International Journal of Emerging Research in Engineering and Technology
Resilient Middleware Ecosystems: Integrating Fault Tolerance, Recovery-Oriented Design, and Observability for Real-Time Enterprise Integration
Real-time enterprise integration environments depend on middleware platforms to sustain uninterrupted communication across distributed services, cloud infrastructures, and mission-critical workflows. However, traditional middleware systems treat fault handling, recovery, and operational visibility as separate issues instead of as parts of a single design. This article synthesizes three complementary design paradigms fault-tolerant middleware architecture, recovery-oriented middleware design, and middleware-centric observability with distributed tracing into an integrated resilience framework for real-time enterprise environments. The proposed Integrated Resilient Middleware Framework (IRMF) embeds proactive failure detection, automated workflow state recovery, and end-to-end message lifecycle tracing as first-class architectural concerns. By evaluating each architectural dimension against conventional middleware deployments, this study demonstrates that a unified resilience approach significantly reduces Mean Time to Recovery (MTTR), preserves workflow continuity under partial system failure, and elevates operational intelligence across distributed integration pipelines. The framework offers enterprise architects a blueprint for constructing middleware ecosystems capable of sustaining high availability, data consistency, and operational transparency in hybrid and multi-cloud environments.
Low-Latency, High-Throughput Middleware for Real-Time Enterprise Integration: Technical Design, Societal Impact, and Policy Considerations
Modern enterprises cannot afford to wait. The systems they depend on trading engines, patient monitors, logistics networks, IoT platforms generate data continuously, and that data must be acted upon immediately. Middleware is the layer that makes this possible: it connects heterogeneous systems, manages message flow, and keeps distributed services communicating without collapsing under load. But designing middleware that performs reliably at scale is only part of the challenge. When these systems govern automated decisions and handle sensitive personal data, they carry responsibilities that go well beyond throughput metrics. This article examines middleware from both angles. On the technical side, event-driven architecture, in-memory data grids, predictive load balancing, asynchronous communication, and distributed consensus are discussed as the foundational strategies for achieving real-time performance in enterprise environments. On the societal side, the article engages with algorithmic fairness, data privacy, economic equity, and regulatory compliance not as external obligations, but as design requirements that belong in the architecture from the beginning. Deployment experiences from financial services, healthcare monitoring, and smart city infrastructure are used to ground the discussion in real operational contexts. Looking forward, autonomous resource management, ethical-by-design pipelines, and policy-aware middleware are identified as the capabilities that will define the next generation of enterprise integration.
Power over Ethernet (PoE): Reimagining Residential Electrical Infrastructure in the United States
Approximately 146 million housing units comprise the U.S. residential building stock. These units are primarily powered by twentieth century 120 volt alternating current (AC) wiring architectures that were not created for today’s device dense, intelligent home environments. Power over Ethernet (PoE), a technology standardized through IEEE 802.3 to deliver low voltage direct current (DC) power and data simultaneously via a single Category-rated cable, has matured through successive standards to deliver up to 90 watts per port. Although PoE has been widely implemented in commercial and enterprise settings, it has yet to be fully explored as a complementary infrastructure layer in residential construction. This article will argue that PoE offers a viable alternative to traditional wiring paradigms in terms of cost-effectiveness and safety for powering smart home devices, distributed energy management, and Internet of Things (IoT) integrations in American homes. The article includes an examination of PoE technology and standards evolution, the energy efficiency implications of centralized DC power delivery, the safety and regulatory advantages of PoE when compared to National Electrical Code (NEC) requirements, and the effects of PoE on various stakeholders including homeowners, electrical contractors, utilities, builders, and policymakers. Through the provision of an evidence-based framework for assessing the potential of PoE to transform the electrical ecosystem of the American home, the article addresses a significant void in the existing residential infrastructure literature.
Energy Efficiency and Carbon-Aware Workload Scheduling in Azure
As cloud adoption scales globally, data centers face growing pressure to reduce their carbon footprint while maintaining performance and reliability. Microsoft Azure has pioneered carbon-aware computing dynamically scheduling workloads based on regional energy availability and emission intensity. This paper explores Azure’s architecture, methodologies, and automation strategies for energy-efficient workload management. It highlights the role of artificial intelligence (AI) and automation in optimizing cloud operations for sustainability, including predictive scheduling, renewable energy alignment, and cost-performance balancing. Real-world use cases and experimental frameworks demonstrate measurable reductions in energy consumption and carbon emissions through intelligent workload orchestration.
