International Journal of Emerging Trends in Computer Science and Information Technology
High-Throughput Data Processing Architectures on AWS for Logistics and Transportation Systems
Modern logistics and transportation systems generate massive volumes of data from IoT sensors, GPS trackers, fleet management systems, and supply chain operations. This White paper presents comprehensive architectural patterns for implementing high-throughput data processing systems on Amazon Web Services (AWS) that handle both streaming and batch workloads. The proposed architectures leverage AWS services, including Amazon Kinesis Data Streams, AWS Lambda, and Amazon EMR to provide scalable, reliable, and cost-effective solutions. Key focus areas include real-time streaming processing for operational insights, batch processing for historical analysis, and fault tolerance mechanisms. Implementation patterns demonstrate processing capabilities exceeding 50,000 messages per second while maintaining sub-second latency for critical logistics operations.
The Software Industrial Revolution: Engineering Implications of AI Generated Software
Generative artificial intelligence systems capable of producing functional software from natural language instructions are rapidly transforming software engineering workflows. Modern AI development tools can generate application programming interfaces, infrastructure configurations, databases, and deployment pipelines within minutes. While much of the public discussion focuses on productivity improvements, the deeper engineering implications of these tools remain underexamined. This paper analyzes how AI assisted code generation changes the structure of software engineering practice. We argue that the role of developers shifts from direct implementation toward architectural reasoning, requirement definition, and system verification. The paper examines AI assisted development workflows, architectural implications, testing challenges, and long term maintainability risks associated with AI generated software. Finally, we propose a future development lifecycle where humans focus on problem definition while AI systems perform implementation, testing, deployment, and monitoring tasks.
Balancing Technical Debt Reduction and Feature Velocity in Legacy Front-End Systems
Enterprise front-end systems rarely become hard to change because of one catastrophic decision. In practice, they slow down release by release: a rushed dependency upgrade here, a copied checkout flow there, an accessibility exception that becomes permanent, or a monolithic bundle that nobody wants to touch before a peak business event. This paper examines how teams can reduce that accumulated drag without sacrificing delivery commitments. The study synthesizes established technical-debt literature with an anonymized industry case study drawn from two domains: grocery eCommerce and telecom retail. The proposed framework combines debt triage, incremental refactoring, component-driven architecture, observability, and release-governance practices that protect feature throughput while the codebase is being modernized. Representative results from the case study show double-digit gains in page performance, higher deployment frequency, and fewer escaped UI defects after teams introduced shared components, automated tests, and targeted refactoring windows. The paper argues that the most effective modernization programs are neither rewrite-everything efforts nor purely cosmetic cleanup initiatives; they are delivery-aware engineering programs that treat technical debt as an operating constraint with measurable business impact.
Small Language Models and Neuro-Symbolic AI in Zonal Architectures: The Rise of Small Language Models (SLMs) in Constrained Environments
The automotive industry faces a pivotal moment with the rise of Software-Defined Vehicles (SDVs), Zonal Electronic/Electrical (E/E) Architectures, and Generative AI. As vehicle architectures shift from centralized domain structures to decentralized zonal networks to handle more complex wiring and data flow, the need for local intelligence grows. However, deploying Large Language Models (LLMs) on resource-limited vehicle edge devices is challenging due to high computational latency, energy demands, and safety concerns. This paper explores the potential of Small Language Models (SLMs)—under 7 billion parameters, such as Microsoft’s Phi-3 and TinyLlama—as a way to embed advanced reasoning directly into Zonal Controllers. It also tackles the core issue of balancing the unpredictability of generative AI with the strict determinism required by safety standards such as ISO 26262. We suggest and evaluate Neuro-Symbolic AI as an essential layer of the architecture, using symbolic logic to verify neural network outputs in real time. By examining hardware options (NXP S32, TI TDA4), virtualization tools (Adaptive AUTOSAR), and safety protocols (RoboGuard, SYNAPSE), this study shows that SLMs, guided by symbolic logic, can support resilient, decentralized vehicle autonomy, reduce dependence on cloud connectivity, and uphold safety integrity.
