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Electronics
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Machine Learning for Service Composition in Cloud Manufacturing
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Machine Learning for Service Composition in Cloud Manufacturing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 13079

Special Issue Editors

Dr. Arif Ali Khan

E-Mail Website
Guest Editor
Faculty of Information Technology and Electrical Engineering, University of Oulu, 90570 Oulu, Finland
Interests: quantum software engineering; software process improvement; multi-criteria decision analysis; DevOps; microservices architecture; AI ethics; agile software development; soft computing; evidence-based software engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Mohammad Shameem

E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Koneru Lakshmiyah Education Foundation Deemed to be University, Vaddeswaram, Vijayawada 522503, AP, India
Interests: cloud computing; empirical software engineering; data science; machine learning; agile software development; software process improvement; multi-criteria decision analysis; global software development; DevOps

Special Issue Information

Dear Colleagues, 

Cloud computing is emerging as one of the major enablers for the manufacturing industry; it can transform the traditional manufacturing business model, help it to align product innovation with business strategies, and create intelligent factory networks that encourage effective collaboration. In cloud manufacturing, distributed resources are encapsulated into cloud services and centrally managed. Clients can use different cloud services according to their requirements. Cloud users can request services ranging from product design, manufacturing, testing, management, and all other product life cycle phases. Various machine learning (ML) techniques and approaches (e.g., neural networks, support vector machines, random forests, K-means clustering, feature Selection, etc.) are required to effectively distribute the resources to tackle the issues mainly related to the service composition in cloud manufacturing.

This Special Issue aims to provide a platform for practitioners and researchers to discuss ML techniques' applications for managing cloud manufacturing activities for service composition. This Special Issue provides an opportunity to present the empirical evidence and technical strategies for proposing novel techniques, tools, frameworks, and standards to maximize the significance of ML techniques in cloud manufacturing. We welcome the article covering the ML applications study for service composite in cloud manufacturing.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but not limited to) the following:

  • Machine Learning applications for cloud manufacturing;
  • Cloud service composition using metaheuristic services;
  • Predictive model for cloud manufacturing;
  • Composite service selection;
  • Intelligent cloud service and machine learning;
  • Cloud services composition using Machine Learning approaches;
  • Automatic machine learning composition;
  • Data science of cloud computing and Machine Learning;
  • QOS-based cloud service composition;
  • Fuzzy based approach for composite services;
  • Composite cloud services for IoT based applications.

Dr. Arif Ali Khan
Dr. Mohammad Shameem
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
  • service manufacturing
  • composite service
  • machine learning

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Published Papers (4 papers)