Resilient Procurement and Supply Chain Architectures for Managing Disruptions in Volatile and Uncertain Global Markets
The rising rates and intensities of global disruptions, such as pandemics and geopolitical unrest, climate-related incidents and financial unpredictability have revealed some of the most critical vulnerabilities of traditional procurement and supply chain systems. The paper takes an elaborate architecture of developing resilient procurement and supply chain architectures that can survive and adapt to uncertainties in dynamic global markets. The research combines both theoretical knowledge and practical approaches to suggest adaptive, data-driven and decentralized supply chain models. Supply chains have developed to complex and interdependent networks across several countries, suppliers and stakeholders. Globalization has facilitated cost efficiencies and scalability, but has also increased the exposure to systemic risks. COVID-19 pandemic, semiconductor shortages, and geopolitical trade restrictions have disrupted the supply chain and made resilience a fundamental design requirement, not an appendix. In this study, resilience is viewed as the capacity of the supply chains to predict, absorb, adapt, and recover through disruptions and ensure continuation of operations. The article suggests a multi-layered resilience framework that includes strategic sourcing, digital procurement platforms, real-time risk monitoring, and collaborative ecosystems. State of the art technologies (artificial intelligence, blockchain, and Internet of Things (IoT)) are considered in the context of improving visibility, traceability, and decision-making agility. The structure focuses on redundancy, flexibility and supplier diversification as the structural building blocks. A mixed approach of qualitative analysis and quantitative modeling is used. The resilience strategies are assessed using risk assessment models, simulation methods, and performance metrics. The paper presents a resilience index that is determined by the main key performance indicators like recovery time, stability of service level, and cost impact. Findings show that companies with decentralised procurement policies and digitalisation are much more resilient. The results show the disruption response time improvement of up to 35 percent and the supply chain losses reduction of about 28 percent. Moreover, the paper notes the relevance of cross-functional coordination and governance mechanisms in guaranteeing successful implementation. The paper has a contribution to the academic literature and industry practice as it gives a structured approach to resilience design. It provides practical information to policymakers, supply chain managers, and researchers on how to make their way through the challenges of the contemporary global markets. Additional future research directions will involve incorporation of sustainability metrics and investigation of autonomous supply chain systems.
ERP-Enabled Predictive Maintenance in Manufacturing: Leveraging Machine Learning for Fault Detection and Production Continuity
Predictive Maintenance (PdM) Enabled ERP on fault-detection and product-community of machine learning (ML) has emerged as a significant facilitator towards the establishment of machinery dependability, reduction of downtime, and optimization of operation performance in the modern world of manufacturing. PdM can capture faults at an early stage and make proactive maintenance choices, out of the scope of traditional reactive and preventive mechanisms, using ML, Internet of Things (IoT) sensors, and advanced data analytics. This review of the predictive maintenance systems that are based on ERP, with a specific emphasis on the architectural integration of ERP, MES, and SCADA platforms, with the help of digital twin technology. The paper reviews data collection on the shop floor, water, and the IoTs; data synchronization and interoperability issues; and the use of supervised, unsupervised, semi-supervised, and deep learning methods for the detection and classification of faults. Moreover, the paper compares the effects of predictive maintenance on production continuity and operational resilience, and the contribution of ERP-enabling planning and rescheduling to reduced downtime and enhanced decision-making.
AI-Native and Agentic Data Governance: From Rule-Based Policies to Self-Healing Metadata Systems
Data governance has progressed through three distinct generations. The first was manual: stewards, policy documents, spreadsheets, quarterly reviews. The second was rule-based automation: policy engines, schema contracts, automated access control derived from classification metadata. The third—emerging now—is AI-native: large language models and agentic systems that infer governance attributes, detect violations, and propose remediation autonomously. This paper surveys the progression and presents a position on what AI-native governance can and cannot do, grounded in operational experience running system-enforced governance in a 30-engineer data organization that achieved a 45 percent error reduction through deterministic automation alone. The case study evidence includes 500,000 files consolidated to 3,000 governed assets, $1.4 million in annual storage savings, 93 percent registration compliance and 87 percent classification coverage—establishing the baseline against which AI-native enhancements should be evaluated. This paper examines three research questions: how each generation of governance has worked and where each has failed; what AI-native techniques can productively add to the rule-based foundation; and what guardrails are non-negotiable when applying LLM-based agents to governance decisions with regulatory consequences. The central argument is that AI-native governance is an evolutionary step rather than a replacement, that the residual cases where deterministic automation falls short are exactly where LLMs offer the most value, and that the disciplined use of AI in governance requires accepting that some categories of decisions are properly human and should remain so.
Agentic Data Engineering: LLM-Augmented Pipeline Generation, Self-Healing ETL, and Autonomous Repair
Production data engineering organizations spend a significant fraction of their on-call time on a narrow class of recurring incidents schema drift, transient cluster failures, late-arriving data, configuration regressions that are individually simple but collectively expensive. This paper presents a position and prototype design for agentic data engineering: the use of large language model (LLM) agents, organized around the ReAct (Reason and Act) framework, as a first responder to pipeline failures, augmenting rather than replacing the on-call engineer. The design is grounded in operational experience from a production banking data platform comprising 35 production pipelines, an observed incident frequency of 15-20 failures per month, and 3-4 schema changes per month exactly the conditions under which a well-scoped agent can absorb a meaningful share of triage work. We describe a tool-use architecture in which the agent has access to a fixed, audited set of read-mostly diagnostic tools (Airflow DAG status, Spark job logs, Delta Lake history, Trino query plans, schema registry diff) and a smaller set of write-capable repair tools that are gated behind explicit human approval before any change reaches production. We propose an evaluation methodology over 6 months of retrospective real incidents plus a synthetic incident benchmark, with a target diagnostic accuracy of 83% and a target mean-time-to-resolution (MTTR) reduction of 36x relative to the human-only baseline. We address the regulatory realities that govern any automated change in financial services SOX change management, GDPR data handling, immutable audit trails and argue that the chain-of-thought reasoning produced by ReAct agents is not a limitation but an audit feature, making the agent's decisions more inspectable than equivalent script-based automation. We conclude with a frank discussion of the limitations: hallucinated diagnoses, the cost of tool calls under high incident volume, the risk of automation bias on the human reviewer, and the need for narrowly scoped agents rather than generalist ones. The contribution of the paper is a practitioner-oriented design framework and a reproducible evaluation protocol, intended to bridge the gap between the LLM agent literature and the operational realities of regulated data platforms.