Data Stewardship: How AI Agents Form the Pillars for Effective Data and AI Governance
The exponential growth of enterprise data and the rapid adoption of artificial intelligence (AI) have fundamentally altered the landscape of data governance. Traditional, manual approaches to data stewardship are increasingly inadequate for managing the complexity, velocity, and scale of modern data ecosystems. In response, agentic AIautonomous systems capable of interpreting intent, executing workflows, and adapting to dynamic conditions has emerged as a transformative solution. This manuscript provides a comprehensive analysis of how AI agents are redefining data stewardship, establishing the foundational pillars for effective data and AI governance. It examines the deployment of specialized AI agents across critical governance domains, including data quality management, metadata curation, master data management, and data retention. Furthermore, this paper addresses the imperative of regulatory compliance and responsible AI frameworks, demonstrating how multi-agent architectures enable real-time policy enforcement and risk mitigation. Through a synthesis of recent technological advancements and enterprise implementation models, this study underscores that agentic data governance is not merely an operational enhancement, but a critical evolution necessary for organizations to maintain data integrity, security, and trust in the AI era.
Ontology and Future of Master Data Management in Respect to Data Quality
Companies are producing large volumes of data due to digital technologies, cloud computing, and AI usage. Big data management is becoming a problem for small and medium-sized businesses too. Master Data Management ensures platform-wide corporate data consistency. Regardless of their size, organisations must manage increasingly sophisticated electronic business data and operations. Complex entity connections are difficult to represent in traditional data. Enterprise information systems require technology-based capabilities to simplify and comprehend company data and procedures. System-wide data integration and understanding are promoted by semantic ontology frameworks. Three information system use cases that might be enhanced are given, along with semantic technology and ontology-based techniques. Ontology-driven Master Data Management improves data quality, semantic integration, and intelligent data governance in contemporary businesses. Trustworthiness and data quality are guided by provenance.
Optimizing Enterprise SPAs: Angular Standalone Components and Signals
The SPAs have dominated the enterprise web applications architectural paradigm in the modern era given the capacity to offer highly user interactive experiences, enhanced responsiveness and less server communication overhead. Angular, React and Vue are some of the frameworks that have contributed considerably to the development of SPA through their structured component-based architectures and mighty state management systems. Angular, of such frameworks, has become an enterprise grade solution with a solid foundation in architecture, a well-developed modular ecosystem as well as strong tooling support. Nevertheless, the Angular classical applications may be highly dependent on the system of NgModule and reactive programming languages like RXJS, which, despite having potent capabilities, could become complicated when used in big-sized applications. Some of the newer features introduced by Angular in recent years include Standalone Components and Signals, which attempt to streamline application architecture, make apps faster and reduce overhead. The Standalone components do not rely on the NgModule, thus components, directives and pipes can be used independently. This minimizes the boilerplate code, enhances modularity, and allows smoother organization of the code. Instead, signals add a reactive state management technique to make changes easy to detect and performance manageable due to reactiveness. The individual problems which face enterprise-level SPAs include growing complexity of applications, slower performance through inefficient change detection cycle, large bundle sizes, and maintainability problems associated with tightly coupled module structure. These problems require the emergence of new architectural solutions that can be used to enhance scalability, maintainability, without affecting the performance. Signal-based reactivity and the adoption of Angular standalone components would be a promising solution to these problems. The paper explores how Angular standalone components and signals affect the optimization of enterprise-level SPA architecture. The study addresses the performance of apps incorporating these new features of Angular, dependency overhead decreasing, and maintainability of large corporations. In the paper, the architectural improvements are analyzed, the performance metrics are evaluated, and a systematic approach to implement a standalone architecture and signal-driven architecture is presented. One of the approaches suggested within the framework of this study is the creation of a modular enterprise SPA hosted on the basis of standalone components and reactive state management in the form of signals. The analysis assesses the aspects of performance that are relevant to the key performance indicators such as application load time, the efficiency of change detection, memory consumption, and developer productivity. The traditional Angular module-based and the proposed standalone architecture are compared to each other in terms of analysis. Findings indicate that applications that use standalone components enjoy less complexity and better modularity of applications. Signal use helps to minimize the unneeded cycles of detecting and changing the state, and the runtime performance can be observed to improve. The simplified architecture also enhances productivity by developers through less configurations required and better readability of the code. The results of the given research can be useful when the enterprise development teams aim to modernize Angular applications and optimize SPA structures. The work is related to the general concept of contemporary reactive programming paradigm in the frontend development and presents the advantages of embracing the new Angular structures in a practical manner. Finally, the concept of Angular standalone components and signals is also a major change in the structure of SPAs. These technologies give organizations scalable, maintainable and high-performance enterprise web applications, by making them simpler, faster, and more responsive.
