Emerging Technologies, Research Opportunities and Experimentation for Network Virtualization and Cloud Computing

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 16297

Special Issue Editor

Special Issue Information

Dear Colleagues,

Network virtualization allows dissociation of network services from the underlying hardware and thereby enables provisioning of services in a virtual network, while cloud computing offers an on-demand self-service, broad network access, multi-tenancy, resource pooling, elasticity, scalability, and measured service. Currently, network virtualization and cloud computing have made tremendous advancements by enabling several emerging technologies including, Software Defined Networking (SDN), Network Function Virtualization (NFV), Containerization, DevOps, Microservices, Artificial Intelligence, Machine Learning, Blockchain, Virtual Reality and Augmented Reality. This special issue is about reporting such advancements in the field of network virtualization and cloud computing. Further, experimentation has played an important role in advancing research in the area of network virtualization and cloud computing. Currently, several open testbeds such as Fed4Fire+, POWDER, COSMOS, GENI, AERPAW, Emulab, ORBIT and Onelab are available on which network virtualization and cloud computing ideas could be tested and validated. This special issue is also about reporting such experiments on testbeds.

The scope of this Special Issue is broad to cover all aspects of network virtualization and cloud computing. Potential topics include, but are not limited to, the following:

  1. Software Defined Networking: challenges, and research opportunities;
  2. Network Function Virtualization: challenges and research opportunities;
  3. Containerization for network virtualization and cloud computing;
  4. Devops for network virtualization and cloud computing;
  5. Microservices for network virtualization and cloud computing;
  6. Artificial Intelligence and machine learning techniques for network virtualization and cloud computing;
  7. Blockchain technology for network virtualization and cloud computing;
  8. Virtual Reality and Augmented Reality in network virtualization and cloud computing;
  9. Network Virtualization and Cloud computing experimentation using testbeds.

Dr. Sachin Sharma
Guest Editor

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Keywords

  • software defined networking
  • network function virtualization
  • containerization
  • microservices
  • artificial intelligence
  • machine learning
  • blockchain
  • virtual reality
  • augmented reality
  • testbeds research

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Published Papers (4 papers)

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Research

16 pages, 621 KiB  
Article
Reliable Application Layer Routing Using Decentralized Identifiers
by Khalid Alsubhi, Bander Alzahrani, Nikos Fotiou, Aiiad Albeshri and Mohammed Alreshoodi
Future Internet 2022, 14(11), 322; https://doi.org/10.3390/fi14110322 - 6 Nov 2022
Cited by 1 | Viewed by 1858
Abstract
Modern internet of things (IoT) applications can benefit from advanced communication paradigms, including multicast and anycast. Next-generation internet architectures, such as information-centric networking (ICN), promise to support these paradigms, but at the same time they introduce new security challenges. This paper presents a [...] Read more.
Modern internet of things (IoT) applications can benefit from advanced communication paradigms, including multicast and anycast. Next-generation internet architectures, such as information-centric networking (ICN), promise to support these paradigms, but at the same time they introduce new security challenges. This paper presents a solution that extends an ICN-like architecture based on software-defined networking (SDN) that supports those communication paradigms. Using the proposed solution, the underlying architecture is enhanced with a novel security mechanism that allows content “advertisements” only from authorized endpoints. This mechanism prevents “content pollution”, which is a significant security threat in ICN architectures. The proposed solution is lightweight, and it enables identity sharing as well as secured and controlled identity delegation. Full article
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13 pages, 1945 KiB  
Article
Improving Quality Indicators of the Cloud-Based IoT Networks Using an Improved Form of Seagull Optimization Algorithm
by Hamza Mohammed Ridha Al-Khafaji
Future Internet 2022, 14(10), 281; https://doi.org/10.3390/fi14100281 - 29 Sep 2022
Cited by 8 | Viewed by 2281
Abstract
The Internet of things (IoT) points to billions of devices located worldwide which are connected and share their data based on the Internet. Due to the new technologies that provide cheap computer chips and universal wireless networks, it is feasible that everything from [...] Read more.
The Internet of things (IoT) points to billions of devices located worldwide which are connected and share their data based on the Internet. Due to the new technologies that provide cheap computer chips and universal wireless networks, it is feasible that everything from a small tablet to a very large airplane will be connected to the Internet and will be a part of the IoT. In most applications, IoT network nodes face limitations in terms of energy source and cost. Therefore, the need for innovative methods to improve quality indicators that increase the lifespan of networks is evident. Here, a novel technique is presented to increase the quality of service (QoS) in IoT using an improved meta-heuristic algorithm, called the improved seagull optimization algorithm (ISOA), along with traffic management in these networks. Based on this subject, the traffic-aware algorithm can manage the sending of packets and increase the QoS provision in terms of time to a great extent. The performance evaluation of the proposed method and comparison with the previous methods demonstrated the accuracy and efficiency of this method and its superiority over the previous works. Full article
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32 pages, 1245 KiB  
Article
Future Wireless Networking Experiments Escaping Simulations
by Sachin Sharma, Saish Urumkar, Gianluca Fontanesi, Byrav Ramamurthy and Avishek Nag
Future Internet 2022, 14(4), 120; https://doi.org/10.3390/fi14040120 - 14 Apr 2022
Cited by 5 | Viewed by 3827
Abstract
In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, [...] Read more.
In computer networking, simulations are widely used to test and analyse new protocols and ideas. Currently, there are a number of open real testbeds available to test the new protocols. In the EU, for example, there are Fed4Fire testbeds, while in the US, there are POWDER and COSMOS testbeds. Several other countries, including Japan, Brazil, India, and China, have also developed next-generation testbeds. Compared to simulations, these testbeds offer a more realistic way to test protocols and prototypes. In this paper, we examine some available wireless testbeds from the EU and the US, which are part of an open-call EU project under the NGIAtlantic H2020 initiative to conduct Software-Defined Networking (SDN) experiments on intelligent Internet of Things (IoT) networks. Furthermore, the paper presents benchmarking results and failure recovery results from each of the considered testbeds using a variety of wireless network topologies. The paper compares the testbeds based on throughput, latency, jitter, resources available, and failure recovery time, by sending different types of traffic. The results demonstrate the feasibility of performing wireless experiments on different testbeds in the US and the EU. Further, issues faced during experimentation on EU and US testbeds are also reported. Full article
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17 pages, 677 KiB  
Article
Detecting IoT Attacks Using an Ensemble Machine Learning Model
by Vikas Tomer and Sachin Sharma
Future Internet 2022, 14(4), 102; https://doi.org/10.3390/fi14040102 - 24 Mar 2022
Cited by 32 | Viewed by 7238
Abstract
Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the [...] Read more.
Malicious attacks are becoming more prevalent due to the growing use of Internet of Things (IoT) devices in homes, offices, transportation, healthcare, and other locations. By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the cloud. Machine learning is frequently used for the detection of attacks due to the huge amount of data available from IoT devices. However, the problem is that fog devices may not have enough resources, such as processing power and memory, to detect attacks in a timely manner. This paper proposes an approach to offload the machine learning model selection task to the cloud and the real-time prediction task to the fog nodes. Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. The proposed approach is tested using the NSL-KDD dataset. The results show the effectiveness of the proposed approach in terms of several performance measures, such as execution time, precision, recall, accuracy, and ROC (receiver operating characteristic) curve. Full article
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