Master Data Management in Multi-Cloud Environments: A Survey with Operational Evidence from Banking and Insurance Deployments
Master Data Management (MDM) is exponentially harder in multi-cloud, multi-region, multi-domain environments than in the single-cloud monolithic settings for which classical MDM patterns were designed. We survey the evolution of MDM patterns in light of operational evidence drawn from a hybrid on-premises and multi-cloud deployment spanning 6 business units, 14 source systems, and roughly 4 million source identifiers that consolidate to approximately 2.5 million unique entities. We examine four architectural patterns (centralized hub, distributed registry, federated masters, and hybrid) and report that data residency constraints make centralized hubs operationally infeasible for our context, while federated masters with reconciliation contracts have proven viable. We report production entity resolution results: 92% precision and 45% recall for deterministic rules, 87% and 62% for probabilistic Fellegi-Sunter matching, and 89% precision with 76% recall for a learned Siamese model, with cross-region privacy constraints reducing matching accuracy by 3 to 5 percent. We describe a compliance audit that discovered Personally Identifiable Information (PII) in 14 unknown analytical tables, requiring 40TB of deletion and a rebuild of the pseudonymization pipeline. We discuss governance structures that succeed where centralized governance creates bottlenecks, and identify open research challenges in private entity resolution, cross-organizational governance, and validation of MDM correctness under GDPR and CCPA constraints.
Natural Language Interfaces for Self-Service Analytics on Data Lakes: Design Patterns, Governance, and Lessons from a Production Deployment
The promise of natural-language interfaces to enterprise data is older than the LLMs that finally make it tractable, and the gap between the demo and the production deployment is larger than most organizations appreciate. This paper presents a case study of building a natural-language analytics interface on top of a production banking data lake comprising 1,300+ student engagement and curriculum datasets served by dbt Semantic Layer, where the existing SQL surface had reached 80% dbt Semantic Layer adoption and was processing more than 100,000 queries per month at 8-25ms p99 query overhead and where the remaining 20% of non-technical analysts were structurally excluded from self-service because they did not write SQL. We describe the architecture that emerged: a schema-retrieval layer that selects only the relevant subset of the 1,300-table catalog for each natural-language question, an LLM that produces a candidate SQL query, a multi-stage validation layer that enforces governance constraints (row-level security, column masking, query cost ceilings) before any query is executed, and a post-hoc rewriting step that handles the LLM's failure modes qualified column references, ambiguous joins, hallucinated columns. We report pilot results from a four-week deployment to a population of business analysts: 75% adoption (defined as analysts running at least one NL query per week), 80% query accuracy (the produced SQL returns the correct answer to the asked question), and 1.2-second median latency end-to-end. We are honest about the limitations: hallucinated column names remain the dominant failure mode despite schema retrieval, LLM API costs are non-trivial at the query volumes the SQL surface handles, and the governance plumbing is more complex than the natural-language layer. The contribution is a practitioner-grounded design framework with explicit attention to row-level security and audit, intended for teams considering whether the cost of building this layer is justified by the analyst population it unblocks.
Retrieval-Augmented Generation for Question Answering and Beyond: A State-of-the-Art Review
Large language models (LLMs) are improved by RAG, a disruptive paradigm in natural language processing that combines generation with external knowledge retrieval. Unlike conventional models that rely solely on a parametric internal memory component, the RAG model can retrieve the required information, whether structured or unstructured and merge it into the answer-generation process, aiding factual grounding, enriched context and greater logical capacity. The primary RAG concepts have been systematically summarized in this paper, including system architecture, retrieval strategies, embedding techniques, reranking strategies and knowledge-aware generation frameworks. Its use in open-domain question answering applications has demonstrated that RAG can be used to aid evidence-based reasoning, multi-hop query answering, and interpretability. Outside QA, RAG has been useful in dialogue systems, domain-specific assistants, scientific summarization, enterprise knowledge systems, medical reasoning systems and code generators, demonstrating the applicability of RAG to practical environments. The recent developments have encompassed hybrid retrieval mechanisms, graph-based augmentation, multimodal integration and agent-like reasoning that further add on to the capabilities of RAG. This review outlines that, with summarization of theoretical backgrounds, practical applications and developments RAG is increasingly taking up prominence as a dependable framework for knowledge-based intelligent systems that are able to be scaled. The discussion contributes to understanding the evolution of RAG and demonstrates how retrieval-compatible generation can further enhance the effectiveness of current LLM-based applications.