Operationalizing Software Invariants: A DevOps-Driven Approach to Reliability in Cloud-Native Systems
The concept of cloud-native architectures has revolutionized the way the modern software systems are designed, deployed and operated. These systems employ containerization and microservices, dynamic orchestration, and distributed infrastructure to provide scalability, the resilience of these systems and high-speed development cycles. Nevertheless, there is an underlying complexity of the distributed cloud-native environments leading to major reliability issues. The use of traditional methods of reliability assurance, such as post-production monitoring and the functions of the first stage tests, are usually not enough in the case of cloud infrastructures, which change dynamically and keep changing continuously. Therefore, there is an increasing level of adoption of DevOps practices in which development and operations processes are combined by organizations to maintain reliability in the software lifecycle. One among promising strategies of reliability engineering with cloud-native systems entails operationalization of software invariants. Software invariants are the conditions or properties that should be always true when the systems are running. These invariants can be constraints on the system, including data consistency constraints, resource constraints, security policies or service availability guarantees. With implicitly specified and constantly checked such invariants, it is possible to identify abnormal states at an early stage, guard against cascade failures and ensure stability in the operation of a system. It provides a DevOps-inspired model of software invariants operationalization in the cloud-native systems. The solution features the combination of the definition of invariants, automated monitoring, policy enforcing, and end-to-end feedback in the DevOps pipelines. Through the framework the development teams are able to change conceptual reliability constraints in to executable policies that can run in the development, testing, deployment, and runtime environments. Invariants (both explicit and implicit) at the inception of a system are enforced as operation level contracts including automated checks via CI/CD pipelines instead of boxed design suppositions. The study seeks to determine the relationship in which the use of invariant-driven reliability engineering can enhance fault detection, system resilience and system operational observability. The approach would include model-based invariant specification, combining invariant monitoring with distributed telemetry, and automated remediation on top of the failures of any of these invariants. Experimental cloud-native workloads are tested by deploying them on container orchestration platforms and assessing them in the framework. The outcomes indicate the quantifiable changes in the system reliability metrics such as the decreased number of incidents, the improved rate of recognizing anomalies, and the better system recovery performance. The paper also identifies the importance of collaboration between the developers and the operations engineers in defining and enforcing reliability invariants through DevOps practices. This work adds to the existing body of work on reliability engineering in distributed systems through a systematic approach to the integration of software invariants into operation in workflows. The results indicate that the use of invariant-supported DevOps can play a major role in enhancing reliability assurance systems in existing cloud-based systems besides facilitating continuous software delivery.
Real User Monitoring (Angular) Angular Error Handling
Mitigating Algorithmic Complexity Attacks in Federated GraphQL Architectures: A Depth-Bounded Semantic Rate Limiting Approach for Open Banking
This swift financial services digitization has given rise to the open banking ecosystems which are powered by the contemporary API architecture. GraphQL has become very popular among them since it is flexible, data retrieval can be made efficient, and integrating multiple microservices into one unity can be achieved. The additional advantages of federated GraphQL are that such architectures support the use of distributed compositional services, such that independent development teams create scalable services that interoperate well with a single API gateway. Nevertheless, the security issues that are posed by this architectural paradigm are new, especially by the attacks associated with algorithmic complexity. The attacks are possible because the computational cost of deeply nested or semantically expensive GraphQL queries can be used to exhaust server resources in denial-of-service conditions. The difference between algorithmic complexity attacks and traditional volumetric attacks is that the attacks are not based on the volume of request. Rather, attackers construct a small set of queries that invoke computations which are computationally expensive like recursive field resolutions, cross-services joins, and graph deep traversal. Such attacks are more harmful in federated GraphQL environments deployed in open banking settings since the query can spread to several back-end services, increasing the computing costs. As a result, the few malicious queries can slow down the performance of the systems and undermine the availability of services. The current mitigation measures usually are based on either fixed query depths, query cost metrics, or conventional rate control methods. These strategies offer certain defence, but they are limited in a number of ways. The use of static depth limiting is known to block legitimate complex queries may be needed in financial analytics. Systems of query cost estimation are challenging to tune on a microservice distributed system. Traditional rate limiting schemes are more concerned with the frequency of requests as opposed to the computation complexity. Due to this, attackers are able to compromise such defenses by making low-frequency, but Poisson queries. In order to overcome these challenges, the current paper suggests an innovative conceptual security framework, namely, Depth-Bounded Semantic Rate Limiting (DBSRL). The suggested approach integrates the analysis of the structure of queries with the semantic interpretation of query implementation cost to dynamically manage API access. Differing with the traditional methods where the syntactic