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Electronics
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Network Management: Advances and Opportunities
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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: closed (30 August 2021) | Viewed by 31085

Special Issue Editors

Prof. Dr. Jemal Abawajy

E-Mail Website
Guest Editor
Faculty of Science, Engineering and Built Environment, Deakin University, Burwood, VIC 3125, Australia
Interests: cyber security; blockchain; internet of things; cloud computing; artificial intelligence applications in business
Special Issues, Collections and Topics in MDPI journals
Dr. Aaqif Afzaal Abbasi

E-Mail 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, Collections and Topics in MDPI journals
Dr. Amir Sinaeepourfard

E-Mail 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 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 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

  • 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 (4 papers)

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Research

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26 pages, 11737 KiB  
Article
Virtual Router Design and Modeling for Future Networks with QoS Guarantees
by Mykola Beshley, Natalia Kryvinska, Halyna Beshley, Oleg Yaremko and Julia Pyrih
Electronics 2021, 10(10), 1139; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10101139 - 11 May 2021
Cited by 6 | Viewed by 2169
Abstract
A virtual router model with a static and dynamic resource reconfiguration for future internet networking was developed. This technique allows us to create efficient virtual devices with optimal parameters (queue length, queue overflow management discipline, number of serving devices, mode of serving devices) [...] Read more.
A virtual router model with a static and dynamic resource reconfiguration for future internet networking was developed. This technique allows us to create efficient virtual devices with optimal parameters (queue length, queue overflow management discipline, number of serving devices, mode of serving devices) to ensure the required level of quality of service (QoS). An analytical model of a network device with virtual routers is proposed. By means of the mentioned mathematical representation, it is possible to determine the main parameters of the virtual queue system, which are based on the first in, first out (FIFO) algorithm, in order to analyze the efficiency of network resources utilization, as well as to determine the parameters of QoS flows, for a given intensity of packets arrival at the input interface of the network element. In order to research the guaranteed level of QoS in future telecommunications networks, a simulation model of a packet router with resource virtualization was developed. This model will allow designers to choose the optimal parameters of network equipment for the organization of virtual routers, which, in contrast to the existing principle of service, will provide the necessary quality of service provision to end users in the future network. It is shown that the use of standard static network device virtualization technology is not able to fully provide a guaranteed level of QoS to all present flows in the network by the criterion of minimum delay. An approach for dynamic reconfiguration of network device resources for virtual routers has been proposed, which allows more flexible resource management at certain points in time depending on the input load. Based on the results of the study, it is shown that the dynamic virtualization of the network device provides a guaranteed level of QoS for all transmitted flows. Thus, the obtained results confirm the feasibility of using dynamic reconfiguration of network device resources to improve the quality of service for end users. Full article
(This article belongs to the Special Issue Network Management: Advances and Opportunities)
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16 pages, 451 KiB  
Article
A Word-Level Analytical Approach for Identifying Malicious Domain Names Caused by Dictionary-Based DGA Malware
by Akihiro Satoh, Yutaka Fukuda, Gen Kitagata and Yutaka Nakamura
Electronics 2021, 10(9), 1039; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics10091039 - 28 Apr 2021
Cited by 5 | Viewed by 2249
Abstract
Computer networks are facing serious threats from the emergence of malware with sophisticated DGAs (Domain Generation Algorithms). This type of DGA malware dynamically generates domain names by concatenating words from dictionaries for evading detection. In this paper, we propose an approach for identifying [...] Read more.
Computer networks are facing serious threats from the emergence of malware with sophisticated DGAs (Domain Generation Algorithms). This type of DGA malware dynamically generates domain names by concatenating words from dictionaries for evading detection. In this paper, we propose an approach for identifying the callback communications of such dictionary-based DGA malware by analyzing their domain names at the word level. This approach is based on the following observations: These malware families use their own dictionaries and algorithms to generate domain names, and accordingly, the word usages of malware-generated domains are distinctly different from those of human-generated domains. Our evaluation indicates that the proposed approach is capable of achieving accuracy, recall, and precision as high as 0.9989, 0.9977, and 0.9869, respectively, when used with labeled datasets. We also clarify the functional differences between our approach and other published methods via qualitative comparisons. Taken together, these results suggest that malware-infected machines can be identified and removed from networks using DNS queries for detected malicious domain names as triggers. Our approach contributes to dramatically improving network security by providing a technique to address various types of malware encroachment. Full article
(This article belongs to the Special Issue Network Management: Advances and Opportunities)
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17 pages, 1658 KiB  
Article
A Cloud-Based Enterprise Resource Planning Architecture for Women’s Education in Remote Areas
by Raheela Nasim, Halim Ullah, Sanam Shahla Rizvi, Almas Abbasi, Sajid Khan, Rabia Riaz and Anand Paul
Electronics 2020, 9(11), 1758; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9111758 - 23 Oct 2020
Cited by 5 | Viewed by 3213
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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25 pages, 1434 KiB  
Review
A Review of Machine Learning Algorithms for Cloud Computing Security
by Umer Ahmed Butt, Muhammad Mehmood, Syed Bilal Hussain Shah, Rashid Amin, M. Waqas Shaukat, Syed Mohsan Raza, Doug Young Suh and Md. Jalil Piran
Electronics 2020, 9(9), 1379; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9091379 - 26 Aug 2020
Cited by 83 | Viewed by 22135
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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