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Electronics
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Advances in Cloud/Edge Computing Technologies and Applications
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Advances in Cloud/Edge Computing Technologies and Applications

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 9975

Special Issue Editors

Dr. Sheng Zhang

E-Mail Website
Guest Editor
State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
Interests: edge computing; edge intelligence; cloud computing
Special Issues, Collections and Topics in MDPI journals
Dr. Yibo Jin

E-Mail Website
Guest Editor
Huawei Technologies Co., Ltd., Shanghai, China
Interests: heterogeneous computing; edge intelligence
Dr. Bolei Zhang

E-Mail Website
Guest Editor
School of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210049, China
Interests: data mining; reinforcement learning; social networks; edge computing
Prof. Dr. Xiangjie Kong

E-Mail Website
Guest Editor
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
Interests: urban computing; mobile computing; network science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Cloud computing provides scalable high-performance computing capabilities by connecting a large number of independent computing units through high-speed networks. The technical advantages of cloud computing, such as low cost, on-demand resource configuration, and rapid deployment, have spawned various convenient applications such as online shopping, online entertainment (cloud games), and online classrooms. With the development of the Internet of Things and the emergence of AR/VR, edge computing, a technology that can radiate cloud computing capabilities to the user side, also provides users with great convenience. Cloud and edge computing are in a synergistic relationship. Only cloud-edge collaboration can meet the needs of various scenarios (e.g., autonomous driving, smart home), so as to maximize the advantages of cloud computing and edge computing.

This Special Issue focuses on advances in cloud and edge computing technologies and applications. We encourage papers in all areas related to this topic, including task scheduling, data management, resource orchestration and edge intelligence.

Dr. Sheng Zhang
Dr. Yibo Jin
Dr. Bolei Zhang
Prof. Dr. Xiangjie Kong
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

  • cloud computing
  • edge computing
  • cloud-edge collaboration
  • autonomous driving
  • smart home
  • 5G network
  • task scheduling
  • resource orchestration
  • blockchain
  • Internet of Things

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Published Papers (3 papers)

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Research

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21 pages, 2483 KiB  
Article
Fine-Grained Management for Microservice Applications with Lazy Configuration Distribution
by Ning Wang, Lin Wang, Xin Li and Xiaolin Qin
Electronics 2023, 12(16), 3404; https://doi.org/10.3390/electronics12163404 - 10 Aug 2023
Cited by 1 | Viewed by 1305
Abstract
Service mesh is gaining popularity as a microservice architecture paradigm due to its lightness, transparency, and scalability. However, fully releasing configurations to the data plane during the business development phase can result in noticeable performance degradation. Therefore, fine-grained traffic management of microservice applications [...] Read more.
Service mesh is gaining popularity as a microservice architecture paradigm due to its lightness, transparency, and scalability. However, fully releasing configurations to the data plane during the business development phase can result in noticeable performance degradation. Therefore, fine-grained traffic management of microservice applications is crucial to service performance. This paper proposes a novel configuration distribution algorithm, DATM, which utilizes inter-service dependencies from the service call chain to manage data-plane traffic and dynamically maintain cluster services. The proposed algorithms enable on-demand distribution based on the obtained service dependency relationships by combining monitoring, information processing, and policy distribution. We validate the proposed mechanism and algorithms via extensive experiments. We show that the approach reduces the memory usage of data-plane agents and improves system resource utilization. Additionally, this reduces the time to issue configuration while effectively saving storage space and significantly reducing the number of cluster updates. Consequently, this approach ensures application performance and guarantees the quality of microservice applications in clusters. Full article
(This article belongs to the Special Issue Advances in Cloud/Edge Computing Technologies and Applications)
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17 pages, 6203 KiB  
Article
Design and Implementation of Machine Learning-Based Fault Prediction System in Cloud Infrastructure
by Hyunsik Yang and Younghan Kim
Electronics 2022, 11(22), 3765; https://doi.org/10.3390/electronics11223765 - 16 Nov 2022
Cited by 4 | Viewed by 2957
Abstract
The method for ensuring availability in an existing cloud environment is primarily a metric-based fault detection method. However, the existing fault detection method makes it difficult to configure the environment as the cloud size increases and becomes more complex, and it is necessary [...] Read more.
The method for ensuring availability in an existing cloud environment is primarily a metric-based fault detection method. However, the existing fault detection method makes it difficult to configure the environment as the cloud size increases and becomes more complex, and it is necessary to accurately understand the metric in order to use the metric accurately. Furthermore, additional changes are required whenever the monitoring environment changes. In order to solve these problems, various fault detection and prediction methods based on machine learning have recently been proposed. The machine learning-based fault detection and recovery model most commonly proposed in the cloud environment is a supervised machine learning method that learns data relating to fault situations and, based on this data, detects faults. However, there is a limit to fault learning because it is difficult to obtain all of the fault situation data necessary to learn all of the fault situations that occur in a large-scale cloud environment. In addition, it is difficult to detect a fault when a fault that differs from the learned fault pattern occurs. Furthermore, it is necessary to discuss the automatic recovery architecture leading to the fault recovery procedure based on the fault detection results. Therefore, in this paper, we designed and implemented a whole system that predicts faults by detecting fault situations using the anomaly detection method. Full article
(This article belongs to the Special Issue Advances in Cloud/Edge Computing Technologies and Applications)
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Review

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30 pages, 2922 KiB  
Review
Computing Offloading Strategy in Mobile Edge Computing Environment: A Comparison between Adopted Frameworks, Challenges, and Future Directions
by Shuchen Zhou, Waqas Jadoon and Iftikhar Ahmed Khan
Electronics 2023, 12(11), 2452; https://doi.org/10.3390/electronics12112452 - 29 May 2023
Cited by 12 | Viewed by 5088
Abstract
With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to [...] Read more.
With the proliferation of the Internet of Things (IoT) and the development of wireless communication technologies such as 5G, new types of services are emerging and mobile data traffic is growing exponentially. The mobile computing model has shifted from traditional cloud computing to mobile edge computing (MEC) to ensure QoS. The main feature of MEC is to “sink” network resources to the edge of the network to meet the needs of delay-sensitive and computation-intensive services, and to provide users with better services. Computation offloading is one of the major research issues in MEC. In this paper, we summarize the state of the art in task offloading in MEC. First, we introduce the basic concepts and typical application scenarios of MEC, and then we formulate the task offloading problem. In this paper, we analyze and summarize the state of research in the industry in terms of key technologies, schemes, scenarios, and objectives. Finally, we provide an outlook on the challenges and future research directions of computational offloading techniques and indicate the suggested direction of follow-up research work. Full article
(This article belongs to the Special Issue Advances in Cloud/Edge Computing Technologies and Applications)
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