ENERGY-AWARE RESOURCE SCHEDULING IN GREEN CLOUD COMPUTING ENVIRONMENTS USING REINFORCEMENT LEARNING
Purpose: The study aims to address the growing environmental concerns caused by the rising energy demands of data centers in cloud computing. It investigates the potential of reinforcement learning (RL)-based scheduling to enhance energy efficiency in cloud environments without compromising service quality. Design/methodology/approach: This research adopts a reinforcement learning approach to energy-aware resource scheduling in cloud computing infrastructures. The model dynamically allocates resources by analyzing real-time factors such as workload variability, energy demand, and environmental conditions, ensuring optimal energy consumption during operations. Findings: The proposed RL-based scheduling strategy significantly reduces energy usage in data centers while maintaining or improving service-level performance. It enables cloud systems to adapt to changing conditions in real-time, resulting in more efficient and sustainable operations. Practical implications: The findings demonstrate a practical pathway for cloud service providers to integrate intelligent scheduling mechanisms that reduce operational costs and carbon footprint. This approach can be implemented across various data center architectures to support greener cloud computing practices. Originality/value: This study contributes to the emerging field of green cloud computing by presenting a novel application of reinforcement learning for energy-aware resource management. It highlights the value of adaptive, intelligent systems in achieving sustainability goals in modern cloud infrastructure.
Copyright (c) 2026 Beura Maddukuri Veluri (Author) https://creativecommons.org/licenses/by-nc/4.0
DETECTION OF VARIOUS TYPES OF CRACKS USING THE INNER ELECTRICAL RESISTIVITY MEASUREMENT IN REINFORCED CONCRETE MEMBERS: A REVIEW
This research aims to detect any types of cracks whether flexural, shear, or torsional generated inside any RC members whether made of normal or heavy weight concrete using efficient non-destructive method such as linear inner electrical resistivity and square inner electrical resistivity measurements. The two efficient parameters used to detect the cracks inside the RC members in real time are the percentage change in inner electrical resistivity between uncracked and cracked members and the decimal logarithm resistivity anisotropy (DLRA) at different setups of measurements inside the cracked members to show the crack's presence and its direction instantaneously.
Inner Electrical Resistivity Measurements
Square Inner Electrical Resistivity Measurement
Linear Inner Electrical Resistivity Measurement
Decimal Logarithm Resistivity Anisotropy (DLRA)
Copyright (c) 2025 Mostafa Hassan, Mohamed Ihab Sherif ELmasry, Nabil Hassan Elashkar (Author) https://creativecommons.org/licenses/by-nc/4.0
SECURITY AND COMPLIANCE CHALLENGES IN CLOUD-NATIVE CI/CD PIPELINES FOR ENTERPRISE SOFTWARE DELIVERY
Purpose: This paper investigates the emerging security and compliance risks associated with cloud-native Continuous Integration and Continuous Deployment (CI/CD) pipelines in enterprise software delivery. It aims to provide a comprehensive understanding of how cloud-native transformation impacts data protection, access control, vulnerability management, and auditability within CI/CD environments. Design/methodology/approach: The study adopts a qualitative, analytical approach by synthesizing current research, industry practices, and regulatory requirements. It examines case studies from enterprise environments and maps CI/CD workflows against prominent compliance frameworks (e.g., ISO 27001, SOC 2, HIPAA) to identify critical vulnerabilities and control gaps. Findings: The research reveals that while cloud-native CI/CD pipelines enhance agility and scalability, they introduce significant security risks if not properly governed. Key findings include a lack of integrated access controls in pipeline tools, poor visibility into vulnerability propagation during automated deployments, and audit challenges due to decentralized cloud infrastructure. Practical implications: Organizations must realign their CI/CD practices with security and compliance requirements to mitigate potential breaches and regulatory penalties. The paper outlines practical strategies for embedding security controls and audit mechanisms into CI/CD workflows, ensuring resilience and trustworthiness in enterprise software delivery. Originality/value: This work provides one of the first in-depth examinations of the intersection between cloud-native CI/CD pipelines and enterprise-grade security/compliance. It offers actionable insights for DevSecOps professionals, compliance teams, and enterprise architects aiming to build secure, compliant, and audit-ready delivery pipelines in a cloud-native context.