depth is only used, DBSRL considers query depth and semantic complexity based on resolver execution patterns and service dependencies. Integrating these metrics in a dynamic rate limiting mechanism can enable the system to identify and partially mitigate to attack by algorithmic complexity as well as offer acceptable performance to end-users. The suggested framework works in three processes. To enable the data transfer, the GraphQL gateway interprets the queries received with the help of a structural parser which does compute the depth and breadth of the query tree. Second, a semantic analyzer approximates computational cost using resolver dependencies, past execution latency and invocation patterns of cross services within the federated architecture. Third, a dynamically rate limiting engine applies thresholds which change with the load of the system and user behaviour restrictions and stops excessive consumption of computational resources. The effectiveness of the proposed approach was tested through experiments done in simulated open banking microservices in a federated GraphQL environment. The metrics used in the evaluation are the query processing latency, the system throughput, CPU utilization and the rate of attack mitigation. Experiments prove that the given DBSRL mechanism mitigates the effects of the algorithm complexity attacks dramatically without affecting the performance under normal workload. Moreover, the suggested technique is more accurate in detection than traditional query depth limitation techniques. The semantic cost estimation can be introduced into the framework to differentiate between legitimate complex queries and malicious queries that aim to obtain unreasonable levels of computational workload. This feature would be quite essential in open banking platforms where authentic applications usually demand queries in multi-services when trying to aggregate accounts, transaction analytics, and financial reporting. This paper has threefold contributions. The first one provides an in-depth assessment of vulnerabilities in an algorithmic complexity of federated GraphQL systems by open banking systems. Second, it also presents the Depth-Bounded Semantic Rate Limiting framework which combines structural and semantic query analysis enabling better attack mitigation. Third, it offers empirical testing that proves the efficiency of the method suggested in terms of improving API security without impacting on the system performance. The findings show that semantic complexity assessment combined with adaptive rate limiting is a viable and scalable approach to securing the current financial services delivered via GraphQL. This study adds to the constantly expanding topic of API security and sentences a strong defense model against open banking infrastructures with computational denial-of-service attacks.
Infrastructure as Code (Terraform/Bicep)
Distributed Systems Reliability Engineering
Secrets Management & Compliance Automation
Automated Deployment & Release Management
High Availability & Disaster Recovery Engineering
A Survey of Statistical Process Control and Its Applications in Manufacturing Quality Assurance
The high-quality products are one of the challenges that industries must guarantee, which rapidly develop in terms of global manufacturing. The quality of the finished product and its degree of functioning, and the customer satisfaction may all be altered substantially by the changes in the manufacturing cycle. Statistical Process control (SPC) is a method which incorporates the application of statistical methods in inspecting, regulating and improving industrial processes. The paper provides a thorough summary of SPC and how it has been used in the manufacturing quality assurance, which is essential in ensuring that variability in processes is eliminated, to enhance the consistency of the products and this is to reduce waste. The control charts, Pareto analysis, cause-and-effect, and process capability assessment are the most commonly used SPC tools that are described and accompanied with the description of how this tool is used in practice. The use of SPC in the practical implementation of manufacturing industry is also discussed in the paper which illustrates its practical use in making sure that processes are doing a good job and provide quality compliance. Moreover, the study illustrating the applicability of SPC in a manufacturing environment and its ability to lead to measurable increase in process capabilities and reduction of defects are also present. The paper identifies the importance of SPC as one of the keystones in the quality management systems in the contemporary production.
Networking Paradigms for Large-Scale IoT Deployments: A Survey on Reliability, Latency, and Scalability
The Internet of things (IoT) devices are usually taken as external dependencies and applications that are mainly used in providing information or carrying out simple processing and implementation of commands. With the recent advent of such devices that have in-built execution environments, practitioners can now develop and execute their own application logic on IoT devices. It is based on the essential requirements and networking paradigms of large-scale IoT deployments with scalability, low latency, and reliability as key performance metrics. As software-defined networking (SDN), network function virtualization (NFV), fog/edge computing, 5G/6G integration, and fog/edge-to-fog communication are highlighted, the conventional client-server and centralized cloud models are examined. By performing a comparative analysis, it is shown that there exist trade-offs between centralized processing and localized decision-making in the attainment of fault tolerance, real-time responsiveness, and massive connectivity. The literature review summarizes the latest developments in secure communication protocols, zero-trust models, gateway-placement clustering algorithms, joint-computing gateway updates, and AI-based security and botnet detection. The results show that contemporary IoT systems improve scalability, performance, and resiliency, but cost optimization, energy efficiency, and practical validation continue to be problematic. The present study is significant because it surveys all the current paradigms in Internet of Things networking and points the way for future studies to build trustworthy, large-scale IoT ecosystems that are safe and dependable.
