New Engineering in Cloud Computing and Cloud Data

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (22 November 2022) | Viewed by 10501

Special Issue Editor

School of Computer Science and Engineering, University of Electronic Science & Technologyof China, Chengdu 610054, China
Interests: cloud computing; big data; deep learning; IOT; wireless networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed a startling rise of cloud computing services by sharing resources to achieve agility, scalability, elasticity, and coherence for unaware users. Many applications and information systems are designed in cloud architectures with dedicated policies, strategies, and methods. The rapid growth of engineering techniques in cloud computing offers new opportunities, but also raises many challenges. Therefore, this Special Issue provides a chance for academic and industry professionals to discuss recent engineering progress in the area of cloud computing and cloud data.

This Special Issue will publish high-quality, original research papers, in the overlapping fields of:

  • Business, industry, healthcare, transport, and other cloud-hosted applications;
  • Cloud-based information systems;
  • Cloud databases and data centers;
  • Elastic computing and utility computing;
  • Cloud service models, cloud architectures, and microservice architectures;
  • Content delivery networks;
  • Cloud engineering;
  • Artificial intelligence, machine learning, and deep learning for intelligent cloud services;
  • Security and privacy in could computing;
  • Virtualization for cloud computing.

Prof. Dr. Ming Liu
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. Applied Sciences 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
  • cloud-hosted applications
  • data collection, storage, and distribution
  • IaaS, PaaS, SaaS, MbaaS, FaaS
  • private cloud, public cloud, hybrid cloud, multicloud
  • virtual machines, load balancers, networking
  • artificial intelligence, machine learning, and deep learning
  • optimization and performance evaluation

Published Papers (7 papers)

