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Symmetry
Special Issues
Asymmetrical Network Control for Complex Dynamic Services
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Special Issue "Asymmetrical Network Control for Complex Dynamic Services"

A special issue of Symmetry (ISSN 2073-8994). This special issue belongs to the section "Computer Science and Symmetry/Asymmetry".

Deadline for manuscript submissions: 31 March 2023 | Viewed by 2900

Special Issue Editors

Dr. Chao Fang

E-Mail Website
Guest Editor
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Interests: future networks; big data for networking; mobile edge computing
Special Issues, Collections and Topics in MDPI journals
Dr. Peng Li

E-Mail Website
Guest Editor
School of Computer Science and Engineering, The University of Aizu, Aizuwakamatsu 965-8580, Japan
Interests: wired and wireless networking; mobile/aerial communication and computing; cloud/edge computing; and machine learning systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to provide a forum for presentations and discussions on the recent advances in asymmetrical network control and its applications. This Special Issue covers pure research and applications within novel scopes related to asymmetrical network control. In addition, it deals with network and computer technologies, such as future networks, big data, security, the IoT, cloud computing, and so on. The topics of this Special Issue include but are not limited to:

  • Software-defined networking
  • Cooperation between cloud and edge computing
  • Service awareness
  • Cross-layer optimization
  • Resource allocation
  • Content delivery
  • Intelligent control
  • Asymmetrical network

Dr. Chao Fang
Dr. Peng Li
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. Symmetry is an international peer-reviewed open access monthly 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 2000 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

  • software-defined networking
  • cooperation between cloud and edge computing
  • service awareness
  • cross-layer optimization
  • resource allocation
  • content delivery
  • intelligent control
  • asymmetrical network

Published Papers (3 papers)

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Article
Deep Reinforcement Learning-Based Resource Allocation for Content Distribution in IoT-Edge-Cloud Computing Environments
by
Tongke Cui
,
Ruopeng Yang
,
Chao Fang
and
Shui Yu
Symmetry 2023, 15(1), 217; https://doi.org/10.3390/sym15010217 - 12 Jan 2023
Viewed by 1491
Abstract
With the emergence of intelligent terminals, the Internet of Vehicles (IoV) has been drawing great attention by taking advantage of mobile communication technologies. However, high computation complexity, collaboration communication overhead and limited network bandwidths bring severe challenges to the provision of latency-sensitive IoV [...] Read more.
With the emergence of intelligent terminals, the Internet of Vehicles (IoV) has been drawing great attention by taking advantage of mobile communication technologies. However, high computation complexity, collaboration communication overhead and limited network bandwidths bring severe challenges to the provision of latency-sensitive IoV services. To overcome these problems, we design a cloud-edge cooperative content-delivery strategy in asymmetrical IoV environments to minimize network latency by providing optimal computing, caching and communication resource allocation. We abstract the joint allocation issue of heterogeneous resources as a queuing theory-based latency minimization objective. Next, a new deep reinforcement learning (DRL) scheme works in each network node to achieve optimal content caching and request routing on the basis of the perceptive request history and network state. Extensive simulations show that our proposed strategy has lower network latency compared with the current solutions in the cloud-edge collaboration system and converges fast under different scenarios. Full article
(This article belongs to the Special Issue Asymmetrical Network Control for Complex Dynamic Services)
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Article
Asymmetry Opinion Evolution Model Based on Dynamic Network Structure
by
An Lu
and
Yaguang Guo
Symmetry 2022, 14(12), 2499; https://doi.org/10.3390/sym14122499 - 25 Nov 2022
Viewed by 432
Abstract
On social media platforms, users can not only unfollow others whose opinion excessively opposes their own, but they can also add new connections. To better reflect the evolution of opinions on social media, this paper proposes an opinion asymmetry evolution model based on [...] Read more.
On social media platforms, users can not only unfollow others whose opinion excessively opposes their own, but they can also add new connections. To better reflect the evolution of opinions on social media, this paper proposes an opinion asymmetry evolution model based on a dynamic network structure, where the trusts between two individuals are not mutual and dynamic. First, the paper analyzes the general properties of the model. We prove that group opinion can converge to a steady state even if the connection is unidirectional. Second, we compare the evolution process of static and dynamic network structures. Computer simulation results show that a higher probability of new connections leads to less aggregation of group opinion, higher information entropy, lower HHI, and lower degrees of polarization. Full article
(This article belongs to the Special Issue Asymmetrical Network Control for Complex Dynamic Services)
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Article
A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments
by
Chao Fang
,
Tianyi Zhang
,
Jingjing Huang
,
Hang Xu
,
Zhaoming Hu
,
Yihui Yang
,
Zhuwei Wang
,
Zequan Zhou
and
Xiling Luo
Symmetry 2022, 14(10), 2120; https://doi.org/10.3390/sym14102120 - 12 Oct 2022
Cited by 1 | Viewed by 692
Abstract
Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of [...] Read more.
Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions. Full article
(This article belongs to the Special Issue Asymmetrical Network Control for Complex Dynamic Services)
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