Heat Dissipation Strategies for High-Performance Computing: A Review of Air and Liquid Cooling Efficiency
Heat dissipation and energy efficiency management have become more important concerns due to the growing computational demands of HPC systems. The drawbacks of conventional air-cooling systems that rely on heat sinks, fans, and ducts include poor heat conductivity, uneven airflow, and rising power demands as heat densities increase. As a result of this, liquid cooling technologies, including direct to chip, immersion, and microchannel cold plate, have become the better choice with better heat transfer coefficient, temperature uniformity, and Power Usage Effectiveness (PUE). Literature shows that liquid cooling may reduce the temperature of the components in a system by a significant margin; reduce the thermal resistance in the system and increase the long-term reliability of the system and provide the benefit of saving energy and making the system sustainable by reusing heat. With comparative analysis, it is noted that liquid cooling is up to 30-50% more efficient and stable to extreme workloads compared to the air-based approaches. Measures such as PUE, CUE and WUE further measure the enhanced energy, carbon and water efficiency. The given review summarizes the progress that has been made in both air and liquid cooling methods, focusing on the shift towards high-density cooling systems that are environmentally friendly. It also highlights the significance of optimized thermal designs in guaranteeing performance as well as reliability, and cost effectiveness of next generation infrastructures of HPC and data centres.
Serverless Architectures for Scalable Data Analytics Workflows in Cloud BI Systems
Cloud Business Intelligence (BI) systems increasingly require processing ever-growing datasets with agility and minimal operational overhead. Serverless computing – epitomized by services like AWS Lambda – has emerged as a promising architecture for scalable data analytics workflows. This paper explores how serverless architectures can be leveraged to build scalable, cost-efficient, and agile data analytics pipelines on the AWS cloud, integrating theoretical underpinnings with industry case studies and performance comparisons. We discuss the paradigm shift from traditional cluster-based ETL and data warehousing to Function-as-a-Service (FaaS) and fully-managed services, highlighting benefits such as automatic scaling, fine-grained billing, and reduced DevOps burden. At the same time, we address challenges including cold-start latency, statelessness, data shuffling overhead, and orchestration complexity, along with recent research advances aimed at mitigating these issues. We present a reference serverless BI architecture on AWS (utilizing services like AWS Lambda, Glue, S3, Athena, and QuickSight) and an event-driven analytics pipeline design, complete with system diagrams. Through deep analysis of academic studies and practical benchmarks, we show that serverless analytics workflows can achieve high scalability and throughput (e.g., processing hundreds of millions of records in minutes) with significantly lower operational complexity and cost – often 4–10× cheaper than equivalent always-on clusters. We also review performance evaluations demonstrating that, with careful design, serverless pipelines can handle large payloads (100+ MB events) with consistent execution times. In conclusion, serverless architectures represent a compelling next step for cloud BI, offering a path to self-service, on-demand analytics at scale, while ongoing innovations (e.g., optimized data orchestration and hybrid runtime models) continue to expand their applicability for larger, stateful, and latency-sensitive workloads.
The Role of Event-Driven Architectures in Enterprise Digital Transformation Initiatives
This white paper explores the critical role of Event-Driven Architecture (EDA) as a foundational operating model for modern digital transformation. As enterprises move away from the rigid constraints of legacy monolithic systems, the adoption of asynchronous, event-based communication has become essential for achieving the scalability, agility, and real-time responsiveness required in the digital era. This research identifies three primary challenges hindering successful transformation: legacy architectural coupling, the phenomenon of event sprawl, and significant observability gaps in distributed environments. To address these, the paper advocates for a strategic shift toward treating events as first-class business artifacts, implementing comprehensive event governance, and leveraging Domain-Driven Design (DDD) to define stable business boundaries. Through a synthesis of academic research and industry performance metrics, the analysis demonstrates how EDA facilitates an "Enterprise as Code" approach, significantly reducing system latency and improving resource utilization. The study concludes that the transition to EDA is a socio-technical journey, requiring not only architectural shifts but also evolution in organizational culture and maturity assessment.
