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Electronics
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Software-Defined Cloud Computing: Latest Advances and Prospects
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Software-Defined Cloud Computing: Latest Advances and Prospects

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

Deadline for manuscript submissions: closed (15 October 2024) | Viewed by 3632

Special Issue Editor

Dr. Ting Wang

E-Mail Website
Guest Editor
Software Engineering Institute, East China Normal University, Shanghai 200062, China
Interests: cloud computing; machine learning; intelligent networking
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Software-defined cloud computing is an emerging field that has gained significant attention in recent years. It involves the use of software-defined technologies to manage and orchestrate cloud resources such as computing, storage, and networking. This approach enables the creation of highly automated, flexible, and dynamic cloud infrastructures that can adapt to changing workloads and business requirements. The software-defined approach also enables the implementation of advanced management and security features, as well as the integration of multiple cloud platforms and services.

In this Special Issue, we aim to bring together the latest research advances and prospects in software-defined cloud computing. We invite original research papers, reviews, and case studies that address topics including (but not limited to):

  • Software-defined cloud infrastructure design and deployment;
  • Cloud resource management and orchestration using software-defined approaches;
  • Security, privacy, and compliance issues in software-defined cloud computing;
  • Interoperability and integration of multiple cloud platforms and services;
  • Performance optimization and energy efficiency in software-defined cloud computing;
  • Applications and use cases of software-defined cloud computing in various domains such as healthcare, finance, and education.

We welcome papers that present novel theoretical and practical contributions, as well as papers that provide insights into real-world deployments and experiences. Submissions will undergo a rigorous peer-review process, and accepted papers will be published in a high-quality Special Issue of the journal.

Dr. Ting Wang
Guest Editor

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

  • software-defined networks
  • cloud computing
  • resource management
  • performance optimization
  • cloud security
  • cloud platform
 

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Published Papers (2 papers)

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Research

24 pages, 2796 KiB  
Article
Performance and Latency Efficiency Evaluation of Kubernetes Container Network Interfaces for Built-In and Custom Tuned Profiles
by Vedran Dakić, Jasmin Redžepagić, Matej Bašić and Luka Žgrablić
Electronics 2024, 13(19), 3972; https://doi.org/10.3390/electronics13193972 - 9 Oct 2024
Viewed by 1131
Abstract
In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network [...] Read more.
In the era of DevOps, developing new toolsets and frameworks that leverage DevOps principles is crucial. This paper demonstrates how Ansible’s powerful automation capabilities can be harnessed to manage the complexity of Kubernetes environments. This paper evaluates efficiency across various CNI (Container Network Interface) plugins by orchestrating performance analysis tools across multiple power profiles. Our performance evaluations across network interfaces with different theoretical bandwidths gave us a comprehensive understanding of CNI performance and overall efficiency, with performance efficiency coming well below expectations. Our research confirms that certain CNIs are better suited for specific use cases, mainly when tuning our environment for smaller or larger network packets and workload types, but also that there are configuration changes we can make to mitigate that. This paper also provides research into how to use performance tuning to optimize the performance and efficiency of our CNI infrastructure, with practical implications for improving the performance of Kubernetes environments in real-world scenarios, particularly in more demanding scenarios such as High-Performance Computing (HPC) and Artificial Intelligence (AI). Full article
(This article belongs to the Special Issue Software-Defined Cloud Computing: Latest Advances and Prospects)
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19 pages, 3648 KiB  
Article
Exploration of Eye Fatigue Detection Features and Algorithm Based on Eye-Tracking Signal
by Weifeng Sun, Yuqi Wang, Bingliang Hu and Quan Wang
Electronics 2024, 13(10), 1798; https://doi.org/10.3390/electronics13101798 - 7 May 2024
Viewed by 1797
Abstract
Eye fatigue has a fatiguing effect on the eye muscles, and eye movement performance is a macroscopic response to the eye fatigue state. To detect and prevent the risk of eye fatigue in advance, this study designed an eye fatigue detection experiment, collected [...] Read more.
Eye fatigue has a fatiguing effect on the eye muscles, and eye movement performance is a macroscopic response to the eye fatigue state. To detect and prevent the risk of eye fatigue in advance, this study designed an eye fatigue detection experiment, collected experimental data samples, and constructed experimental data sets. In this study, eye-tracking feature extraction was completed, and the significance difference of eye-tracking features under different fatigue states was discussed by two-way repeated-measures ANOVA (Analysis of Variance). The experimental results demonstrate the feasibility of eye fatigue detection from eye-tracking signals. In addition, this study considers the effects of different feature extraction methods on eye fatigue detection accuracy. This study examines the performance of machine learning algorithms based on manual feature calculation (SVM, DT, RM, ET) and deep learning algorithms based on automatic feature extraction (CNN, auto-encoder, transformer) in eye fatigue detection. Based on the combination of the methods, this study proposes the feature union auto-encoder algorithm, and the accuracy of the algorithm for eye fatigue detection on the experimental dataset is improved from 82.4% to 87.9%. Full article
(This article belongs to the Special Issue Software-Defined Cloud Computing: Latest Advances and Prospects)
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