Copyright (c) 2026 Dahir Hashi Sheed (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigation of Cloud Security Posture Management Strategies Using Automated Infrastructure Provisioning Tools
Cloud Security Posture Management (CSPM) is a critical domain in ensuring the compliance, integrity, and security of cloud-native environments. With the rapid adoption of Infrastructure as Code (IaC) and automation tools such as Terraform, AWS CloudFormation, and Ansible, organizations are redefining how they approach cloud infrastructure provisioning and its associated security challenges. This paper investigates the integration of automated provisioning tools with CSPM strategies to enhance threat detection, compliance enforcement, and vulnerability management in dynamic cloud environments. We evaluate architectural models, operational workflows, and real-world use cases to highlight the benefits, limitations, and emerging research directions in the automated management of cloud security postures.
Copyright (c) 2022 Murata Akiyuki Kanae (Author) https://creativecommons.org/licenses/by-nc/4.0
BENCHMARKING THE PERFORMANCE OF CONTAINERIZED MICROSERVICES USING DISTRIBUTED TRACING AND DYNAMIC LOAD TESTING
The rapid proliferation of microservices architectures, especially in cloud-native environments, necessitates robust methods for evaluating their performance under real-world conditions. Traditional performance evaluation tools fall short in capturing the complexities of containerized microservices, particularly in heterogeneous deployment environments. This paper proposes an integrated approach combining distributed tracing with dynamic load testing to benchmark performance more holistically. By observing latency, throughput, and resource consumption across service dependencies, our method offers a deeper understanding of performance bottlenecks and scalability limits. We implement this approach in a Kubernetes environment using open-source observability tools and dynamic stress generators, providing a modular benchmarking framework for DevOps and SRE teams. Empirical results demonstrate how fine-grained tracing and adaptive load generation enable proactive performance tuning and improve service reliability under fluctuating workloads.
Copyright (c) 2022 Shohei Ooka Ogawa (Author) https://creativecommons.org/licenses/by-nc/4.0
APPLICATION OF ANT COLONY OPTIMIZATION FOR SCHEDULING CONTAINER DEPLOYMENTS IN CLOUD NATIVE CI/CD PIPELINES
The evolution of software delivery methodologies, particularly with the adoption of cloud-native continuous integration and continuous deployment (CI/CD) pipelines, has led to complex challenges in resource allocation and scheduling of containerized workloads. Traditional heuristic methods often fall short in addressing the dynamic and distributed nature of such environments. This paper proposes the use of Ant Colony Optimization (ACO) as a metaheuristic approach to optimize the scheduling of container deployments in cloud-native CI/CD pipelines. By mimicking the foraging behavior of ants, ACO adapts to changing system states and ensures efficient resource utilization and reduced deployment latency. Simulation-based experiments demonstrate the viability of the approach in handling high-volume, multi-tenant CI/CD tasks across distributed cloud infrastructure.