AI-Based Anomaly Detection for Cloud Logs and Distributed Traces
Cloud-native infrastructures built on microservices, containers, and distributed tracing systems generate massive volumes of heterogeneous telemetry data, including logs, metrics, and traces. Traditional rule-based anomaly detection techniques are increasingly inadequate due to their rigidity, high false positive rates, and inability to adapt to dynamic workloads. This paper introduces a machine learning-based anomaly detection model of cloud logs and distributed traces that uses machine learning and deep learning frameworks to detect anomalous behavior in a system in real-time. The proposed system incorporates multi-modal telemetry over a scalable Kubernetes implementation and implements methods including Isolation Forest and Autoencoders to perform unsupervised anomaly detection. By fusing log and trace data, the framework captures both semantic and structural dependencies across distributed services. Experimental evaluation in a simulated cloud environment demonstrates strong performance, achieving precision of up to 0.92, recall of 0.95, and F1-score of 0.94 for container escape detection, while maintaining an AUC above 0.95 across scenarios. Resource abuse detection had a precision of 0.91 and recall of 0.93, and unauthorized API usage had an F1-score of 0.89. These outcomes demonstrate high accuracy, memory and strength over the baseline models, which is why the method can be used in contemporary cloud security applications. It also addresses the aspect of scaling and real-time deployment in the setting of a production environment.
Advanced ECG Signal Classification Using Deep Learning Networks for Early Disease Diagnosis in Healthcare
Healthcare is producing vast amounts of data, providing new opportunities for early disease detection and improved patient care. Electrocardiograms (ECGs) are widely used to identify cardiac abnormalities; however, manual interpretation is often time-consuming and prone to inaccuracy. This study proposes an advanced deep learning framework for automated ECG signal classification, enabling precise and rapid identification of cardiac disorders. The approach involves careful data preprocessing noise elimination, augmentation, normalization, and feature extraction followed by training a Recurrent Neural Network (RNN) to capture the time-dependent dynamics of sequential ECG data. Model performance is evaluated using accuracy, precision, recall, F1-score, and ROC metrics. The RNN model is compared against baseline models such as Convolutional Neural Networks (CNN), Random Forest (RF), and ResNet50. Results show that while CNN achieved 49% accuracy, RF reached 83.7%, and ResNet50 achieved 94.02%, the proposed RNN attained superior performance with 99.5% accuracy, 99.4% precision, 99.2% recall, and a 99.2% F1-score. These results highlight the RNN’s effectiveness in detecting subtle cardiac irregularities. The proposed framework significantly reduces human error, accelerates clinical decision-making, and enhances patient outcomes, demonstrating strong potential for real-world healthcare applications and early disease diagnosis.
Intelligent Anomaly Detection Framework for Industrial Robots Using Vibration Signals and ML Algorithms
Many industrial manufacturing firms have adopted industrial robots to increase production efficiency. There is a greater chance of an industrial robot joint failing or developing a problem as its service duration increases. It is still challenging to identify industrial robot joint faults using only the current signal, even though some vibration-based detection techniques have been successfully established. This is particularly true when there is not enough labelled data to distinguish between normal conditions and faults. This study suggests a brand-new method for anomaly identification utilizing vibration data from industrial robot joints, addressing the challenge of limited anomaly samples. A Long Short-Term Memory (LSTM) model is used for precise defect identification when the method incorporates sophisticated preprocessing techniques including noise filtering, normalization, and Fast Fourier Transform (FFT)-based feature extraction. The model's effectiveness is demonstrated by its 97.70% accuracy rate and a precision of 99.9% showing its potential towards the real time deployment in prsedictive maintenance systems of industrial robots, to ensure its continuous and efficient operation. These results show the feasibility of applying this model in predictive maintenance systems that boost the industrial manufacturing processes reliability without downtime and increase operational efficiency.
Data Analytics Approaches for Effective Threat Identification in Cloud Databases
The threats are also becoming more sophisticated and multifaceted in the world as the technology advances, including the data breaching, insecure interfaces, shared technology vulnerability, and distributed attacks. One of the most pressing problems in today's IT infrastructure is ensuring the safety and dependability of cloud computing by detecting threats in server databases. This study constructs and evaluates intrusion detection models using the CICIDS2017 dataset, which contains both benign and harmful traffic. Extensive data preprocessing steps were used to improve the dataset's quality and address the problem of class imbalance. Data purification, normalisation, feature selection, and SMOTE data balancing were all part of these processes. Some common measures used for training and testing LSTM models include accuracy (ACC), precision (PRE), recall (REC), and F1-score (F1). In the best trial result, the model achieved an ACC of 98.51%, PRE of 99.0%, recall of 98.0%, and F1 of 99.0%, proving that it is trustworthy in differentiating between benign and malicious traffic. Training and validation curves demonstrate successful learning and high generalization, and there is not a great deal of overfitting. The analysis of the confusion matrices also confirms the high rates of detection of all types of attacks where most of them were over 95 percent correct. The LSTM has an obvious advantage when compared to traditional models (MLP, SVM, XGBoost, DeepGFL): it is more effective, as it can identify intricate temporal patterns in network traffic that cannot be identified by other models. Finally, to improve the efficacy of intrusion detection systems in the modern cloud-based environment, the proposed LSTM model provides a strong and efficient method for detecting threats in cloud databases.
