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2020 ; 9
(1
): 66
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Flexible computation offloading in a fuzzy-based mobile edge orchestrator for IoT
applications
#MMPMID33532167
Nguyen V
; Khanh TT
; Nguyen TDT
; Hong CS
; Huh EN
J Cloud Comput (Heidelb)
2020[]; 9
(1
): 66
PMID33532167
show ga
In the Internet of Things (IoT) era, the capacity-limited Internet and
uncontrollable service delays for various new applications, such as video
streaming analysis and augmented reality, are challenges. Cloud computing
systems, also known as a solution that offloads energy-consuming computation of
IoT applications to a cloud server, cannot meet the delay-sensitive and
context-aware service requirements. To address this issue, an edge computing
system provides timely and context-aware services by bringing the computations
and storage closer to the user. The dynamic flow of requests that can be
efficiently processed is a significant challenge for edge and cloud computing
systems. To improve the performance of IoT systems, the mobile edge orchestrator
(MEO), which is an application placement controller, was designed by integrating
end mobile devices with edge and cloud computing systems. In this paper, we
propose a flexible computation offloading method in a fuzzy-based MEO for IoT
applications in order to improve the efficiency in computational resource
management. Considering the network, computation resources, and task
requirements, a fuzzy-based MEO allows edge workload orchestration actions to
decide whether to offload a mobile user to local edge, neighboring edge, or cloud
servers. Additionally, increasing packet sizes will affect the failed-task ratio
when the number of mobile devices increases. To reduce failed tasks because of
transmission collisions and to improve service times for time-critical tasks, we
define a new input crisp value, and a new output decision for a fuzzy-based MEO.
Using the EdgeCloudSim simulator, we evaluate our proposal with four benchmark
algorithms in augmented reality, healthcare, compute-intensive, and infotainment
applications. Simulation results show that our proposal provides better results
in terms of WLAN delay, service times, the number of failed tasks, and VM
utilization.
free
free
free