Copyright (c) 2022 Osamu Shikibu Keigo (Author) https://creativecommons.org/licenses/by-nc/4.0
FROM FEATURES TO FINANCIAL PERSONAS: MAPPING FEATURE TRANSFORMATION EFFICACY TO CUSTOMER ARCHETYPES IN BEHAVIORAL BANKING DATA
Current research in customer behavior prediction within retail banking heavily benchmarks the aggregate performance of feature engineering and machine learning pipelines. While these studies demonstrate that techniques like logarithmic scaling, polynomial expansion, and interaction term creation improve overall classification accuracy for metrics such as customer activity status, they provide scant insight into the heterogeneous effects these transformations have across a bank’s diverse customer base. This study posits that the efficacy of a feature transformation is not universal but is intrinsically linked to the underlying financial behavior pattern, or ‘persona,’ of the customer. To address this critical gap, we conduct a two-stage analytical process on a real-world dataset of 30,000 customers from a major European bank. First, we employ a combination of k-means clustering and Gaussian Mixture Models on raw transactional and demographic features to derive five distinct, interpretable customer archetypes: The Digital Nomad (high-frequency, low-value digital transactions), The Traditional Accumulator (low-frequency, high-value branch-based savings), The Credit-Reliant (revolving credit users), The Multi-Product Holder (diversified product portfolio), and The Dormant Saver. Subsequently, we apply eight common feature transformation techniques (including log1p, Box-Cox, polynomial degrees 2 & 3, and PCA) to create multiple engineered datasets. A Random Forest classifier is then trained and evaluated on each archetype’s subpopulation separately, using both raw and transformed features. Our findings reveal a striking disproportion in transformation utility. For instance, logarithmic transformation dramatically improved recall for The Digital Nomad (from 0.72 to 0.89) by normalizing heavy-tailed transaction frequency data but provided negligible improvement for The Traditional Accumulator. Conversely, interaction terms between branch visit frequency and account tenure were uniquely powerful for predicting the activity of The Traditional Accumulator archetype. Model interpretability tools, specifically SHAP (SHapley Additive exPlanations), were employed post-hoc to deconstruct why certain transformations ‘resonate’ with specific personas. The analysis revealed that for The Credit-Reliant segment, polynomial transformations successfully captured the non-linear, threshold-based relationship between credit utilization ratio and churn risk a pattern absent in other segments. This research makes a pivotal contribution by shifting the paradigm from a one-size-fits-all feature engineering strategy to a persona-aware modeling framework. We demonstrate that strategic feature transformation must be guided by customer segment characteristics, thereby offering bankers and data scientists a nuanced roadmap for developing more precise, interpretable, and ultimately actionable predictive models. The implications extend beyond activity prediction to personalized marketing, risk assessment, and customer lifetime value modeling.
Copyright (c) 2024 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
Benchmarking Prompt Architectures: A Quantitative Study of Contextual and Decomposed Prompting for Complex ETL Code Generation
The integration of large language models (LLMs) into data engineering workflows promises significant acceleration in the development of Extract, Transform, and Load (ETL) pipelines. However, the practical adoption of this paradigm is hindered by a lack of empirical rigor in evaluating the efficacy of different prompt design strategies, particularly for tasks of high complexity. While current pedagogical resources effectively teach the art of prompt crafting, they do not provide quantitative evidence on which prompt architectures yield the most reliable, accurate, and efficient code generation. This study addresses this critical gap by conducting a systematic, benchmark-driven evaluation of two advanced prompt-design paradigms contextual prompting and decomposed prompting against a baseline of generic, direct prompts for the generation of complex ETL logic. Our methodology constructs a novel benchmark suite of 50 challenging data transformation tasks, derived from real-world scenarios, including nested JSON flattening, hierarchical data aggregation, and time-series imputation with conditional logic. For each task, we generate code solutions using the state-of-the-art GPT-4 model across three distinct prompt architectures: a minimal direct prompt, a context-rich prompt incorporating domain-specific schema definitions and constraint annotations, and a decomposed prompt that breaks the problem into a sequenced chain of subtasks. Drawing inspiration from the human-AI collaboration framework explored by Wu et al. (2022) in "PromptChainer: Chaining Large Language Model Prompts through Visual Programming," we formalize the decomposed prompting strategy into a reproducible chain-of-thought process tailored for data engineering. Furthermore, we extend the evaluation principles for code-generating models discussed by Chen et al. (2021) in "Evaluating Large Language Models Trained on Code," by applying a multi-faceted assessment metric. Each generated code artifact is automatically evaluated for functional correctness through unit-test execution on sample datasets, for computational efficiency via runtime profiling, and for robustness through stress testing with edge-case data. Our quantitative results demonstrate a statistically significant superiority of structured prompt architectures over direct prompting. Contextual prompting reduced critical runtime errors by approximately 60% by mitigating schema hallucinations, while decomposed prompting improved functional correctness on complex multi-step tasks by over 45% compared to the baseline. However, we also identify a trade-off: decomposed prompts increased total token consumption and initial latency by an average of 30%. The study concludes that the choice of an optimal prompt architecture is contingent upon the specific task complexity and operational priorities correctness versus latency. These findings provide the first rigorous, evidence-based framework for prompt engineering in data engineering, moving beyond heuristic advice to deliver actionable guidelines for implementing reliable, AI-assisted ETL development. We contribute our benchmark suite and evaluation toolkit to the community to foster further research in this domain.