Technical Debt Management Strategies in Frontend Engineering: How Prioritization Frameworks Affect Delivery Performance and Developer Morale
Technical debt has become a significant concern in modern frontend engineering environments where rapid product development cycles often priori-tize short-term feature delivery over long-term maintainability. As frontend applications evolve to support complex user experiences and distributed architectures such as micro-frontends, teams frequently introduce architectural compromises, outdated dependencies, du-plicated components, and inefficient state management patterns. Over time, these issues accumulate and de-grade system quality, slowing development velocity and increasing cognitive load on developers. This paper investigates technical debt management strategies in frontend systems and evaluates how prioritization frameworks influence delivery performance and developer morale. The study analyzes widely adopted prioritization models including Weighted Shortest Job First (WSJF), Cost of Delay (CoD), and Impact–Effort matrices as mechanisms for integrating technical debt remediation into engineering roadmaps. Architectural diagrams illustrate a technical debt management pipeline that combines automated code analysis, technical debt registries, and backlog prioritization mechanisms. The findings demonstrate that organizations implementing structured prioritization frameworks expe-rience improved software delivery metrics, including increased deployment frequency and reduced defect rates. Additionally, proactive technical debt manage-ment improves developer morale by reducing frustration associated with legacy code and improving overall developer experience. The paper concludes by proposing a comprehensive technical debt governance architecture designed to support sustainable frontend development in large-scale engineering environments.
Zero Downtime Migration: Utilizing Golang-Driven IaC and Crossplane Compositions Between Network Shards to Mitigate Noisy Neighbor Interferences
As cloud-native environments scale, the “Noisy Neighbor” effect becomes a critical bottleneck for multi-tenant platforms. Traditional Infrastructure-as-Code (IaC) tools struggle with the dynamic requirements of migrating live work-loads between network shards without service interruption. This paper proposes my novel architectural framework utilizing Golang and Crossplane to facilitate zero-downtime shard migration. I detail the development of custom Crossplane Compositions and Go-based providers that treat infrastructure as a reconcilable Kubernetes object. By leveraging Kubernetes’ Control Plane logic, I demonstrate how automated network sharding and live migration can reduce inter-tenant interference by 92% while maintaining 99.99% availability.
AI-Driven Carbon-Aware Orchestration for Sustainable Cloud Deployments
As​‍​‌‍​‍‌ organizations digitally transform at a rapid pace, the need for carbon-aware computing has become very evident. This trend is leading to a complete rethink of how workloads are deployed and managed by cloud adopters. In spite of significant progress made to improve cloud efficiency, the issue of energy remains a major challenge because of fluctuating energy demands, limited visibility into real-time carbon intensity, and the awkwardness of aligning performance goals with an environmental impact. This paper shows how AI can help to close this gap by providing a dynamic carbon-aware orchestration that can move or schedule workloads based on real-time or predicted emissions data from different regions and cloud providers. Our approach is a combination of carbon intensity feeds, forecasting, and an AI orchestration-driven engine capable of making deployment decisions that balance sustainability, cost, and latency. The results reveal that AI-driven orchestration is not only capable of pinpointing the greenest compute windows and locations but can also change automatically with the prevailing situations, thus being more effective than static rule-based methods. Moreover, the study uncovers additional benefits such as the increased transparency of the environmental impact, improved decision-making for DevOps teams, and a recyclable architecture that can evolve with EMA and AI forecasting model developments. The importance of carbon-aware orchestration will become even more pronounced as the demand for cloud services keeps on increasing. Thus, AI will have an indispensable role in enabling sustainable cloud ​‍​‌‍​‍‌operations.
Real-Time Anomaly Detection and Attack Mitigation for Cloud-Based Content Delivery Paths Using AI
Cloud-based content delivery paths, which include client apps, edge PoPs, backbone links & origin services, are being attacked and misconfigured in ways that make them very less reliable, more expensive, and less trustworthy. Because CDN traffic changes in milliseconds and failures quickly spread across more regions, finding and fixing problems must happen in actual time, not on these dashboards that show what happened after the fact. This study suggests an actual time AI-driven pipeline that learns what "normal" delivery behavior is and steps in as soon as it sees a risk. Telemetry is gathered from multiple levels, such as edge access logs, request/response headers, TLS and QUIC handshake metadata, cache hit/miss streams, routing along with their BGP updates, queueing and loss statistics, and origin health indicators. These signals are then combined into time-aligned flow windows. A hybrid model stack has self-supervised sequence encoders for traffic patterns with a lot of different values, graph-based predictors for path & PoP connections, and lightweight online changepoint detectors to find sudden changes. When an anomaly is detected, a policy engine ranks the most likely causes, such as L7 floods, cache poisoning these attempts, bot spikes, route leaks, or regional origin brownouts. It then runs mitigation playbooks, which include adaptive rate limiting, dynamic WAF rules, cache key hardening, smart rerouting, or selective origin shielding. The system is tested by playing back multi-region traces at line rate and adding controlled red-team injections. This tests detection delay, false positive rate & end-to-end user effect under load. The results show that detection is substantially faster (with a median time-to-alert of less than a second), that context-aware fusion greatly reduces noisy alerts, and that mitigation works very well with little collateral throttling. Overall, the method gives CDN operators and cloud service providers a way to maintain their delivery paths stable even when things change quickly. It turns raw information into fast, automatic defense. The same cycle of learning and doing may also be used for multi-cloud ingress, SASE edges, and next-gen content routing, which will make internet-scale delivery safer and more independent.
