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Computers
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Cloud Computing and Big Data Mining
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Cloud Computing and Big Data Mining

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Cloud Continuum and Enabled Applications".

Deadline for manuscript submissions: 28 February 2026 | Viewed by 19524

Special Issue Editor

Dr. Rao Mikkilineni

E-Mail Website
Guest Editor
1. Barowsky School of Business, Dominican University of California, San Rafael, CA 94901, USA
2. Ageno School of Business, Golden Gate University, San Francisco, CA 94105, USA
Interests: cloud computing; enterprise software; virtualization; data center; artificial intelligence; distributed self-regulating software

Special Issue Information

Dear Colleagues,

The fields of cloud computing and big data mining are undergoing rapid evolution, underscored by significant advancements in both technologies and methodologies. Artificial Intelligence (AI) and Machine Learning (ML) technologies are increasingly integrated into cloud services, leading to more intelligent and efficient cloud solutions. Edge computing infrastructure, where computing resources and storage are brought closer to end users, is pushing data processing closer to the user’s device, instead of relying on a distant central location.

Tailored IT architectures that span multiple hybrid cloud servers are enabling the flexibility to choose services from various cloud vendors or providers, and they incorporate multi-cloud solutions for distributed data analytics. The adoption of trending technologies such as the Internet of Things (IoT), blockchain, Kubernetes, and Docker is expected to pave the way for emerging technologies such as quantum computing, cloud gaming, and augmented and virtual reality (VR/AR) in the coming years. As data analytics become more intimately integrated with distributed software applications, new approaches will inevitably emerge.

This Special Issue is designed to serve as a platform for researchers and practitioners to share their most recent research findings, practical experiences, and innovative approaches within these domains. We welcome submissions of original research papers, insightful case studies, and comprehensive review articles. Topics of interest encompass, but are not limited to, architectures in cloud computing, algorithms for big data mining both in the centralized and distributed infrastructures, techniques in data analytics, and applications of machine learning.

We invite high-quality papers that discuss the technologies and methodologies advancing big data analytics while utilizing distributed cloud computing resources.

Dr. Rao Mikkilineni
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 250 words) can be sent to the Editorial Office for assessment.

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. Computers 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 1800 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
  • big data mining
  • data analytics
  • machine learning
  • artificial intelligence
  • data security
  • data privacy
  • internet of things (IoT)
  • edge computing
  • distributed computing
  • data warehousing
  • data processing
  • cloud storage
  • data visualization
  • hybrid cloud servers
  • edge cloud servers
  • integration of the internet of things

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Published Papers (6 papers)