Copyright (c) 2025 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
MAPPING IMPERFECTIONS TO INSTRUMENTS: A UNIFIED TAXONOMY FOR DATA ENGINEERING IN BEHAVIORAL ECONOMICS
The burgeoning field of cognitive economics seeks to disentangle preferences, beliefs, and decision-making errors, a task impossible with standard choice data alone. While “data engineering” the deliberate design of novel data-generating processes has emerged as the prescribed solution, its application remains ad hoc and domain-specific. This lack of a systematic framework hinders the replicability, comparability, and cumulative progress of research across economic sub-fields. This paper proposes, develops, and partially validates a domain-agnostic “Data Engineering Matrix” (DEM) to formalize the linkage between latent cognitive constructs and the empirical instruments capable of identifying them. The DEM is structured along two primary axes: (1) a typology of target cognitive imperfections, synthesized from behavioral economics and cognitive science (e.g., systematic belief biases like overconfidence; attentional failures like salience effects; and procedural mistakes like heuristic application), and (2) a taxonomy of engineered data instruments, categorized by their informational content (e.g., belief-elicitation protocols, process-tracing data like eye-tracking or MouselabWeb, response latency measures, and interactive, state-contingent choice architectures). The core contribution is a set of principled mappings between these axes, specifying which instrument or combination of instruments provides the necessary variation to separately identify a given construct within a standard economic model. We draw upon and synthesize methodologies from two pivotal recent studies. First, building on the work of Enke & Graeber (2023), “Cognitive Uncertainty,” who employ a sophisticated Bayesian estimation model on a series of surveys and incentivized experiments to decompose uncertainty into distinct cognitive types (e.g., aleatory vs. epistemic, or “fuzzy thinking”), our taxonomy explicitly incorporates the instruments they pioneer namely, finely graded probabilistic surveys and within-subject variation in information provision as a formalized tool for the “beliefs” column of our matrix. Second, we integrate insights from Gabaix & Koijen (2023), “Inattention and the Limits of Inflation Stabilization,” whose macro-finance model uses asset price and flow data to back out a time-varying inattention parameter. While their data is observational, their structural approach exemplifies “model-based data engineering,” a category we formalize, where the economic model itself defines the necessary moment conditions that guide what constitutes engineered data. To demonstrate the DEM’s utility, we conduct two proof-of-concept replication-and-extension studies. In the first, we apply the DEM to a classic problem of retirement savings, showing how the matrix prescribes a specific sequence: belief elicitation on returns (following Enke & Graeber’s method) followed by a process-tracing analysis of information acquisition (e.g., using a pension simulator with clickstream data) to separate present bias from exponential growth bias. In the second, we apply the DEM to a laboratory market experiment on price formation, illustrating how the matrix guides the integration of communication transcripts (as a form of process data) with trading outcomes to disentangle strategic uncertainty from fundamental uncertainty. Our results indicate that research designs informed by the DEM achieve significantly higher out-of-sample predictive validity in identifying the dominant cognitive mechanism at play compared to single-instrument approaches. The paper concludes by discussing the DEM’s role as a tool for research design, peer evaluation, and the development of a cumulative science of imperfect decision-making. It argues that such a framework is a prerequisite for the broader application of data engineering beyond cognitive economics, facilitating symbiotic advances in theory and measurement.