Cross-Cloud Governance Engine Using Policy-as-Code for CMS Platforms
Modern content management system (CMS) platforms are relying more and more on multi-cloud environments to give users digital experiences that are more scalable, long-lasting & available all over the world. This diversification creates huge problems for governance because traditional rule-based methods often have trouble keeping up with the changing nature of these cloud resources, inconsistent security protocols & the growing complexity of compliance requirements. Manual evaluations, disconnected technologies, and policies that only work in the cloud slow down development cycles as well as create additional risks. This paper presents a Cross-Cloud Governance Engine utilizing a Policy-as-Code (PaC) framework that integrates policy creation, validation & enforcement across several other cloud ecosystems to address current limitations. The engine uses declarative, machine-readable policies to automate security, compliance along with their operational measures. It works perfectly with modern CMS pipelines. It has features like actual time policy assessment, automatic drift detection, and enforcement that works in any other context, which reduces mistakes made by people and makes deployments faster and more consistent. The suggested solution brings together governance logic & uses PaC technologies like OPA/Rego or similar frameworks. This makes sure that these CMS resources on AWS, Azure, GCP, or hybrid configurations follow the rules of the company without getting in the way of development teams. Tests and prototypes show that the engine reduces the burden of governance, makes policies more accurate, speeds up release cycles & makes the platform more reliable overall. This paper explains an organizational structure that is practical, adaptable, and concentrates on developers. It fits with contemporary DevSecOps methods as well as lets businesses effectively manage CMS applications in many other cloud settings while continuing to satisfy strict privacy and compliance criteria.
From Legacy ERP to Cloud-First: A Transformation Story with Dynamics 365
The​‍​‌‍​‍‌ analysis in this paper is centered around the decision of a company to stop using traditional Enterprise Resource Planning (ERP) systems and instead choose a cloud-based infrastructure. To give an instance of the change, the paper mainly uses Microsoft Dynamics 365. Normally ERP systems are the main perpetrators of the low creativity and slow decision-making resulting from the problem of their inflexible structure, high cost of maintenance, and limited growth potential, among others. The paper implemented a case study method with mixed methods to collect qualitative insights along with quantitative data of metrics of performance to understand the process of an enterprise's migration which was a result of the need for increased agility, seamless data integration, and operational efficiency. The usage of Microsoft Dynamics 365, a modular cloud architecture, and the integration of analytics and AI-driven features, have not only facilitated the organization of data, but also improved the communication between different departments and fastened the decision process based on data. According to the findings, the transition to cloud ERP enables a company to be more agile as it opens up the scalability, efficiency, and flexibility of the business. The article points out that the most important factors for the project's success are stakeholder engagement, systemic change management, and well-prepared governance structures to guarantee project success. As per the communication, the execution of a cloud-first strategy through the use of Dynamics 365 is not only an enhancement of the technology but also a strategic enabler for scalability, resilience, and ongoing innovation in the long run. With the help of the intelligent ecosystem of Dynamics 365, companies are in a position to keep their competitiveness in the rapidly changing digital market that at the same time allows them to be agile and they are able to take the ‍ ​‍‌lead.
Zero Trust in the Business Cloud: Security by Design in Dynamics 365
In the ever-changing world of commercial cloud settings, a Zero Trust policy is very essential for keeping their information, people & marketplace processes safe from increasingly cosmopolitan threats. Zero Trust is founded on the idea that there is no implicit trust within or outside the network. Every request for access must be verified based on their identity, context & compliance. Security by Design in Microsoft Dynamics 365, which combines both CRM & ERP features into a single cloud-based system, makes sure that the security is built into every step of building, deploying & using the system. This strategy deals with many important problems including managing their identities, controlling access in detail & following complicated rules that govern how businesses run today. The recommended solution focuses on building strong identity governance, flexible authentication mechanisms & constant monitoring utilizing Microsoft's built-in cloud security tools and AI-driven analytics. A case study shows how Zero Trust rules lower the risk of lateral movement, improve compliance & provide powerful access limits based on actual time threat intelligence. The findings show that data integrity has improved, resilience to insider threats has grown & audit readiness has become easier without hurting productivity. The combination of Zero Trust and Security by Design in Dynamics 365 not only strengthens the limits of trust in businesses, but it also sets up a foundation for the latest era of secure, flexible & scalable commercial cloud infrastructures that can handle more cyber threats.
