Live big data analytics resource management techniques in fog computing for tele-health applications

Other Title(s)

تقنيات إدارة الموارد المتعلقة بتحليل البيانات الضخمة الحية في الحوسبة الضبابية للتطبيقات الصحية عن بعد

Joint Authors

Tahir, Muhammad
Shihab, Raja
Muhammad, Huda K.

Source

Jordanian Journal of Computetrs and Information Technology

Issue

Vol. 7, Issue 1 (31 Mar. 2021), pp.89-103, 15 p.

Publisher

Princess Sumaya University for Technology

Publication Date

2021-03-31

Country of Publication

Jordan

No. of Pages

15

Main Subjects

Information Technology and Computer Science

Abstract EN

Enhancing the IoT health monitoring systems used in various environments, such as smart homes and smart hospitals, imply lively analyzing the patients’ critical streams (e.g.

ECG stream).

Conducting these tele-health applications over the traditional cloud violates the deadline constrains of the stream analytics applications, which results not only in performance degradation, but also in inaccurate analytics results due to patient's stream loss.

Fog computing can take place within the patient's vicinity and is considered as the best candidate for critically analyzed stream applications.

Fog nodes are geo-distributed and are poor in resources, thus a scalable and fault-tolerant resource management platform for stream analytics in fog computing is a must.

Current Stream Processing (SP) resource managers are designed for massive resource nodes, deploying them over the poor resource edge fog nodes greatly decreasing the fog infrastructure utilization.

Innovative SP resource managers that cope with the fog nature are needed.

We propose Fog Assisted Resource Management (FARM) platform based on Apache Hadoop2 resource manager (YARN) for compatible stream/batch analytics.

Static FARM (S-FARM) represents two YARN schedulers; per-user and per-module.

Results indicate that per- user scheduler overcomes the lack of resources issues of the edge fog nodes, fully utilizes the fog infrastructure and allows the system to expand safely up to its double size.

In addition, Differentiated S-FARM scheduler is proposed to support per-user control to the analytic results' accuracy and speed.

Stream CardioVascular Disease (S-CVD) application for patient's ECG analytics is simulated in iFogSim to judge the proposed YARN schedulers.

The research is pioneer in enhancing the poor resource edge fog node utilization, supporting per- user control to live big data analytics IoT applications and utilizing iFogSim to implement and evaluate the resource manager performance of a stream analytics platform.

American Psychological Association (APA)

Shihab, Raja& Tahir, Muhammad& Muhammad, Huda K.. 2021. Live big data analytics resource management techniques in fog computing for tele-health applications. Jordanian Journal of Computetrs and Information Technology،Vol. 7, no. 1, pp.89-103.
https://search.emarefa.net/detail/BIM-1415622

Modern Language Association (MLA)

Shihab, Raja…[et al.]. Live big data analytics resource management techniques in fog computing for tele-health applications. Jordanian Journal of Computetrs and Information Technology Vol. 7, no. 1 (Mar. 2021), pp.89-103.
https://search.emarefa.net/detail/BIM-1415622

American Medical Association (AMA)

Shihab, Raja& Tahir, Muhammad& Muhammad, Huda K.. Live big data analytics resource management techniques in fog computing for tele-health applications. Jordanian Journal of Computetrs and Information Technology. 2021. Vol. 7, no. 1, pp.89-103.
https://search.emarefa.net/detail/BIM-1415622

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 101-103

Record ID

BIM-1415622