Copyright (c) 2026 Rajitha Gentyala (Author) https://creativecommons.org/licenses/by-nc/4.0
A Comparative Study of Monitoring Telemetry Pipelines with Prometheus and Grafana in Cloud-Native Java Applications
In cloud-native Java applications, monitoring telemetry pipelines is critical for observability, performance tuning, and reliability. This paper presents a comparative study of two popular open-source tools—Prometheus (for metrics collection and alerting) and Grafana (for visualization and dashboarding)—in the context of a microservices-based Java application deployed on Kubernetes. We instrument the Java services using JMX exporters, configure Prometheus to scrape metrics, and build Grafana dashboards to visualize latency, throughput, error rates and JVM metrics. Key evaluation criteria include setup complexity, scalability (scrape frequency and time-series retention), query performance, alerting responsiveness, dashboard usability, and resource overhead. Our results show that while Prometheus excels at time-series collection and alerting, Grafana significantly enhances interpretability via dashboards, but careful design is needed to scale effectively. We also identify trade-offs around data retention, cardinality, and query workload. Practical recommendations for cloud-native Java developers are provided.
Copyright (c) 2022 Glomb Asgarova Caspary (Author) https://creativecommons.org/licenses/by-nc/4.0
Algorithmic Generation of GraphQL Schema Definitions Using Large Language Models Trained on Open-Source APIs
This paper explores the use of Large Language Models (LLMs) for the algorithmic generation of GraphQL schema definitions derived from open-source API documentation. By leveraging pretrained transformer architectures fine-tuned on structured API datasets, schema components such as queries, mutations, and type relationships can be automatically constructed with minimal developer input. The study evaluates model accuracy, schema validity, and integration efficiency with Node.js and Apollo Server environments. Experiments reveal that LLM-generated schemas maintain functional correctness while reducing development effort by over 60%. Furthermore, schema optimization techniques improve model-driven inference consistency across heterogeneous API sources, underscoring the potential of AI-augmented schema engineering.
Copyright (c) 2022 Baric Finck Skala (Author) https://creativecommons.org/licenses/by-nc/4.0
END-TO-END SECURITY MODELING IN MICROSERVICES ARCHITECTURES USING OAUTH2 AND JWT IN A SPRING BOOT ECOSYSTEM
Modern microservices architectures require robust security mechanisms that can scale seamlessly across services. This paper explores a comprehensive end-to-end security model using OAuth2 and JWT (JSON Web Tokens) within a Spring Boot ecosystem. We propose a layered model that supports fine-grained access control, token validation, and secure communication between distributed services. Integration with Spring Security simplifies enforcement, while OAuth2 provides industry-standard authorization flows.
Copyright (c) 2022 Pokorny Lagger Bognar (Author) https://creativecommons.org/licenses/by-nc/4.0
Classification Models for Identifying Safety Hazards from Wearable Device Data in Construction Sites
The integration of wearable technologies in construction has revolutionized safety management by enabling real-time monitoring of workers' physiological and environmental conditions. This paper explores the application of classification models to analyze data from wearable devices, aiming to identify safety hazards proactively on construction sites. By leveraging machine learning algorithms, the study demonstrates how predictive analytics can enhance hazard detection, thereby reducing accidents and improving overall site safety.
Copyright (c) 2022 Christopher Fuentes William (Author) https://creativecommons.org/licenses/by-nc/4.0
MACHINE LEARNING DRIVEN QUALITY CONTROL SYSTEMS FOR DEFECT DETECTION IN STRUCTURAL COMPONENT FABRICATION
Structural component fabrication is a critical phase in engineering where precision and integrity are paramount. Traditional quality control systems often fall short in terms of adaptability and real-time responsiveness. This paper explores machine learning (ML) applications in automated defect detection, demonstrating their potential to significantly enhance fabrication reliability. Leveraging image recognition, anomaly detection, and supervised learning models, ML systems offer cost-effective, scalable, and high-accuracy alternatives to manual inspection. Through a review of prior works and analysis of recent advancements, we propose a framework integrating convolutional neural networks (CNNs) and real-time sensors to detect and classify fabrication defects with high precision.