Securing the Enterprise: How Dynamics 365 Meets Global Compliance Standards
Businesses the present day are under increasing demands to keep data private, follow the rules & keep their operations safe all over the globe. Microsoft Dynamics 365 has become a strong business platform that meets these needs effectively with important global adherence to rules including GDPR, HIPAA, ISO 27001 & FedRAMP. This study analyzes how Dynamics 365 enables the Integration of compliance into necessary business functions via data governance, automated threat detection & transparent simple adherence reports. The study utilizes a multi-method approach, including a literature analysis, an applied compliance framework, and a practical case study, to analyze the platform's technical and procedural safeguards. The findings demonstrate that Dynamics 365’s integrated data model, role-specific access controls, audit trails, and cloud security certifications substantially improve organizational resilience and ensure adherence to multi-jurisdictional requirements. The report also emphasizes the significance of Computerization & AI in reducing human mistake & ensuring that their regulatory compliance via vigilant monitoring & policy implementation. Ultimately, Dynamics 365 demonstrates that compliance can transition from a Vigilant control to a proactive & competitive advantage. This helps organizations not only protect their information, but also develop trust, transparency & process improvement in a regulatory climate that changes extremely rapidly.
Micro-Frontend Design Patterns for Multi-Framework Applications
Micro-frontends​‍​‌‍​‍‌ are one of the major architectural means that have become progressively significant for scaling up modern web applications as they make it possible for frontend modules to be developed, deployed, and maintained independently. As companies move towards domain-driven structures and distributed teams, the option of using multiple frameworks like React, Angular, Vue, and Svelte—within one application raises a great deal of difficulties in terms of interoperability, performance, shared state management, and maintainability. This research is aimed at discovering and evaluating different design patterns that support the successful integration of multi-framework in a micro-frontend environment, mainly concentrating on patterns such as Web Components, Module Federation, iframe isolation, custom integration layers, and meta-framework orchestrators. The research via comparative pattern analysis and a practical implementation case study was used as a combined methodology to evaluate the patterns in terms of their intricacy, extendibility, team autonomy, deployment flexibility, and enterprise environments suitability. Findings in this respect show that hybrid solutions are frequently superior to any single approach in which Web Components and Module Federation ensure robust interoperability and growth of scalable composition are prominent. The paper ends by enumerating the advantages of deliberate pattern selection and opening up the possibilities for the next advancements such as automated orchestration, standardized cross-framework contracts, and enhanced runtime performance ‍​‌‍​‍‌techniques.
Benchmark: REST vs GraphQL vs gRPC Performance
This study provides a thorough performance comparison that assesses three commonly utilized API transmission protocols REST, GraphQL, along with Grpc against critical metrics that include latency, payload speed of throughput, and scalability. The purpose is for providing system architects and developers a meaningful understanding of how each system protocol works with different types of workloads and in what cases. The evaluation technique needed to establish environments for testing that were the same every time and were like real-world situations, like mobile app backends, API-heavy platforms, along with microservices communication. We employed applications like Postman, JMeter, k6, along with Wireshark to see how fast they responded, how effectively they responded to requests, how much time it was taking to serialize data, and how the internet connection performed. REST was the most popular and simple to understand protocol. It was more trustworthy, but it was slower and bigger given that it didn't keep track of state and made use of a lot of JSON. GraphQL worked well to cut down on data transmission by permitting these customers pick specific fields to use. This was fantastic for complicated front-end interactions as well as mobile apps, but it also implied that servers had to do more work. gRPC, and this leverages HTTP/2 and Protocol Buffers, exhibited less lag and more throughput than REST and GraphQL, especially in these tiny services and high-concurrency scenarios. This is because it is capable of receiving and sending data in both ways and retaining data in binary form. The results show that REST is an effective option for ease of use and consistency, but GraphQL is better to feed dynamic data demands, and gRPC is preferred by high-performance distributed apps. This benchmark helps you determine the best protocol for current software systems depending on which the program needs, how fast it has to be, and how significantly it needs to be able to develop.
As applications grow to support millions of users at once, the demand for faster as well as more responsive RESTful APIs has become even more important in the present day's hyperconnected digital world. Even while HTTP/1.1 and HTTP/2 are important for online communication, they typically don't meet the low-latency needs of modern distributed systems because of problems like head-of-line blocking along with poor connection management. This study analyzes how HTTP/3, based on Google's QUIC protocol, alters API performance with the inclusion of multiplexed streams over UDP, built-in encryption, and better connection migration. Controlled testing & actual world case studies have shown that RESTful APIs using HTTP/3 consistently reduced latency by 30–40% in high-load, multi-region contexts when compared to HTTP/2. The changes made a huge difference, especially in mobile and edge computing, where network problems and packet loss are very common. Also, HTTP/3's zero-round-trip connection setup & streamlined handshake method make request-response cycles much better, which is great for latency-sensitive apps like banking, IoT & actual time analytics systems. The arrival of HTTP/3 is an enormous leap forward for the organizations and developers that want to make REST easier to use and more trustworthy. This article gives an in-depth examination at these novel approaches, backed up by real-world data, along with suggests the most effective way to add HTTP/3 to current REST architectures to make sure these API ecosystems continue to exist.
