الفهرس 
Fog Computing ParadigmOnly 14 pages are availabe for public view
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Abstract
Towards the fourth industrial revolution (industry 4.0), IoT enriches the e-health
market by valuable applications (i.e. remote patient monitoring). Enhancing the
IoT health monitoring systems used in various environments such as smart
homes and smart hospitals, imply lively analyzing the patient’s critical streams
(i.e. ECG stream). Conducting these tele-health applications over the traditional
cloud violates the deadline constrains of the stream analytics applications due to
network congestion, 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. Due to fog nodes geo-distribution and
lack of resources, a scalable and fault tolerant resource management platform for
stream analytics in fog computing is a must. Current stream analytics schedulers
are designed for massive resource nodes, which degrades the fog infrastructure
utilization. Innovative stream analytics schedulers in fog computing are needed.
This study presents live big data analytics resource management techniques in
fog and mist computing for tele-health applications. It proposes a Fog Assisted
Resource Management (FARM) platform based on Apache Hadoop2 (YARN).
FARM provides compatible short-term and long-term big data analytics. Static
FARM (S-FARM) represents YARN schedulers in per-user and per-module
modes. Results indicate that per-user S-FARM scheduler overcomes the mist
nodes’ lack of resources, enhances the fog infrastructure utilization, and allows
for safer system expansion than per-module S-FARM scheduler. In addition,
differentiated S-FARM scheduler is studied to allow per-user control to the
analytics results accuracy and speed.Stream CardioVascular Disease (S-CVD)
application is modeled and simulated to test the proposed YARN schedulers.
S-CVD lively analyzes the patient’s ECG streams to conduct the patient’s state
using a linear classifier. IFogSim simulator has been used to judge the
application and fog infrastructure performance under various scenarios. The
research is a pioneer in solving the lack of computing resources issue of the mist
nodes, supporting per-user control to live big data analytics IoT applications,
and utilizing iFogSim to implement and evaluate the performance of a stream
analytics platform resource manager.