Copyright (c) 2022 Pablo Ross Susan (Author) https://creativecommons.org/licenses/by-nc/4.0
MULTI-AGENT ARTIFICIAL INTELLIGENCE SYSTEMS FOR COORDINATED TASK SCHEDULING IN COMPLEX CONSTRUCTION PROJECTS
The complexity and scale of modern construction projects demand dynamic, real-time scheduling systems capable of handling diverse interdependencies across tasks, resources, and stakeholders. This paper proposes the application of Multi-Agent Artificial Intelligence (MAAI) systems to manage coordinated task scheduling in construction environments. Through a review of key literature, analysis of agent-based models, and exploration of AI integration, we demonstrate how MAAI enhances efficiency, conflict resolution, and adaptability. Simulation results highlight the capacity of agent-based systems to outperform traditional methods under uncertainty and changing project conditions.
Copyright (c) 2022 Kimberly Calvo William (Author) https://creativecommons.org/licenses/by-nc/4.0
DAMAGE DETECTION IN REINFORCED CONCRETE STRUCTURES USING VIBRATION-BASED MONITORING TECHNIQUES
Reinforced concrete (RC) structures are fundamental in modern infrastructure, yet their integrity is frequently challenged by environmental stresses, aging, and loading. Vibration-based monitoring techniques (VBMT) have emerged as reliable methods for early damage detection due to their sensitivity to changes in dynamic behavior. This study presents a concise evaluation of VBMT, focusing on modal parameters and machine learning integrations. A literature survey underpins theoretical evolution, while graphical illustrations show real-world applicability. Flowcharts outline decision-making processes in VBMT frameworks, and tables summarize key algorithmic approaches. The paper provides recommendations for implementing smart VBMT systems in civil infrastructure management.
Copyright (c) 2022 Christopher Antonio Labanyi (Author) https://creativecommons.org/licenses/by-nc/4.0
PERFORMANCE-BASED SEISMIC DESIGN FRAMEWORKS FOR RESILIENT INFRASTRUCTURE DEVELOPMENT
Performance-Based Seismic Design (PBSD) frameworks aim to develop infrastructure that not only resists earthquake forces but also ensures quick functionality recovery post-event. This paper outlines the theoretical foundation, implementation challenges, and potential innovations in PBSD for resilient development. The framework emphasizes risk-informed decision-making, multilevel performance targets, and resilience metrics. With increasing urbanization and seismic threats, the adoption of PBSD is essential to mitigate infrastructure failures and economic disruptions.
Copyright (c) 2022 Adelaida Corbalan Ryan (Author) https://creativecommons.org/licenses/by-nc/4.0
AUGMENTED REALITY TOOLS FOR ENHANCING WORKER TRAINING AND DESIGN VISUALIZATION IN CONSTRUCTION PROJECTS
Augmented Reality (AR) has emerged as a transformative technology in the construction sector, facilitating interactive design visualization and immersive worker training. As the construction industry grapples with skilled labor shortages and rising design complexity, AR provides real-time, on-site simulation environments. These tools enhance decision-making, increase accuracy, and reduce errors. This paper explores the contemporary applications of AR tools in training and visualization, reviewing past literature and evaluating recent advancements through quantitative and qualitative assessments.
Copyright (c) 2022 Salvador Giner Carmen (Author) https://creativecommons.org/licenses/by-nc/4.0
NATURAL LANGUAGE PROCESSING APPLICATIONS FOR DOCUMENT AUTOMATION AND CLAIM ANALYSIS IN CONSTRUCTION CONTRACTS
Natural Language Processing (NLP) has emerged as a transformative tool in the construction industry, particularly in the domains of document automation and claim analysis. Given the legal and contractual complexity inherent in construction projects, NLP enables systematic processing, classification, and extraction of information from construction contracts, streamlining operations and mitigating disputes. This paper explores current applications of NLP in this field, reviews pertinent literature, and proposes a structured methodology for incorporating NLP tools into contractual workflows.
Copyright (c) 2022 Xudayberdiyeva Kholdorov (Author) https://creativecommons.org/licenses/by-nc/4.0
Autonomous Code Review and Refactoring Suggestions Using AI-Augmented Software Engineering Agents
The increasing complexity of modern software systems necessitates intelligent tools for code maintenance and quality assurance. This paper explores the integration of AI-augmented software engineering agents for autonomous code review and refactoring. Drawing on recent advances in machine learning, multi-agent systems, and large language models (LLMs), we present the evolution, methodologies, and impact of these intelligent agents. A detailed literature review and a comparative analysis of current systems are included, highlighting trends, limitations, and future directions.
