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Electronics
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Edge Computing for Real-Time Systems
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Edge Computing for Real-Time Systems

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (30 May 2022) | Viewed by 4818

Special Issue Editors

Dr. Sheng Zhang

E-Mail Website
Guest Editor
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
Interests: edge computing; edge intelligence; cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Xuyun Zhang

E-Mail Website
Guest Editor
Department of Computing, Macquarie University, Sydney, Australia
Interests: cloud/edge computing; scalable machine learning; data privacy and cybersecurity
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Han

E-Mail Website
Guest Editor
Helen and John C. Hartmann Department of Electrical and Computer Engineering, New Jersey Institute of Technology (NJIT), Newark, NJ 07102, USA
Interests: edge computing; mobile systems; augmented reality systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Edge computing is an emerging computing paradigm which advocates processing data at the logical edge of a network and enables data analytics to occur closer to the data source and users, thereby reducing the response latency of analytics tasks. This advantage makes it a promising approach to real-time systems, ranging from smart cities and intelligent traffic control to video surveillance, in which live data (e.g., video, audio) generated from devices have strong requirements in terms of fast treatment, e.g., real-time mixed reality which requires the system to have a comprehensive understanding of different objects and instances as quickly as possible in the real world.

This Special Issue focuses on optimizing real-time systems via edge computing. We encourage papers in all areas related to this topic, including task scheduling, software architectures, data management, middleware, resource orchestration, and artificial intelligence.

Dr. Sheng Zhang
Dr. Xuyun Zhang
Dr. Tao Han
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Edge computing
  • Real-time systems
  • Fog computing
  • Cloud computing
  • Data analytics
  • Network slicing
  • 5G network
  • Task scheduling
  • Resource orchestration

Published Papers (2 papers)

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Research

24 pages, 869 KiB  
Article
Deadline-Aware Dynamic Task Scheduling in Edge–Cloud Collaborative Computing
by Yu Zhang, Bing Tang, Jincheng Luo and Jiaming Zhang
Electronics 2022, 11(15), 2464; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11152464 - 08 Aug 2022
Cited by 8 | Viewed by 2352
Abstract
In recent years, modern industry has been exploring the transition to cyber physical system (CPS)-based smart factories. As intelligent industrial detection and control technology grows in popularity, massive amounts of time-sensitive applications are generated. A cutting-edge computing paradigm called edge-cloud collaborative computing was [...] Read more.
In recent years, modern industry has been exploring the transition to cyber physical system (CPS)-based smart factories. As intelligent industrial detection and control technology grows in popularity, massive amounts of time-sensitive applications are generated. A cutting-edge computing paradigm called edge-cloud collaborative computing was developed to satisfy the need of time-sensitive tasks such as smart vehicles and automatic mechanical remote control, which require substantially low latency. In edge-cloud collaborative computing, it is extremely challenging to improve task scheduling while taking into account both the dynamic changes of user requirements and the limited available resources. The current task scheduling system applies a round-robin policy to cyclically select the next server from the list of available servers, but it may not choose the best-suited server for the task. To satisfy the real-time task flow of industrial production in terms of task scheduling based on deadline and time sensitivity, we propose a hierarchical architecture for edge-cloud collaborative environments in the Industrial Internet of Things (IoT) and then simplify and mathematically formulate the time consumption of edge-cloud collaborative computing to reduce latency. Based on the above hierarchical model, we present a dynamic time-sensitive scheduling algorithm (DSOTS). After the optimization of DSOTS, the dynamic time-sensitive scheduling algorithm with greedy strategy (TSGS) that ranks server capability and job size in a hybrid and hierarchical scenario is proposed. What cannot be ignored is that we propose to employ comprehensive execution capability (CEC) to measure the performance of a server for the first time and perform effective server load balancing while satisfying the user’s requirement for tasks. In this paper, we simulate an edge-cloud collaborative computing environment to evaluate the performance of our algorithm in terms of processing time, SLA violation rate, and cost by extending the CloudSimPlus toolkit, and the experimental results are very promising. Aiming to choose a more suitable server to handle dynamically incoming tasks, our algorithm decreases the average processing time and cost by 30% and 45%, respectively, as well as the average SLA violation by 25%, when compared to existing state-of-the-art solutions. Full article
(This article belongs to the Special Issue Edge Computing for Real-Time Systems)
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15 pages, 936 KiB  
Article
STAFL: Staleness-Tolerant Asynchronous Federated Learning on Non-iid Dataset
by Feng Zhu, Jiangshan Hao, Zhong Chen, Yanchao Zhao, Bing Chen and Xiaoyang Tan
Electronics 2022, 11(3), 314; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics11030314 - 20 Jan 2022
Cited by 2 | Viewed by 1862
Abstract
With the development of the Internet of Things, edge computing applications are paying more and more attention to privacy and real-time. Federated learning, a promising machine learning method that can protect user privacy, has begun to be widely studied. However, traditional synchronous federated [...] Read more.
With the development of the Internet of Things, edge computing applications are paying more and more attention to privacy and real-time. Federated learning, a promising machine learning method that can protect user privacy, has begun to be widely studied. However, traditional synchronous federated learning methods are easily affected by stragglers, and non-independent and identically distributed data sets will also reduce the convergence speed. In this paper, we propose an asynchronous federated learning method, STAFL, where users can upload their updates at any time and the server will immediately aggregate the updates and return the latest global model. Secondly, STAFL will judge the user’s data distribution according to the user’s update and dynamically change the aggregation parameters according to the user’s network weight and staleness to minimize the impact of non-independent and identically distributed data sets on asynchronous updates. The experimental results show that our method performs better on non-independent and identically distributed data sets than existing methods. Full article
(This article belongs to the Special Issue Edge Computing for Real-Time Systems)
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