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Research

19 pages, 4370 KiB  
Article
A Digital Twin-Based Heuristic Multi-Cooperation Scheduling Framework for Smart Manufacturing in IIoT Environment
by Haotian Chen, Sekione Reward Jeremiah, Changhoon Lee and Jong Hyuk Park
Appl. Sci. 2023, 13(3), 1440; https://0-doi-org.brum.beds.ac.uk/10.3390/app13031440 - 21 Jan 2023
Cited by 7 | Viewed by 2121
Abstract
Intertwining smart manufacturing and the Internet of Things (IoT) is known as the Industrial Internet of Things (IIoT). IIoT improves product quality and reliability and requires intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. Recently, it has been increasingly deployed; [...] Read more.
Intertwining smart manufacturing and the Internet of Things (IoT) is known as the Industrial Internet of Things (IIoT). IIoT improves product quality and reliability and requires intelligent connection, real-time data processing, collaborative monitoring, and automatic information processing. Recently, it has been increasingly deployed; however, multi-party collaborative information processing is often required in heterogeneous IIoT. The security and efficiency requirements of each party interacting with other partners have become a significant challenge in information security. This paper proposes an automated smart manufacturing framework based on Digital Twin (DT) and Blockchain. The data used in the DT are all from the cluster generated after blockchain authentication. The processed data in the DT will only be accessed and visualized in the cloud when necessary. Therefore, all the data transmitted in the process are result reports, avoiding the frequent transmission of sensitive data. Simulation results show that the proposed authentication mode takes less time than the standard protocol. In addition, our DT framework for a smart factory deploys the PDQN DRL model, proving to have higher accuracy, stability, and reliability. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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19 pages, 3476 KiB  
Article
Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles
by Jiwei Hua, Bo Zhang, Jinao Wang, Xin Shao and Jinqi Zhu
Appl. Sci. 2023, 13(2), 695; https://0-doi-org.brum.beds.ac.uk/10.3390/app13020695 - 04 Jan 2023
Viewed by 1033
Abstract
Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods [...] Read more.
Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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17 pages, 3487 KiB  
Article
Unmanned Aerial Vehicle Computation Task Scheduling Based on Parking Resources in Post-Disaster Rescue
by Jinqi Zhu, Hui Zhao, Yanmin Wei, Chunmei Ma and Qing Lv
Appl. Sci. 2023, 13(1), 289; https://0-doi-org.brum.beds.ac.uk/10.3390/app13010289 - 26 Dec 2022
Cited by 5 | Viewed by 1079
Abstract
Natural disasters bring huge loss of life and property to human beings. Unmanned aerial vehicles (UAVs) own the advantages of high mobility, high flexibility, and rapid deployment, and are important equipment during post-disaster rescue. However, UAVs usually have restricted battery and computing power. [...] Read more.
Natural disasters bring huge loss of life and property to human beings. Unmanned aerial vehicles (UAVs) own the advantages of high mobility, high flexibility, and rapid deployment, and are important equipment during post-disaster rescue. However, UAVs usually have restricted battery and computing power. They are not fit for performing compute-intensive tasks during rescue. Since there are widespread parking resources in a city, multiple parked vehicles working together to compute the applications from UAVs in a post-disaster rescue is investigated to ensure the quality of experience (QoE) of the UAVs. To execute uploaded task effectively, surviving parked vehicles within the monitoring range of an UAV are arranged into a cluster as much as possible. Then, the task execution cost is analyzed. Furthermore, a deep reinforcement learning (DRL)-based offloading policy is constructed, which interacts with the environment in an intelligent way to achieve optimization goals. The simulation experiments show that the proposed offloading scheme has a higher task completion rate and a lower task execution cost than other baselines schemes. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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15 pages, 583 KiB  
Article
A Novel Convolutional Adversarial Framework for Multivariate Time Series Anomaly Detection and Explanation in Cloud Environment
by Peian Wen, Zhenyu Yang, Lei Wu, Sibo Qi, Juan Chen and Peng Chen
Appl. Sci. 2022, 12(20), 10390; https://0-doi-org.brum.beds.ac.uk/10.3390/app122010390 - 15 Oct 2022
Cited by 5 | Viewed by 1293
Abstract
Anomaly detection is critical to ensure cloud infrastructures’ quality of service. However, due to the complexity of inconspicuous (indistinct) anomalies, high dynamicity, and the lack of anomaly labels in the cloud environment, multivariate time series anomaly detection becomes more difficult. The existing approaches [...] Read more.
Anomaly detection is critical to ensure cloud infrastructures’ quality of service. However, due to the complexity of inconspicuous (indistinct) anomalies, high dynamicity, and the lack of anomaly labels in the cloud environment, multivariate time series anomaly detection becomes more difficult. The existing approaches are rarely effective in meeting these challenges. In this paper, we propose a novel convolutional adversarial model, convolutional-adversarial-training-based integrated anomaly detection with explanation framework (CAT-IADEF), for multivariate time series anomaly detection in the cloud. We adopt three convolutional neural networks to learn sequence features and adversarial training to amplify “slight” anomalies while enhancing the robustness of the model. The dynamic threshold is determined in real time by the peaks over threshold (POT) method to improve detection accuracy. In addition, anomaly explanation is also conducted efficiently by analyzing anomaly score vectors. Experiments with seven data subsets from various public datasets show that CAT-IADEF outperforms state-of-the-art methods. The average F1 score on the seven datasets is 0.907, which is 6.5% higher than the state-of-the-art model and up to 22.1% higher than the baseline method. Furthermore, the proposed anomaly explanation framework is also integrated into various models to verify its effectiveness on the experimental datasets. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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18 pages, 12294 KiB  
