Condori Coaquira, Angel Rosendo

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Condori Coaquira, Angel Rosendo
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Condori Coaquira, Angel Rosendo
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Coaquira, Angel Rosendo Condori
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Universidad Peruana Unión
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  • Publicacion
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    AI-driven optimization in cloud computing: a systematic review of cost, resource management, and security
    (2026-04-30)
    Ito López, Ronaldy Solano
    ;
    Gutierrez Oscata, Romel
    ;
    Condori Coaquira, Angel Rosendo  
    ;
    Gonzales Medina, Ronny Ivan
    ;
    López Gonzales, Javier Linkolk  
    Cloud computing environments face persistent structural challenges in cost control, dynamic resource allocation, and security risk management, which traditional infrastructure approaches fail to address adequately. This systematic literature review aimed to synthesize empirical evidence on the application of artificial intelligence (AI) and machine learning (ML) models for cost optimisation, resource management, and security enhancement in cloud computing environments. Following the PRISMA 2020 guidelines and the Kitchenham-Charters methodology, a structured search was conducted across IEEE Xplore, Web of Science, ScienceDirect, and the ACM Digital Library, covering the period 2020-2025. From an initial pool of 216 records, 18 primary studies were selected after applying the PICOC framework, predefined inclusion and exclusion criteria, and a dual-reviewer quality assessment process yielding substantial inter-rater agreement (Cohen's κ = 0.86). The synthesized evidence demonstrates that predictive provisioning systems and intelligent load-balancing mechanisms reduce operational costs by up to 85%, metaheuristic algorithms such as the Whale Optimization Algorithm and Particle Swarm Optimization improve energy efficiency by 30%-40% and increase resource utilization by up to 80%, and deep learning-based intrusion detection systems achieve accuracy levels exceeding 92%. These findings confirm that AI constitutes a structural mechanism for strengthening economic efficiency, operational resilience, and the sustainability of cloud infrastructures. However, heterogeneity in simulation environments, limited validation in production-scale deployments, and insufficient coverage of virtual machine migration dynamics represent critical gaps requiring standardized benchmarking frameworks and empirical validation in hybrid and multicloud architectures. A quantitative synthesis (Table 1) reveals that metaheuristic algorithms achieve 30%-40% cost and energy efficiency improvements, while ensemble deep learning approaches attain >97% security threat detection rates.
    Citas (OpenAlex): 1metric-badges.view 5
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    LoRaWAN-based system for multivariate data acquisition in high Andean agriculture
    (Elsevier B.V., 2025-10-25)
    Quea Lopez, Godofredo  
    ;
    Condori Coaquira, Angel Rosendo  
    ;
    Otazu Luque, Jorge Eddy  
    ;
    Gutierrez Quispe, Eder  
    High-Andean agriculture in Peru faces significant challenges from climate change and a scarcity of reliable data for precise decision-making. Remote monitoring in these zones is constrained by a lack of Internet connectivity, particularly in hard-to-reach agricultural areas. This study presents a prototype Internet of Things (IoT) system based on LoRaWAN technology for the collection and storage of multivariate data tailored for agriculture in high-Andean zones. The system integrates sensors for temperature, humidity, solar radiation, wind speed, and wind direction, transmitting the information via long-range, low-power communication to an IoT server for storage and analysis. Field tests, conducted at 3,800 m.a.s.l., achieved communication distances of 5 km and 10 km with a 100% successful transmission rate. The sensor nodes were solar powered, enabling autonomous and continuous operation. The results demonstrate that LoRaWAN can overcome connectivity barriers in high-altitude rural environments, offering a scalable and sustainable solution that strengthens climate resilience and optimizes agricultural practices in high-Andean regions.
    Citas (OpenAlex): 5metric-badges.view 4