Boosting Edge Computing Performance Through Heterogeneous Manycore Systems

2018 ◽  
Author(s):  
Ramneek ◽  
Seung-Jun Cha ◽  
Seung Hyub Jeon ◽  
Yeon Jeong Jeong ◽  
Jin Mee Kim ◽  
...  
Keyword(s):  
Edge Computing ◽  
Computing Performance

FPGA on Cyber-Physical Systems for the Implementation of Internet of Things

2020 ◽  
pp. 82-96
Author(s):  
Rajit Nair ◽  
Preeti Nair ◽  
Vidya Kant Dwivedi
Keyword(s):  
High Performance ◽  
Management Strategies ◽  
Cyber Physical Systems ◽  
Edge Computing ◽  
Reconfigurable Systems ◽  
Smart Power ◽  
Physical Systems ◽  
Computing Performance ◽  
High Level ◽  
Processing Architecture

Today, in cyber-physical systems, there is a transformation in which processing has been done on distributed mode rather than performing on centralized manner. Usually this type of approach is known as Edge computing, which demands hardware time to time when requirements in computing performance get increased. Considering this situation, we must remain energy efficient and adaptable. So, to meet the above requirements, SRAM-based FPGAs and their inherent run-time reconfigurability are integrated with smart power management strategies. Sometimes this approach fails in the case of user accessibility and easy development. This chapter presents an integrated framework to develop FPGA-based high-performance embedded systems for Edge computing in cyber-physical systems. The processing architecture will be based on hardware that helps us to manage reconfigurable systems from high level systems without any human intervention.

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

scholarly journals Robust Edge Computing in UAV Systems via Scalable Computing and Cooperative Computing

2021 ◽  
Author(s):  
Zhi Liu ◽  
Cheng Zhan ◽  
Ying Cui ◽  
Celimuge Wu ◽  
Han Hu
Keyword(s):  
Video Streaming ◽  
Unmanned Aerial Vehicle ◽  
Edge Computing ◽  
Future Research ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Aerial Vehicle ◽  
Cooperative Computing ◽  
Computing Performance ◽  
Simulation Results

<div>Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.</div>

scholarly journals Hierarchical Mobile Edge Computing Architecture Based on Context Awareness

2018 ◽  
Vol 8 (7) ◽  
pp. 1160 ◽  
Author(s):  
Juyong Lee ◽  
Jihoon Lee
Keyword(s):  
Mobile Network ◽  
Content Delivery ◽  
Edge Computing ◽  
Multimedia Streaming ◽  
Mobile Edge Computing ◽  
Computing Power ◽  
Computing Services ◽  
Recent Developments ◽  
Computing Performance ◽  
Fast Access

Due to the recent developments in mobile network technology and the supply of mobile devices, services that require high computing power and fast access speed, such as machine learning and multimedia streaming, are attracting attention. Mobile Edge Computing (MEC) has emerged. MEC allows servers to be located close to users to efficiently handle these services and provides users with ultra-low latency content delivery and powerful computing services. However, there has been a lack of research into the architecture required to efficiently use the computing power and resources of MEC. So, this paper proposes hierarchical MEC architecture in which MEC servers (MECS) are arranged in a hierarchical scheme to provide users with rapid content delivery, high computing performance, and efficient use of server resources.

scholarly journals Pattern-Identified Online Task Scheduling in Multitier Edge Computing for Industrial IoT Services

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Nhu-Ngoc Dao ◽  
Duc-Nghia Vu ◽  
Yunseong Lee ◽  
Sungrae Cho ◽  
Chihyun Cho ◽  
...  
Keyword(s):  
Real Time ◽  
Task Scheduling ◽  
Edge Computing ◽  
Smart Manufacturing ◽  
Self Organizing Map ◽  
Industrial Internet Of Things ◽  
Industrial Internet ◽  
Industrial Iot ◽  
Service Capability ◽  
Computing Performance

In smart manufacturing, production machinery and auxiliary devices, referred to as industrial Internet of things (IIoT), are connected to a unified networking infrastructure for management and command deliveries in a precise production process. However, providing autonomous, reliable, and real-time offloaded services for such a production is an open challenge since these IIoT devices are assumed lightweight embedded platforms with limited computing performance. In this paper, we propose a pattern-identified online task scheduling (PIOTS) mechanism for the networking infrastructure, where multitier edge computing is provided, in order to handle the offloaded tasks in real time. First, historical IIoT task patterns in every timeslot are used to train a self-organizing map (SOM), which represents the features of the task patterns within defined dimensions. Consequently, offline task scheduling among edge computing-enabled entities is performed on the set of all SOM neurons using the Hungarian method to determine the expected optimal task assignments. In real-time context, whenever a task arrives at the infrastructure, the expected optimal assignment for the task is scheduled to the appropriate edge computing-enabled entity. Numerical simulation results show that the proposed PIOTS mechanism overcomes existing solutions in terms of computation performance and service capability.

Smart Community Edge: Stream Processing Edge Computing Node for Smart Community Services

2020 ◽  
Vol 140 (9) ◽  
pp. 1030-1039
Author(s):  
W.A. Shanaka P. Abeysiriwardhana ◽  
Janaka L. Wijekoon ◽  
Hiroaki Nishi
Keyword(s):  
Stream Processing ◽  
Community Services ◽  
Edge Computing ◽  
Computing Node ◽  
Smart Community

Load Balancing for Energy-Harvesting Mobile Edge Computing

2021 ◽  
Vol E104.A (1) ◽  
pp. 336-342
Author(s):  
Ping ZHAO ◽  
Jiawei TAO ◽  
Abdul RAUF ◽  
Fengde JIA ◽  
Longting XU
Keyword(s):  
Energy Harvesting ◽  
Load Balancing ◽  
Edge Computing ◽  
Mobile Edge Computing
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