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Research

Jump to: Review

23 pages, 3559 KB  
Article
From Static Prediction to Mindful Machines: A Paradigm Shift in Distributed AI Systems
by Rao Mikkilineni and W. Patrick Kelly
Computers 2025, 14(12), 541; https://doi.org/10.3390/computers14120541 - 10 Dec 2025
Viewed by 1283
Abstract
A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted [...] Read more.
A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
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19 pages, 913 KB  
Article
Decision-Making Model for Risk Assessment in Cloud Computing Using the Enhanced Hierarchical Holographic Modeling
by Auday Qusay Sabri and Halina Binti Mohamed Dahlan
Computers 2025, 14(11), 491; https://doi.org/10.3390/computers14110491 - 13 Nov 2025
Viewed by 604
Abstract
Risk assessment is critical for securing and sustaining operational resilience in cloud computing. Traditional approaches often rely on single-objective or subjective weighting methods, limiting their accuracy and adaptability to dynamic cloud conditions. To address this gap, this study provides a framework for multi-layered [...] Read more.
Risk assessment is critical for securing and sustaining operational resilience in cloud computing. Traditional approaches often rely on single-objective or subjective weighting methods, limiting their accuracy and adaptability to dynamic cloud conditions. To address this gap, this study provides a framework for multi-layered decision-making using an Enhanced Hierarchical Holographic Modeling (EHHM) approach for cloud computing security risk assessment. Two methods were used, the Entropy Weight Method (EWM) and Criteria Importance Through Intercriteria Correlation (CRITIC), to provide a multi-factor decision-making risk assessment framework across the different security domains that exist with cloud computing. Additionally, fuzzy set theory provided the respective levels of complexity dispersion and ambiguities, thus facilitating an accurate and objective participation for a cloud risk assessment across asymmetric information. The trapezoidal membership function measures the correlation, rank, and scores, and was applied to each corresponding cloud risk security domain. The novelty of this re-search is represented by enhancing HHM with an expanded security-transfer domain that encompasses the client side, integrating dual-objective weighting (EWM + CRITIC), and the use of fuzzy logic to quantify asymmetric uncertainty in judgments unique to this study. Informed, data-related, multidimensional cloud risk assessment is not reported in previous studies using HHM. The different Integrated Weight measures allowed for accurate risk judgments. The risk assessment across the calculated cloud computing security domains resulted in a total score of 0.074233, thus supporting the proposed model in identifying and prioritizing risk assessment. Furthermore, the scores of the cloud computing dimensions highlight EHHM as a suitable framework to support and assist corporate decision-making in cloud computing security activity and informed risk awareness with innovative activity amongst a turbulent and dynamic cloud computing environment with corporate operational risk. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
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52 pages, 3006 KB  
Article
Empirical Performance Analysis of WireGuard vs. OpenVPN in Cloud and Virtualised Environments Under Simulated Network Conditions
by Joel Anyam, Rajiv Ranjan Singh, Hadi Larijani and Anand Philip
Computers 2025, 14(8), 326; https://doi.org/10.3390/computers14080326 - 13 Aug 2025
Cited by 2 | Viewed by 9879
Abstract
With the rise in cloud computing and virtualisation, secure and efficient VPN solutions are essential for network connectivity. We present a systematic performance comparison of OpenVPN (v2.6.12) and WireGuard (v1.0.20210914) across Azure and VMware environments, evaluating throughput, latency, jitter, packet loss, and resource [...] Read more.
With the rise in cloud computing and virtualisation, secure and efficient VPN solutions are essential for network connectivity. We present a systematic performance comparison of OpenVPN (v2.6.12) and WireGuard (v1.0.20210914) across Azure and VMware environments, evaluating throughput, latency, jitter, packet loss, and resource utilisation. Testing revealed that the protocol performance is highly context dependent. In VMware environments, WireGuard demonstrated a superior TCP throughput (210.64 Mbps vs. 110.34 Mbps) and lower packet loss (12.35% vs. 47.01%). In Azure environments, both protocols achieved a similar baseline throughput (~280–290 Mbps), though OpenVPN performed better under high-latency conditions (120 Mbps vs. 60 Mbps). Resource utilisation showed minimal differences, with WireGuard maintaining slightly better memory efficiency. Security Efficiency Index calculations revealed environment-specific trade-offs: WireGuard showed marginal advantages in Azure, while OpenVPN demonstrated better throughput efficiency in VMware, though WireGuard remained superior for latency-sensitive applications. Our findings indicate protocol selection should be guided by deployment environment and application requirements rather than general superiority claims. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
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16 pages, 452 KB  
Article
GARMT: Grouping-Based Association Rule Mining to Predict Future Tables in Database Queries
by Peixiong He, Libo Sun, Xian Gao, Yi Zhou and Xiao Qin
Computers 2025, 14(6), 220; https://doi.org/10.3390/computers14060220 - 6 Jun 2025
Viewed by 1277
Abstract
In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing [...] Read more.
In modern data management systems, structured query language (SQL) databases, as a mature and stable technology, have become the standard for processing structured data. These databases ensure data integrity through strongly typed schema definitions and support complex transaction management and efficient query processing capabilities. However, data sparsity—where most fields in large table sets remain unused by most queries—leads to inefficiencies in access optimization. We propose a grouping-based approach (GARMT) that partitions SQL queries into fixed-size groups and applies a modified FP-Growth algorithm (GFP-Growth) to identify frequent table access patterns. Experiments on a real-world dataset show that grouping significantly reduces runtime—by up to 40%—compared to the ungrouped baseline while preserving rule relevance. These results highlight the practical value of query grouping for efficient pattern discovery in sparse database environments. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
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17 pages, 2984 KB  
Article
Educational Resource Private Cloud Platform Based on OpenStack
by Linchang Zhao, Guoqing Hu and Yongchi Xu
Computers 2024, 13(9), 241; https://doi.org/10.3390/computers13090241 - 23 Sep 2024
Cited by 4 | Viewed by 3122
Abstract
With the rapid development of the education industry and the expansion of university enrollment scale, it is difficult for the original teaching resource operation and maintenance management mode and utilization efficiency to meet the demands of teachers and students for high-quality teaching resources. [...] Read more.
With the rapid development of the education industry and the expansion of university enrollment scale, it is difficult for the original teaching resource operation and maintenance management mode and utilization efficiency to meet the demands of teachers and students for high-quality teaching resources. OpenStack and Ceph technologies provide a new solution for optimizing the utilization and management of educational resources. The educational resource private cloud platform built by them can achieve the unified management and self-service use of the computing resources, storage resources, and network resources required for student learning and teacher instruction. It meets the flexible and efficient use requirements of high-quality teaching resources for student learning and teacher instruction, reduces the construction cost of informationization investment in universities, and improves the efficiency of teaching resource utilization. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
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Review

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28 pages, 340 KB  
Review
Revolutionizing Data Exchange Through Intelligent Automation: Insights and Trends
by Yeison Nolberto Cardona-Álvarez, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez
Computers 2025, 14(5), 194; https://doi.org/10.3390/computers14050194 - 17 May 2025
Viewed by 2147
Abstract
This review paper presents a comprehensive analysis of the evolving landscape of data exchange, with a particular focus on the transformative role of emerging technologies such as blockchain, field-programmable gate arrays (FPGAs), and artificial intelligence (AI). We explore how the integration of these [...] Read more.
This review paper presents a comprehensive analysis of the evolving landscape of data exchange, with a particular focus on the transformative role of emerging technologies such as blockchain, field-programmable gate arrays (FPGAs), and artificial intelligence (AI). We explore how the integration of these technologies into data management systems enhances operational efficiency, precision, and security through intelligent automation and advanced machine learning techniques. The paper also critically examines the key challenges facing data exchange today, including issues of interoperability, the demand for real-time processing, and the stringent requirements of regulatory compliance. Furthermore, it underscores the urgent need for robust ethical frameworks to guide the responsible use of AI and to protect data privacy. In addressing these challenges, the paper calls for innovative research aimed at overcoming current limitations in scalability and security. It advocates for interdisciplinary approaches that harmonize technological innovation with legal and ethical considerations. Ultimately, this review highlights the pivotal role of collaboration among researchers, industry stakeholders, and policymakers in fostering a digitally inclusive future—one that strengthens data exchange practices while upholding global standards of fairness, transparency, and accountability. Full article
(This article belongs to the Special Issue Cloud Computing and Big Data Mining)
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