Designing Zero-Downtime Migration Frameworks for Mission-Critical Legacy Systems to Microsoft Azure
The digital transformation of enterprise-scale organizations often hinges on the successful migration of legacy infrastructure to high-availability cloud platforms such as Microsoft Azure. For mission-critical systems, defined by their inability to tolerate service interruptions without severe financial or operational consequences, traditional migration methodologies such as the "Big Bang" approach are increasingly insufficient. This white paper delineates a comprehensive architectural framework for zero-downtime migration (ZDM), specifically optimized for the Azure ecosystem. By synthesizing evidence from distributed systems theory, change data capture (CDC) mechanisms, and hybrid-layering strategies, the report explores the orchestration of continuous availability. The proposed framework integrates real-time synchronization, blue-green deployment patterns, and the "Strangler Fig" application modernization model to ensure transactional integrity during transitions. Furthermore, the study addresses the convergence of technical implementation with stringent regulatory mandates, including FDIC and GDPR compliance. Through an analysis of log-based replication and global traffic management via Azure Front Door, the paper provides a roadmap for maintaining operational continuity. The findings emphasize that a successful ZDM strategy requires the decoupling of application logic from the underlying data store, allowing for iterative modernization while upholding the 24/7 demands of contemporary digital services.
AFP: An SLA-Aware Adaptive Freshness Protocol for Log Collection in Large-Scale Geographically Distributed Systems
Anyone who has operated a large-scale distributed system across multiple datacenters knows the frustration: log data piles up at staggering rates, and getting it from point of origin to a place where someone can query it is a constant exercise in tradeoffs. Today’s log pipelines handle this with a one-size-fits-all freshness model. If even a single consumer needs sub-second access to a log stream, the whole pipeline for that stream runs at full tilt synchronous replication, eager ordering, and immediate indexing regardless of whether the other 95% of downstream consumers would have been perfectly happy waiting thirty seconds. The waste adds up fast. This paper introduces AFP (Adaptive Freshness Protocol), a protocol that rethinks this assumption. AFP lets each consumer declare its own freshness SLA, and then dynamically tunes the pipeline replication mode, ordering strategy, indexing priority on a per-stream basis to meet the tightest active SLA at the lowest possible cost. When the most demanding consumer disconnects, the pipeline relaxes on its own. We formalize the problem as a constrained optimization over composable stage-latency functions, show it can be solved greedily in O(S log S) time per scheduling epoch, and introduce a degradation policy for WAN partitions that prioritizes critical streams while guaranteeing zero data loss. Under a workload mix we believe is representative of production environments (5% critical, 20% interactive, 75% batch consumers), our analysis indicates AFP cuts cross-region bandwidth by 58%, indexing CPU by 49%, and ordering overhead by 66% compared to uniform provisioning, all while keeping 99.7% of SLA contracts satisfied.
Evaluating the Impact of Event Hub Based Streaming Architectures on Enterprise Decision Latency
In the contemporary hyper-competitive business landscape, the ability to transform raw data streams into actionable insights with minimal delay is a primary driver of strategic advantage. This research paper evaluates the systemic impact of Azure Event Hub–based streaming architectures on enterprise decision latency the temporal gap between a business event and the subsequent corrective or opportunistic action. By examining the integration of high-throughput ingestion via Azure Event Hubs, real-time transformation through Azure Stream Analytics, and continuous state synchronization via Change Data Capture (CDC), this study articulates a framework for reducing data and analysis latencies. Furthermore, the inclusion of Artificial Intelligence for IT Operations (AIOps) is analyzed as a mechanism for autonomous system maintenance and incident remediation. Drawing upon empirical benchmarks and industry success stories, including Netflix’s anomaly detection frameworks and Penguin Random House’s inventory optimization, the paper demonstrates that transitioning from traditional batch-oriented Lambda architectures to unified, streaming-first Kappa architectures can reduce decision latency by 17–25%. The findings suggest that such architectures not only improve operational key performance indicators (KPIs) by 9–14% but also fundamentally transform the role of the corporate strategist from an intuitive visionary to an empirical architect.