Copyright (c) 2022 Nomura Ishikane Ichikawa (Author) https://creativecommons.org/licenses/by-nc/4.0
Closed-Loop Feedback Systems in DevOps Automation Using AI for Predictive Failure Recovery and Deployment Rollbacks
The integration of Artificial Intelligence (AI) into DevOps pipelines has transformed modern software engineering by enabling closed-loop feedback systems that autonomously predict, detect, and recover from deployment failures. These intelligent systems significantly reduce Mean Time to Recovery (MTTR), enhance system availability, and automate rollback processes through proactive interventions. This paper explores the architecture, operational model, and advantages of AI-powered feedback mechanisms in continuous delivery environments. It also presents a critical analysis of existing approaches, outlines key components such as anomaly detection and automated rollback, and examines the measurable impact of AI in improving deployment resilience. Empirical metrics, conceptual diagrams, and comparative evaluations provide a comprehensive understanding of predictive intelligence in DevOps automation.
Copyright (c) 2022 Risa Minato Wataya (Author) https://creativecommons.org/licenses/by-nc/4.0
Workflow Optimization in Function-as-a-Service Systems Through Intelligent Event Routing and Cold Start Profiling
Function-as-a-Service (FaaS) revolutionizes cloud-native application deployment by providing stateless execution environments. However, challenges such as cold start latency and inefficient workflow routing degrade performance. This paper explores intelligent event routing and cold start profiling as optimization strategies. We present a model combining routing heuristics and runtime profiling to mitigate latency and optimize throughput. Empirical data shows up to 53% reduction in cold start latency and 35% improvement in event throughput in simulated environments.
Copyright (c) 2022 Kenzaburo Natsuo Kirino Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
END-TO-END PERFORMANCE MONITORING OF SERVERLESS ARCHITECTURES IN LATENCY-CRITICAL APPLICATION WORKLOADS
Serverless computing has emerged as a disruptive cloud paradigm, enabling developers to deploy code without managing infrastructure. However, its benefits come with performance trade-offs, particularly in latency-critical application workloads. This paper explores the challenges and solutions associated with end-to-end performance monitoring of serverless systems, where traditional observability techniques may fail due to dynamic execution models, cold starts, and third-party dependencies. Through a synthesis of academic literature and empirical data from AWS Lambda, Azure Functions, and Google Cloud Functions, we present a comprehensive evaluation of latency bottlenecks. A novel hybrid monitoring framework is proposed that leverages OpenTelemetry tracing, machine learning-based anomaly detection, and orchestration-aware instrumentation. Our results demonstrate that strategic monitoring interventions can reduce latency violations by over 30% in multi-step workflows.
Copyright (c) 2026 Espido Cristina Morales Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
MODEL-DRIVEN ENGINEERING FOR MICROSERVICES DEPLOYMENT WITH FOCUS ON SERVICE INTEROPERABILITY AND API GOVERNANCE
Microservices architecture, while enabling modular and scalable system development, introduces complexities in deployment and interoperability. Model-Driven Engineering (MDE) addresses these issues by offering abstract modeling mechanisms to automate deployment, enforce consistency, and govern APIs. This paper investigates the application of MDE in microservices deployment with a focus on enhancing service interoperability and API governance. It includes a structured literature review, an architecture model, comparative metrics, and use-case data analysis.
Copyright (c) 2026 Quevedo Carmen Laforet Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
TEMPORAL COUPLING AND DATA CONSISTENCY CHALLENGES IN SYNCHRONOUS VERSUS ASYNCHRONOUS MICROSERVICES COMMUNICATION
Modern microservices-based systems require robust communication models to ensure responsiveness, scalability, and data consistency. Synchronous and asynchronous communication models each bring distinct trade-offs. This paper investigates the challenges of temporal coupling and data consistency in both models. We use a combination of literature analysis, data tables, and diagrams to show how communication mechanisms impact service decoupling, latency, and consistency. Empirical insights from previous studies are synthesized into a comparative framework to aid architectural decision-making.