Article
A Novel Fault-Tolerant Approach for Dynamic Redundant Path Selection Service Migration in Vehicular Edge Computing
by Jiale Zhao, Yong Ma, Yunni Xia, Mengxuan Dai, Peng Chen, Tingyan Long, Shiyun Shao, Fan Li, Yin Li and Feng Zeng
Appl. Sci. 2022, 12(19), 9987; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199987 - 04 Oct 2022
Cited by 4 | Viewed by 1474
Abstract
Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which [...] Read more.
Vehicular Edge Computing (VEC) provides users with low-latency and highly responsive services by deploying Edge Servers (ESs) close to applications. In practice, vehicles are usually moving rapidly. To ensure the continuity of services, edge service migration technology is in high need, by which an application, infrastructure or any edge-hosted applications or services are not locked into a single vendor and allowed to shift between different edge resource vendors. Nevertheless, due to their complex and dynamic nature, real edge computing environments are error and fault prone and thus the reliability of edge service migrations can be easily compromised if the proactive measures are not taken to counter failures at different levels. In this paper, we propose a novel fault-tolerant approach for Dynamic Redundant Path Selection service migration (DRPS). The DRPS approach consists of path selection algorithm and service migration algorithm. The path selection algorithm is capable of evaluating time-varying failure rates of ESs by leveraging a sliding window-based model and identifying a set of service migration paths. The service migration algorithm incorporates resubmission and replication mechanisms as well and decides edge service migration schemes by choosing multiple redundant migration paths. We also conduct extensive simulations and show that our proposed method outperforms traditional solutions by 17.45%, 13.17%, and 7.22% in terms of ACT, TCR, and AFC, respectively. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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19 pages, 497 KiB  
Article
Cloud-Assisted Privacy Protection Energy Trading Based on IBS and Homomorphic Encryption in IIoT
by Huajie Wang, Yao Xiao, Yong Feng, Qian Qian, Yingna Li and Xiaodong Fu
Appl. Sci. 2022, 12(19), 9509; https://0-doi-org.brum.beds.ac.uk/10.3390/app12199509 - 22 Sep 2022
Cited by 5 | Viewed by 1044
Abstract
The decentralized and tamper-proof features of blockchain technology can solve the problems of low compatibility, poor flexibility, and single point of failure in the traditional Industrial Internet of Things (IIoT). However, the transparency of the blockchain ledger makes the privacy disclosure of users [...] Read more.
The decentralized and tamper-proof features of blockchain technology can solve the problems of low compatibility, poor flexibility, and single point of failure in the traditional Industrial Internet of Things (IIoT). However, the transparency of the blockchain ledger makes the privacy disclosure of users a huge security risk. Given the privacy leakage problem exposed in the existing energy trading scheme based on the blockchain, this paper creatively proposes a privacy protection scheme for IIoT energy trading based on an identity-based signature (IBS) and homomorphic encryption. On the premise of satisfying the transaction traceability and verifiability, this scheme uses IBS technology to provide an anonymous mechanism for energy trading nodes and utilizes Paillier homomorphic encryption to prevent the disclosure of transaction amounts. To meet the high-concurrency and high-throughput energy trading requirements in IIoT, moreover, the proposed scheme combines the off-chain storage with cloud assistance and the off-chain transaction based on PCN to reduce redundant data written into the blockchain and to improve the concurrent trading efficiency, respectively. The security analysis and performance evaluation results show that the proposed scheme can realize the dual privacy protection of identities and transaction amounts in the trading process at the cost of reasonable calculation. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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18 pages, 470 KiB  
Article
Target-Oriented Teaching Path Planning with Deep Reinforcement Learning for Cloud Computing-Assisted Instructions
by Tengjie Yang, Lin Zuo, Xinduoji Yang and Nianbo Liu
Appl. Sci. 2022, 12(18), 9376; https://0-doi-org.brum.beds.ac.uk/10.3390/app12189376 - 19 Sep 2022
Cited by 1 | Viewed by 1449
Abstract
In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimization scheme for cloud computing-assisted instructions, in which a [...] Read more.
In recent years, individual learning path planning has become prevalent in online learning systems, while few studies have focused on teaching path planning for traditional classroom teaching. This paper proposes a target-oriented teaching path optimization scheme for cloud computing-assisted instructions, in which a sequence of learning contents is arranged to ensure the maximum benefit for a given group of students. First, to evaluate the teaching performance, we investigate various student models and define some teaching objectives, including the pass rate, the excellence rate, the average score, and related constraints. Second, a new Deep Reinforcement Learning (DRL)-based teaching path planning method is proposed to tackle the learning path by maximizing a multi-objective target while satisfying all teaching constraints. It adopts a Proximal Policy Optimization (PPO) framework to find a model-free solution for achieving fast convergence and better optimality. Finally, extensive simulations with a variety of commonly used teaching methods show that our scheme provides nice performance and versatility over commonly used student models. Full article
(This article belongs to the Special Issue New Engineering in Cloud Computing and Cloud Data)
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