Distributed Stream Processing for Real-Time Healthcare-Motivated Analytics in Multi-Cloud: A Semantics-Aligned Benchmark of Kafka-Centric Pipelines with Flink and Spark Structured Streaming
Healthcare providers increasingly rely on Kafka-centric streaming architectures for sub-second alerting and continuous analytics over heterogeneous clinical and device events, yet practitioners lack a semantics-aligned benchmark to compare Apache Flink and Spark Structured Streaming in multi-cloud, lakehouse-oriented deployments. We present a benchmark of Kafka-based pipelines using Flink 1.18 and Spark Structured Streaming 3.5 on AWS and Azure, with outputs persisted via a neutral Databricks Delta ingestion job that decouples engine performance from lakehouse commit behavior. Our healthcare-motivated workload suite models stateless FHIR-like validation, stateful event-time window analytics with large patient-level state, and enrichment with deduplication under controlled late arrivals, all under a formal semantic-alignment protocol. Across colocated deployments at 50 k events/s, Flink achieves 3.1x lower p99 alert latency (74 +/- 3.1 ms vs. 231 +/- 8.4 ms) under exactly-once guarantees. RocksDB incremental checkpoints reduce checkpoint duration by 2.6x and recovery time by 2.9x versus Spark’s HDFSBackedStateStore; switching Spark to RocksDBStateStoreProvider narrows the checkpoint gap to 1.4x while leaving Spark’s p99 latency unchanged, confirming the micro-batch trigger as the latency bottleneck. Directional cross-cloud experiments reveal 78–112 ms additional p99 latency, with AWS-to-Azure consistently 18–24 ms slower than Azure-to-AWS. Persistence latency to Delta (SLA-B) is dominated by the ingestion job’s commit interval and is engine-invariant, reframing engine selection for lakehouse architectures.
Operational Telemetry and Observability in Ingestion Pipelines
The rise in reliance of contemporary businesses on data-driven decision-making has placed data ingestion pipelines in mission-critical infrastructure, however, many of them are operationally opaque, providing little insight into their internal behavior and health. The paper is a synthesis of an operational telemetry and observability framework of data ingestion pipelines, which has been developed by systematically reviewing the literature of 18 published in the years 2018-2025. The paper suggests a four-dimensional observability framework including infrastructure observability, the pipeline process observability, data content observability, and end-to-end lineage observability, which adds the three pillars of metrics, logs, and traces to the specific needs of the data-intensive systems. The secondary data collection methodology was used, which included the descriptive statistical aggregation, thematic coding, and cross-study benchmarking based on four levels of observability maturity. The results prove that the observability based on telemetry delivers significant and consistent gains in all industry domains and technology stacks, and highlight the importance of ensuring the successful implementation is based on both the technological implementation and culturally altering the engineering team.
Ultra-Low Latency AI Systems: Leveraging Edge AI and Semiconductor Acceleration for Local Language Model Inference
Artificial intelligence applications increasingly rely on language models capable of understanding and generating natural language in real time. However, most large language models are typically deployed through cloud-based infrastructures, where network communication, bandwidth limitations, and data transfer delays introduce latency that constrains time-sensitive applications. These limitations have motivated growing interest in performing AI inference directly on edge devices, where computation occurs closer to the data source. At the same time, recent advances in semiconductor design, including neural processing units, application-specific integrated circuits, and specialized AI accelerators, have significantly improved the feasibility of executing complex models on resource-constrained hardware. This study examines how the integration of Edge AI architectures with semiconductor acceleration can enable ultra-low latency inference for locally deployed language models. The paper proposes a hardware-aware system architecture that combines optimized language models with dedicated AI accelerators to support efficient on-device inference. Model optimization strategies, including quantization and parameter reduction, are incorporated to accommodate the computational constraints of edge platforms without significantly degrading performance. A comparative evaluation framework is developed to analyze latency, throughput, and energy efficiency across different deployment environments. Experimental analysis demonstrates that edge-based inference supported by semiconductor accelerators can substantially reduce response latency while maintaining stable throughput and improved energy efficiency compared with conventional cloud-based approaches. These findings highlight the practical viability of deploying compact language models directly on edge devices for real-time intelligent systems. The proposed framework guides the design of future AI systems that require rapid response times, enhanced privacy, and reduced dependence on centralized infrastructure. Applications such as smart surveillance, autonomous robotics, mobile assistants, and industrial monitoring systems can particularly benefit from these advancements.
Federated Learning (FL) has emerged as a transformative paradigm for distributed machine learning, enabling model training across decentralized edge devices while preserving data privacy. This methodology is critical for sectors handling sensitive information, such as finance, healthcare, and the Internet of Things (IoT). Despite its benefits, the coordination and communication overhead between distributed nodes remain significant challenges. This paper evaluates the efficacy of REST and GraphQL API architectures in facilitating FL workflows. While REST APIs are favored for their statelessness and simplicity, GraphQL offers enhanced flexibility and efficiency by enabling precise data fetching—a vital feature for bandwidth-constrained decentralized systems. We provide a comparative analysis of these paradigms across performance, security, and scalability metrics, specifically regarding data synchronization and model aggregation. Finally, we propose design best practices for developing APIs that support robust, compliant, and efficient federated prediction systems.