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Research

21 pages, 3051 KiB  
Article
Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm
by Balajee R M and Jayanthi Kannan M K
Electronics 2023, 12(6), 1423; https://doi.org/10.3390/electronics12061423 - 16 Mar 2023
Cited by 12 | Viewed by 3796
Abstract
The network security and cloud environment have been playing vital roles in today’s era due to increased network data transmission, the cloud’s elasticity, pay as you go and global distributed resources. A recent survey for the cloud environment involving 300 organizations in North [...] Read more.
The network security and cloud environment have been playing vital roles in today’s era due to increased network data transmission, the cloud’s elasticity, pay as you go and global distributed resources. A recent survey for the cloud environment involving 300 organizations in North America with 500 or more employees who had spent a minimum of USD 1 million on cloud infrastructure, as per March 2022 statistics, stated that 79% of organizations experienced at least one cloud data breach. In the year 2022, the AWS cloud provider leads the market share with 34% and a USD 200 billion cloud market, proving important and producing the motivation to improve the detection of intrusion with respect to network security on the basis of the AWS cloud dataset. The chosen CSE-CIC-IDS-2018 dataset had network attack details based on the real time attack carried out on the AWS cloud infrastructure. The proposed method here is the hybrid deep learning based approach, which uses the raw data first to do the pre-processing and then for normalization. The normalized data have been feature extracted from seventy-six fields to seven bottlenecks using Principal Component Analysis (PCA); those seven extracted features of every packet have been categorized as two-way soft-clustered (attack and non-attack) using the Smart Monkey Optimized Fuzzy C-Means algorithm (SMO-FCM). The attack cluster data have been further provided as inputs for the deep learning based AutoEncoder algorithm, which provides the outputs as attack classifications. Finally, the accuracy of the results in intrusion detection using the proposed technique (PCA + SMO-FCM + AE) is achieved as 95% over the CSE-CIC-IDS-2018 dataset, which is the highest known for state-of-the-art protocols compared with 11 existing techniques. Full article
(This article belongs to the Special Issue Machine Learning for Service Composition in Cloud Manufacturing)
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18 pages, 3230 KiB  
Article
Scalable and Optimal QoS-Aware Manufacturing Service Composition via Business Process Decomposition
by Jiayan Xiang, Guosheng Kang, Hangyu Cheng, Jianxun Liu, Yiping Wen and Junhua Xu
Electronics 2023, 12(4), 991; https://doi.org/10.3390/electronics12040991 - 16 Feb 2023
Cited by 9 | Viewed by 2182
Abstract
With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, [...] Read more.
With the adoption of service-oriented manufacturing modes, more and more manufacturing services are released over manufacturing service platforms. As it is known, the problem of the QoS (Quality of Service)-aware manufacturing service composition is NP-hard. Thus, the optimization remains a challenging research issue, especially in the situation of large-scale manufacturing service data which arouse a scalability problem as well. To improve both the optimization performance and scalability of the QoS-aware manufacturing service composition, this paper proposes a scalable and optimal QoS-aware manufacturing service composition approach via business process decomposition. Specifically, the service composition process is decomposed by using a refined process structure tree (RPST). Moreover, an optimized service composition is achieved layer by layer based on the refined process structure tree in a bottom-up manner. For the atomic tasks or the compound tasks in the same layer of RPST, the corresponding QoS-aware service selection is optimized by calculating Skyline services, which can be carried out in parallel if necessary. When the optimization arrives at the root node, the complete service composition plans are derived. In our approach, the optimal manufacturing service candidates are picked out stage by stage. In this way, both the optimality and scalability of the whole approach can be guaranteed. Extensive experiments are conducted to verify the optimality and scalability of our approach. Full article
(This article belongs to the Special Issue Machine Learning for Service Composition in Cloud Manufacturing)
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15 pages, 2100 KiB  
Article
Optimization of DevOps Transformation for Cloud-Based Applications
by Ahmed Mateen Buttar, Adeel Khalid, Mamdouh Alenezi, Muhammad Azeem Akbar, Saima Rafi, Abdu H. Gumaei and Muhammad Tanveer Riaz
Electronics 2023, 12(2), 357; https://doi.org/10.3390/electronics12020357 - 10 Jan 2023
Cited by 8 | Viewed by 4112
Abstract
Rapid software development is critical for meeting company objectives and competing more effectively in the competitive IoT infrastructure. DevOps is a growing technique that enables enterprises to provide high-quality software capabilities through automation, to improve team communication, and to increase efficiency across the [...] Read more.
Rapid software development is critical for meeting company objectives and competing more effectively in the competitive IoT infrastructure. DevOps is a growing technique that enables enterprises to provide high-quality software capabilities through automation, to improve team communication, and to increase efficiency across the software product lifecycle. Research problem: Due to the increased demand for new products and technologies, a huge overwork shifted on the organizations for introducing software with pace and to become stable to compete with others. Due to this, the majority of organizations prefer an automated system for product development and require cloud-based applications. The git version control system is used for version management and Docker is used to package code and provide libraries. AWS services are leveraged to deploy an application as a cloud. Jenkins is used as a CI/CD pipeline to manage various phases of development and to make the development process continuous. The ELK stack is used to monitor and visualize the execution of code. In light of the findings, DevOps is an efficient method for cloud application deployment and resource selection based on the relative importance of each optimized objective in terms of value parameters such as cost, memory, and CPU capacity, and that the method can be tailored to specific application requirements. The findings of this analysis indicate that an application can be deployed to the cloud using DevOps techniques. The proposed approach cost 60% less at full weight 1.0 and 11.3% less with no weight compared to the benchmark solution’s 15.078% Full article
(This article belongs to the Special Issue Machine Learning for Service Composition in Cloud Manufacturing)
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23 pages, 5541 KiB  
Article
QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach
by Soumya Snigdha Mohapatra, Rakesh Ranjan Kumar, Mamdouh Alenezi, Abu Taha Zamani and Nikhat Parveen
Electronics 2022, 11(21), 3469; https://doi.org/10.3390/electronics11213469 - 26 Oct 2022
Cited by 4 | Viewed by 1950
Abstract
As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum [...] Read more.
As a result of the proliferation of cloud services in recent years, several service providers now offer services that are functionally identical but have different levels of service, known as Quality of Service (QoS) characteristics. Therefore, offering a cloud assistance arrangement with optimum QoS estimates that fulfilling a customer’s expectations becomes a complicated and demanding task. Several different metaheuristics are presented as potential solutions to this problem. However, most of them are unable to strike a healthy balance between exploring new territory and capitalizing on existing resources. A novel approach is suggested to balance exploration and exploitation via the use of Genetic Algorithms (GA) and the Eagle Strategy algorithm. Cloud computing provides clients with capabilities that are enabled by information technology by using services that are available on demand. To circumvent difficulties such as a delayed convergence rate or an early convergence, this technique allows for the establishment of a healthy equilibrium between exploratory and exploitative activities. The result of the experiment shows that the Eagle Strategy algorithm (ESA) and GA are better than other conventional algorithms at making a globally QoS-based Cloud Service Selection System faster. Full article
(This article belongs to the Special Issue Machine Learning for Service Composition in Cloud Manufacturing)
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