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Symmetry
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Cloud Computing and Symmetry: Latest Advances and Prospects
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Cloud Computing and Symmetry: Latest Advances and Prospects

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

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 9657

Special Issue Editor

Dr. Minxian Xu

E-Mail Website
Guest Editor
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
Interests: cloud computing; microservices

Special Issue Information

Dear Colleagues,

Cloud Computing has been regarded as a new paradigm in the IT industry. One of the key research topics in the cloud computing area is optimizing the resource allocation in the cloud computing environment. This key topic can be addressed by taking advantage of some sort of symmetry, which can achieve better resource optimization, such as load balancing and energy efficiency. A set of approaches have been proposed to improve the system performance by optimizing resource usage, while the symmetry-based approaches have seldom been investigated. Given the success of cloud computing technology, it is expected that symmetry will be leveraged to achieve better performance in the area of cloud computing.

Given this, in this Special Issue we aim to observe academic advancements and industry practices in the area of cloud computing by exploiting the power of symmetry, including its contribution to cloud applications. The symmetry can be utilized in workload prediction for cloud computing, virtual machine allocation, microservice management, resource provisioning in terms of symmetry, etc.

Dr. Minxian Xu
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. 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 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

  • cloud computing
  • resource scheduling
  • load balancing
  • energy efficiency
  • microservice management

Published Papers (2 papers)

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Research

26 pages, 7679 KiB  
Article
A Fuzzy-Based Mobile Edge Architecture for Latency-Sensitive and Heavy-Task Applications
by Yanjun Shi, Jinlong Chu, Chao Ji, Jiajian Li and Shiduo Ning
Symmetry 2022, 14(8), 1667; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14081667 - 11 Aug 2022
Cited by 6 | Viewed by 1418
Abstract
Appropriate task offloading management strategy is a challenging problem for high delay-sensitive and heavy-task applications. This paper proposes a fuzzy-based mobile edge manager with task partitioning, which can handle the multi-criteria decision-making process by considering multiple parameters in the MEC network framework and [...] Read more.
Appropriate task offloading management strategy is a challenging problem for high delay-sensitive and heavy-task applications. This paper proposes a fuzzy-based mobile edge manager with task partitioning, which can handle the multi-criteria decision-making process by considering multiple parameters in the MEC network framework and make appropriate offloading decisions for incoming tasks of IoT applications. Considering that the mobile devices are becoming more and more powerful, this paper also takes WLAN delay and the computing power of mobile devices into account, forming a three-level fuzzy logic system. In addition, since many tasks of Internet of Things applications are composed of several independent modules, this paper also sets two optimal task partitioning ratios, which have symmetry, so that each module can be independently executed in each layer of the MEC network. In addition, results will return to the mobile devices after execution, so as to minimize the service time and improve QoS. Finally, several indexes such as task failure rate and service time are simulated, and the results show that the proposed scheme has better performance compared with the other four comparison schemes, especially for high-latency sensitivity and heavy-task applications. Full article
(This article belongs to the Special Issue Cloud Computing and Symmetry: Latest Advances and Prospects)
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15 pages, 322 KiB  
Article
Machine-Learning-Based DDoS Attack Detection Using Mutual Information and Random Forest Feature Importance Method
by Mona Alduailij, Qazi Waqas Khan, Muhammad Tahir, Muhammad Sardaraz, Mai Alduailij and Fazila Malik
Symmetry 2022, 14(6), 1095; https://0-doi-org.brum.beds.ac.uk/10.3390/sym14061095 - 27 May 2022
Cited by 47 | Viewed by 7549
Abstract
Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of [...] Read more.
Cloud computing facilitates the users with on-demand services over the Internet. The services are accessible from anywhere at any time. Despite the valuable services, the paradigm is, also, prone to security issues. A Distributed Denial of Service (DDoS) attack affects the availability of cloud services and causes security threats to cloud computing. Detection of DDoS attacks is necessary for the availability of services for legitimate users. The topic has been studied by many researchers, with better accuracy for different datasets. This article presents a method for DDoS attack detection in cloud computing. The primary objective of this article is to reduce misclassification error in DDoS detection. In the proposed work, we select the most relevant features, by applying two feature selection techniques, i.e., the Mutual Information (MI) and Random Forest Feature Importance (RFFI) methods. Random Forest (RF), Gradient Boosting (GB), Weighted Voting Ensemble (WVE), K Nearest Neighbor (KNN), and Logistic Regression (LR) are applied to selected features. The experimental results show that the accuracy of RF, GB, WVE, and KNN with 19 features is 0.99. To further study these methods, misclassifications of the methods are analyzed, which lead to more accurate measurements. Extensive experiments conclude that the RF performed well in DDoS attack detection and misclassified only one attack as normal. Comparative results are presented to validate the proposed method. Full article
(This article belongs to the Special Issue Cloud Computing and Symmetry: Latest Advances and Prospects)
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