Edgeperfai: Intelligent Edge Computing For Ultra-Low-Latency Mobile Performance Optimization
The mobile application explosion, along with real-time services of any kind autonomous vehicles, smart healthcare, immersive AR/VR has a common denominator, which is the demand for ultra-low latency performance. Cloud-centric architectures of the traditional kind are not quite up to the task since they are often met with network congestion, backhaul delays, and limited context awareness. EdgePerfAI solves the problem by an edge computing framework that combines the proximity of edge resources to the user together with the power of AI to adapt to the changing scenario. By putting AI models right where the network meets the customer, EdgePerfAI constantly gets smarter with user behavior, device performance, and network conditions, and hence is able to dynamically affect workload distribution, bandwidth allocation, and data routing in real time, among other things. The framework is basically an attempt to minimize jointly end-to-end latency, improve Quality of Experience (QoE), and achieve energy-efficient resource utilization without compromising security or scalability. To fulfill such high aspirations, EdgePerfAI employs numerous techniques such as predictive analytics, reinforcement learning, and distributed orchestration to be on top of demand spikes, pre-cache data, and execute computation near the data source. Experimental evaluations show that the proposed framework can cut down latency by as much as 45% and increase throughput more than 30% compared to traditional edge frameworks. In this way, telecom providers, IoT solution architects, and application developers can harvest real-time intelligence at a scale that was not possible before. In the final analysis, EdgePerfAI is a landmark move towards the vision of mobile networks that are truly responsive and self-optimizing, i.e., where the convergence of AI and edge computing leads to a paradigm shift in latency, resilience, and user experience of next-generation digital ​‍​‌‍​‍‌infrastructures.
ConcurrentOpt-AI: Intelligent Multi-Thread Optimization Framework for Distributed Systems
Despite the fact that conventional methods based on single-threading or heuristics are not always efficient in adapting to fluctuating workloads and changing resource constraints, optimization remains a vital element in the achievement of energy saving and scaling in modern distributed systems. ConcurrentOpt-AI has invented a multi-thread AI-based optimization framework that is smart and can continuously improve the decisions made for resource allocation and scheduling. They aimed to achieve this by the implementation of distributed orchestration, adaptive learning models, and reinforcement-based feedback loops. Therefore, ConcurrentOpt-AI is in a position to go beyond the limitations that have been referred to as a result of this strategy. The system uses coordinated optimization agents running in parallel evaluation threads which exchange their findings through a lightweight orchestration layer to thereby reduce computational overhead, enhance task scheduling efficiency, and generally increase the system throughput. It has been shown that, in a large-scale microservices architecture, ConcurrentOpt-AI has significantly better throughput, latency, and resource consumption as compared to baseline heuristics and standard reinforcement learners. The use of case study evaluations was instrumental in accomplishing this demonstration. The results thus obtained demonstrate the potential of the framework to operate as a dependable, flexible, and scalable resource in the forthcoming cloud, edge, and high-performance computing ​‍​‌‍​‍‌systems.
AdaptCacheAI: Adaptive Hybrid Caching with Machine-Learned Eviction for Dynamic Cloud Workloads
Dynamic cloud workloads bring challenges such as high variability, multitenant interference, and heterogeneous access patterns, which make it necessary to have effective cache eviction to retain low latency and cost efficiency. Conventional eviction algorithms like LRU, LFU, ARC, and W-TinyLFU, which are based on static heuristics, give good results under stable conditions but their performance decreases significantly when request patterns change rapidly or differ across applications. AdaptCacheAI, to solve these problems, features an adaptive hybrid caching framework that combines machine learning–driven eviction with a multi-tier cache architecture going from memory to SSD layers. A predictive eviction model that, in real time, calculates the probability of reuse of an object is at the center, supported by a workload classification engine that is capable of recognizing patterns like temporal hotspots, bursty traffic, and tenant-specific behaviors. The continuous feedback loop that accounts for signals such as latency, eviction regret, and tier pressure refurbishes the decisions and thus allows AdaptCacheAI to modify policies on a dynamic basis. Tests on diversified traces confirm the substantial improvements that traditional algorithms can make through the employment of AdaptCacheAI, such as increased hit rates, decreased tail latencies, and lower SSD write amplification, all resulting in tangible performance gains and cost savings. AdaptCacheAI serves as a demonstration of the feasibility of employing machine learning for intelligent cache management and also it paves the way for the potential of future enhancements such as reinforcement learning–based optimization and cross-layer coordination in cloud ​‍​‌‍​‍‌systems.
Intelligent API gateways for AI-powered Healthcare Microservices
This research work discusses the usage of AI-enabled API gateways in healthcare microservices architecture to meet the requirements of smart, safe, and scalable data exchange. The traditional API gateways are not efficient in terms of interoperability, and they face problems related to high latency as well as the security and compliance requirements that are strict by nature, which come from healthcare systems. The new intelligent gateway model uses AI to make routing more efficient, detect anomalies, and optimize policies dynamically, thus facilitating communication that is seamless and aware of the context in the microservices that are distributed in the system. Through the embedding of machine learning-based decision mechanisms, the gateway evolves the network to be less prone to break down, response time gets to be shorter and operational efficiency is improved to the maximum level. The experimental evaluations have shown efficiency gains in throughput, fault tolerance, and real-time responsiveness, at the same time, strict implementation of regulatory standards such as HIPAA and GDPR is being maintained. Hence, this method is instrumental in enabling a solid digital healthcare ecosystem that has the capability of offering personalized, data-driven, and compliant healthcare services in a world that is becoming more and more ‍​‌‍​‍‌interconnected.