the emergency physician could not have access to the patient's data in real time in the context of COPD Patient Monitoring. In addition, Remote medical monitoring platforms are manipulating large volumes of data and the risks of data lost or data quality are real. It is therefore important to have the mechanisms to continuously improve the quality of service of these monitoring platforms in general and COPD patients particularly. In this article, we propose an ontology that uses SLA information from COPD monitoring platforms with dynamic data from the patient context. The purpose of this article is to propose a dynamic mechanism model for evaluating SLA violations. This solution allows retrieving knowledge from the main items of the SLA document based on XML and the COPD patient context data dynamically from a COPD SLA ontology. These data retrieved in real time allow the calculation of SLO-based metrics and display a SLA template available on the supplier and consumer interfaces. The information of the SLA violation control Interface changes dynamically depending the context-aware system and SLA document data. The SLA parties can dynamically control their Key Performance Indicators (KPI) Target.

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SLA based healthcare big data analysis and computing in cloud network

Cited by 40 publications

References 24 publications

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments

Service Level Agreement Based Secured Data Analytics Framework for Healthcare Systems

Design an Adaptive Mechanism Model for Supporting the Prevention of SLAs Violation in a Context of COPD Patient Monitoring

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