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Applied Sciences
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Cloud Computing: Challenges, Application and Prospects
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Cloud Computing: Challenges, Application and Prospects

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

Deadline for manuscript submissions: 10 March 2025 | Viewed by 4050

Special Issue Editor

Prof. Dr. Gustavo Rau De Almeida Callou

E-Mail Website
Guest Editor
Departamento de Computação, Universidade Federal Rural de Pernambuco (UFRPE), Dois Irmãos, Recife 52171-900, PE, Brazil
Interests: reliability analysis; fault tolerant computing; performance engineering; sustainability; security; cloud computing

Special Issue Information

Dear Colleagues,

Cloud computing has revolutionized how organizations and individuals access, store, and process data. As the field evolves, new challenges, applications, and prospects arise. Applied Sciences (ISSN 2076-3417) invites researchers and practitioners to contribute to a Special Issue focused on advancing the understanding of cloud computing and exploring its challenges, applications, and prospects.

Topics of interest for this Special Issue include, but are not limited to, the following:

  • Cloud infrastructure design and optimization;
  • Security and privacy in cloud computing;
  • Cloud-based big data analytics;
  • Cloud-based machine learning and artificial intelligence;
  • Cloud storage and data management;
  • Cloud service models (IaaS, PaaS, and SaaS) ;
  • Cloud orchestration and management;
  • Cloud-based applications and services;
  • Edge and fog computing in cloud environments;
  • Mobile cloud computing;
  • Green and sustainable cloud computing;
  • Cloud economics and business models;
  • Performance optimization in cloud systems;
  • Cloud interoperability and standards;
  • Cloud-based Internet of Things (IoT) solutions;
  • Cloud-based healthcare and e-health systems;
  • Cloud-based educational technologies;
  • Cloud-based gaming and entertainment.

Prof. Dr. Gustavo Rau De Almeida Callou
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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

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Research

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14 pages, 515 KiB  
Article
Popularity-Aware Service Provisioning Framework in Cloud Environment
by Haneul Ko, Yumi Kim, Bokyeong Kim and Yeunwoong Kyung
Appl. Sci. 2024, 14(18), 8201; https://doi.org/10.3390/app14188201 - 12 Sep 2024
Viewed by 299
Abstract
To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework [...] Read more.
To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than 50% OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement. Full article
(This article belongs to the Special Issue Cloud Computing: Challenges, Application and Prospects)
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19 pages, 859 KiB  
Article
A Longitudinal Study on the Adoption of Cloud Computing in Micro, Small, and Medium Enterprises in Montenegro
by Ivana Ognjanović, Ramo Šendelj, Milica Daković-Tadić and Ines Kožuh
Appl. Sci. 2024, 14(14), 6387; https://doi.org/10.3390/app14146387 - 22 Jul 2024
Viewed by 1330
Abstract
In recent years, small and medium enterprises worldwide have increasingly adopted digital technologies and cloud computing. The pace of this digital transition has varied across countries, and the socioeconomic conditions during the pandemic have further accelerated the digitalization of enterprises. This situation calls [...] Read more.
In recent years, small and medium enterprises worldwide have increasingly adopted digital technologies and cloud computing. The pace of this digital transition has varied across countries, and the socioeconomic conditions during the pandemic have further accelerated the digitalization of enterprises. This situation calls for an examination of the reasons, benefits, and risks associated with enterprises adopting cloud computing in different settings. Our study aimed to collect longitudinal data from 71 Montenegrin micro, small, and medium enterprises. We conducted a repeated measurement study with two measurement periods: 2017 and 2023. The quantitative results were analyzed alongside qualitative data from a semi-structured interview (N = 15) conducted in 2023. The findings revealed substantial shifts in SME attitudes towards cloud computing, highlighting key catalysts and influencing factors such as security, technological accessibility, external expertise, effort expectations, privacy, social influence, perceived security and safety, ease of use, and usefulness. These factors were examined in the context of global digital innovation, the impact of the COVID-19 pandemic, and recent cyberattacks that disrupted national e-services in Montenegro for 3–9 months. The findings provide new insights into how enterprises can become more prepared to effectively use cloud computing. Full article
(This article belongs to the Special Issue Cloud Computing: Challenges, Application and Prospects)
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13 pages, 2287 KiB  
Article
Isolated Forest-Based Prediction of Container Resource Load Extremes
by Chaoxue Wang and Zhenbang Wang
Appl. Sci. 2024, 14(7), 2911; https://doi.org/10.3390/app14072911 - 29 Mar 2024
Viewed by 745
Abstract
Given the wide application of container technology, the accurate prediction of container CPU usage has become a core aspect of optimizing resource allocation and improving system performance. The high volatility of container CPU utilization, especially the uncertainty of extreme values of CPU utilization, [...] Read more.
Given the wide application of container technology, the accurate prediction of container CPU usage has become a core aspect of optimizing resource allocation and improving system performance. The high volatility of container CPU utilization, especially the uncertainty of extreme values of CPU utilization, is challenging to accurately predict, which affects the accuracy of the overall prediction model. To address this problem, a container CPU utilization prediction model, called ExtremoNet, which integrates the isolated forest algorithm, and classification sub-models are proposed. To ensure that the prediction model adequately takes into account critical information on the CPU utilization’s extreme values, the isolated forest algorithm is introduced to compute these anomalous extreme values and integrate them as features into the training data. In order to improve the recognition accuracy of normal and extreme CPU utilization values, a classification sub-model is used. The experimental results show that, on the AliCloud dataset, the model has an R2 of 96.51% and an MSE of 7.79. Compared with the single prediction models TCN, LSTM, and GRU, as well as the existing combination models CNN-BiGRU-Attention and CNN-LSTM, the model achieves average reductions in the MSE and MAE of about 38.26% and 23.12%, proving the effectiveness of the model at predicting container CPU utilization, and provides a more accurate basis for resource allocation decisions. Full article
(This article belongs to the Special Issue Cloud Computing: Challenges, Application and Prospects)
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Review

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35 pages, 741 KiB  
Review
Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
by Jesus Arellano-Uson, Eduardo Magaña, Daniel Morato and Mikel Izal
Appl. Sci. 2024, 14(5), 1987; https://doi.org/10.3390/app14051987 - 28 Feb 2024
Cited by 1 | Viewed by 1088
Abstract
A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the [...] Read more.
A cloud-based interactive application (CIA) is an application running in the cloud with stringent interactivity requirements, such as remote desktop and cloud gaming. These services have experienced a surge in usage, primarily due to the adoption of new remote work practices during the pandemic and the emergence of entertainment schemes similar to cloud gaming platforms. Evaluating the quality of experience (QoE) in these applications requires specific metrics, including interactivity time, responsiveness, and the assessment of video- and audio-quality degradation. Despite existing studies that evaluate QoE and compare features of general cloud applications, systematic research into QoE for CIAs is lacking. Previous surveys often narrow their focus, overlooking a comprehensive assessment. They touch on QoE in broader contexts but fall short in detailed metric analysis. Some emphasise areas like mobile cloud computing, omitting CIA-specific nuances. This paper offers a comprehensive survey of QoE measurement techniques in CIAs, providing a taxonomy of input metrics, strategies, and evaluation architectures. State-of-the-art proposals are assessed, enabling a comparative analysis of their strengths and weaknesses and identifying future research directions. Full article
(This article belongs to the Special Issue Cloud Computing: Challenges, Application and Prospects)
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