Copyright (c) 2026 Carmen Antonio Barca Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
EXPLAINABILITY AND FAIRNESS ASSESSMENT IN LARGE LANGUAGE MODELS FOR EDUCATIONAL TECHNOLOGY APPLICATIONS
Large Language Models (LLMs) have revolutionized educational technology by enabling personalized learning experiences, automated grading, content generation, and more. However, with the widespread use of LLMs in educational settings, there is an increasing need to assess their fairness and explainability. This paper examines the state-of-the-art methods for evaluating these two critical aspects of LLMs, focusing on their application within educational technology. We analyze recent advancements in explainability techniques, fairness metrics, and the potential risks associated with bias. By providing a comprehensive review of these issues, we aim to highlight the importance of building more transparent and fair AI systems for educational contexts.
Copyright (c) 2026 Federico Garcia Pedro Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
DESIGNING FOR FAILURE PATTERNS FOR RESILIENT AND STATEFUL MICROSERVICES IN ASYNCHRONOUS MORTGAGE LIFECYCLE MANAGEMENT
The modern mortgage lifecycle is a complex, long-running, and stateful business process that traditional monolithic architectures struggle to manage with resilience and scalability. The migration to a cloud-native microservices architecture introduces new challenges in managing distributed state and ensuring data consistency across asynchronous services. This paper presents a set of architectural patterns and a reference implementation for building resilient, stateful microservices capable of gracefully handling failures inherent in distributed systems. By leveraging the Saga pattern with compensatory transactions, an Event-Driven backbone, and durable execution engines, we propose a framework that decomposes the 30-45 day mortgage process into a series of atomic, reversible events. The paper includes a quantitative analysis demonstrating a significant reduction in system-wide failures and manual intervention rates, providing a blueprint for achieving high reliability in financial services technology.
Copyright (c) 2026 Chadwick Millidge Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
THE COMPOSABLE ENTERPRISE: BUILDING MORTGAGE TECHNOLOGY PLATFORMS FROM REUSABLE AND DOMAIN-ALIGNED MICROSERVICES AND DATA PRODUCTS
The mortgage industry, traditionally characterized by monolithic, legacy technology stacks, faces immense pressure to improve agility, reduce costs, and enhance customer experience. This paper explores the paradigm of the "Composable Enterprise" as a modern architectural blueprint for mortgage technology. It proposes that by constructing platforms from finely grained, reusable, and domain-aligned microservices and data products, lenders can achieve unprecedented levels of flexibility, scalability, and resilience. The paper details the architectural components, including domain-driven design (DDD) for service boundaries, API-led connectivity, and the treatment of data as a product. It argues that this approach directly addresses key industry pain points such as long cycle times, integration bottlenecks, and data silos, enabling lenders to compose and reconfigure business capabilities rapidly in response to market and regulatory changes.
Copyright (c) 2026 Carollo Pantazi Brooks Journal (Author) https://creativecommons.org/licenses/by-nc/4.0
DATA QUALITY CHALLENGES AND PREPROCESSING TECHNIQUES FOR ROBUST MACHINE LEARNING PIPELINES
The exponential growth of data has intensified the importance of high-quality data preprocessing to ensure robust machine learning (ML) pipelines. Despite advanced model architectures, the integrity of input data remains a critical determinant of model performance and generalization. This paper reviews the prevailing data quality challenges, outlines state-of-the-art preprocessing techniques, and evaluates their roles in contemporary ML workflows. Emphasis is placed on data completeness, consistency, accuracy, and handling noisy, imbalanced, and heterogeneous datasets. The study also contextualizes these challenges with reference to recent advances in ML tooling and automation. Literature is reviewed to establish a baseline understanding, followed by current perspectives that align with industrial-scale AI systems. diagrams and tables highlight the comparative effectiveness of preprocessing approaches and common quality pitfalls across domains.
Copyright (c) 2026 Jessica Kirstin F Journal (Author) https://creativecommons.org/licenses/by-nc/4.0