Special Issue "Network Management: Advances and Opportunities"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 31 March 2021.

Special Issue Editors

Dr. Aaqif Afzaal Abbasi
Website
Guest Editor
Department of Software Engineering, Foundation University, Islamabad, Pakistan
Interests: cloud computing; data center performance optimization; edge computing; high-performance computing; internet of things; network resource allocation and management; parallel and distributed systems
Special Issues and Collections in MDPI journals
Dr. Amir Sinaeepourfard
Website
Guest Editor
Department of Computer Science, Norwegian University of Science and Technology, NTNU, NO-7491 Trondheim, Norway
Interests: IoT; smart cities; big data management; cloud computing; edge/fog computing; large-scale IoT management; data & software management
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the last decade, there has been an enormous increase in the use of internet-based technologies and services. This includes both web-based services and social media. Due to an increase in the use of internet-based technologies, the computer and networking industry has developed at a very fast pace. The biggest challenge for network development remained the need to develop technologies that can allow a large number of users to access network resources simultaneously. The use of vendor-based technologies and standardization often led to compatibility and resource constraints challenges. However, the latest trends in the networking paradigm are focusing on these challenges by bringing open source technologies on vendor-agnostic platforms.

In this context, the networking industry developed multiple networking and data sharing technologies, such as peer to peer networking (P2P), grid computing, cloud computing, fog computing, edge computing, dew computing, and IoT. With the development of the smart cities concept, smartphones can be connected to other devices to form an ecosystem of smart devices. Similarly, software-defined networking (SDN) and network function virtualization (NFV) technology are vital in providing platform-independent shared storage platforms for users. If we observe closely, we can see that the main purpose of these technologies is to improve network accessibility to a shared pool of resources and the ease of network management.

The purpose of this special issue is to provide a platform for researchers to share their research experiences, both theoretical and practical, in defining the issues, challenges, opportunities, and proposed solutions to address a wide range of network resource management challenges. We are looking for contributions in the best interest of the network research community. The potential topics of interest include, but are not limited to, the following:

  • AI for cloud computing
  • Lightweight analytics on edge computing
  • Fog computing challenges for energy sensitive IoT devices
  • Mobile devices and dew computing integration
  • Smart cities and IoT enabled resource constraints
  • Machine learning technologies for the Internet of Things (IoT) dataset analysis
  • Software-defined networking (SDN) in cloud data centers
  • Mobile centric IoT/5G
  • Network functions virtualization (NFV) and VM management for cloud storage
  • SDN in datacenters’ resource management
  • Future network architectures and internet measurement tools

Prof. Dr. Jemal H. Abawajy
Dr. Aaqif Afzaal Abbasi
Dr. Amir Sinaeepourfard
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 papers will be 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 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 1500 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

  • Network resource management
  • Data centers
  • Internet of Things
  • Cloud computing
  • Distributed systems management
  • Scalable systems
  • Edge computing
  • Smart cities
  • Energy efficiency IoT
  • Machine learning

Published Papers (2 papers)

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Research

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Open AccessArticle
A Cloud-Based Enterprise Resource Planning Architecture for Women’s Education in Remote Areas
Electronics 2020, 9(11), 1758; https://doi.org/10.3390/electronics9111758 - 23 Oct 2020
Abstract
This research provides an approach to exploring a suitable enterprise resource planning system using cloud management architecture for the educational environment. It enables enterprises to get into the competition. Enterprise resource planning for educational firms provides an approach to address the targeted female [...] Read more.
This research provides an approach to exploring a suitable enterprise resource planning system using cloud management architecture for the educational environment. It enables enterprises to get into the competition. Enterprise resource planning for educational firms provides an approach to address the targeted female population. To achieve this goal, a system has been established that has an infrastructure basis on governments, nongovernment organizations (NGOs), universities, and other social service providers. This paper helps to present the architecture of cloud computing for the overall educational environment concerns around the world. This research aims to contribute to women’s education with respect to modern technology. It ensures that technology is cost-efficiently available for women’s education in view of the availability and consistency of the system and in accordance with goals. An architecture is proposed to solve and take over the limitations that have been faced and are the reasons for the failure of the available systems. After designing the architecture, a survey questionnaire was designed and conducted with students and professionals of Air University, Bahria University, and Preston University. Full article
(This article belongs to the Special Issue Network Management: Advances and Opportunities)
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Review

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Open AccessReview
A Review of Machine Learning Algorithms for Cloud Computing Security
Electronics 2020, 9(9), 1379; https://doi.org/10.3390/electronics9091379 - 26 Aug 2020
Abstract
Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across [...] Read more.
Cloud computing (CC) is on-demand accessibility of network resources, especially data storage and processing power, without special and direct management by the users. CC recently has emerged as a set of public and private datacenters that offers the client a single platform across the Internet. Edge computing is an evolving computing paradigm that brings computation and information storage nearer to the end-users to improve response times and spare transmission capacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones. However, CC and edge computing have security challenges, including vulnerability for clients and association acknowledgment, that delay the rapid adoption of computing models. Machine learning (ML) is the investigation of computer algorithms that improve naturally through experience. In this review paper, we present an analysis of CC security threats, issues, and solutions that utilized one or several ML algorithms. We review different ML algorithms that are used to overcome the cloud security issues including supervised, unsupervised, semi-supervised, and reinforcement learning. Then, we compare the performance of each technique based on their features, advantages, and disadvantages. Moreover, we enlist future research directions to secure CC models. Full article
(This article belongs to the Special Issue Network Management: Advances and Opportunities)
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