Correspondent Banking and Nested Relationships: Managing Cross-Border AML/CTF Risk, Due Diligence, and Monitoring Expectations
Correspondent banking remains a critical backbone of cross-border payments, trade finance, and financial inclusion, yet it is also a persistent exposure point for money laundering and terrorist financing (AML/CTF) risk. These risks intensify in nested correspondent relationships where respondent banks provide downstream access to their own clients or other financial institutions because transparency diminishes across the payment chain and accountability for controls can become fragmented. This study examines how financial institutions can manage AML/CTF risk in correspondent and nested relationships while meeting evolving expectations for risk-based due diligence, transaction monitoring, and ongoing oversight across multiple jurisdictions. The research synthesizes regulatory guidance, supervisory enforcement themes, and industry practices to develop a structured framework for (i) risk scoping and customer/relationship classification, (ii) enhanced due diligence (EDD) for nested access, including governance, ownership, sanctions exposure, and AML program effectiveness, and (iii) monitoring and escalation standards that integrate payment-message data quality, typology-driven alerts, and periodic relationship reviews. Methodologically, the study employs comparative policy analysis across key regulatory regimes and a scenario-based assessment using representative nested-relationship typologies (e.g., indirect access to high-risk geographies, fintech/payment intermediary layering, and payable-through account–like structures) to identify practical control gaps and feasible mitigations. The expected contribution is a set of actionable, risk-proportionate recommendations and monitoring expectations that balance financial crime compliance with the operational realities of cross-border banking, supporting safer access to the global financial system without unnecessary de-risking.
Resource Scheduling Using AI in Cloud Environments
Efficient resource scheduling is a foundational challenge in cloud computing environments, where dynamic workloads, heterogeneous resources, and stringent service-level agreements (SLAs) converge to complicate optimal task allocation. Traditional scheduling algorithms such as Round-Robin, Min-Min, and other heuristic/metaheuristic approaches often struggle to adapt in real time to fluctuations in demand, resource availability, and cost-energy trade-offs [1],[2]. In contrast, artificial intelligence (AI) techniques including supervised learning, reinforcement learning (RL), and hybrid optimisation models offer adaptive and predictive capabilities that significantly enhance scheduling performance by learning from past workload patterns and system feedback [3],[4]. This paper investigates the application of AI-driven resource scheduling in cloud environments, proposing a framework that integrates workload forecasting, dynamic resource allocation, and SLA compliance optimisation. Experimental evaluation, based on simulated and real-trace datasets, demonstrates that the proposed AI-based scheduling approach can improve resource utilisation, reduce energy consumption, and lower SLA violation rates compared to baseline methods. The findings highlight the potential of AI to reshape scheduling in modern cloud infrastructures and underscore the need for further research into scalability, transparency of AI decisions, and hybrid edge-cloud deployment scenarios.
RevenuePilot: Operationalizing Agentic AI for Airline Revenue Management at Scale
Airline revenue management (RM) represents one of the most complex real-time optimization challenges in commercial operations, requiring simultaneous decisions across pricing, inventory allocation, and demand forecasting under significant uncertainty. Traditional RM systems, while mathematically sophisticated, struggle to adapt to rapidly changing market conditions, competitive dynamics, and emerging disruptions. This paper presents RevenuePilot, an agentic AI system that operationalizes large language model capabilities for airline revenue management at scale. RevenuePilot employs a multi-agent architecture with specialized agents for dynamic pricing optimization, inventory control, demand forecasting, and overbooking management, coordinated through a central orchestration layer. We evaluate RevenuePilot on a major airline’s domestic network, demonstrating a 4.2% improvement in revenue per available seat mile (RASM), 12% reduction in spoilage, and 8% decrease in denied boardings compared to the incumbent Expected Marginal Seat Revenue (EMSR) system. Our results show that agentic AI can effectively augment traditional optimization approaches while providing explainability and adaptability critical for operational deployment.
Optimization Techniques for High-Performance computing on CPU Architectures
This paper introduces a comprehensive methodology for optimizing Linux libraries to maximize performance on CPU architectures such as POWER. The proposed optimization pipeline encompasses compiler selection and configuration, runtime profiling, and manual vectorization. The methodology systematically addresses critical performance bottlenecks by applying architecture-specific compiler flags, managing dependencies strategically, and implementing targeted code-level optimizations. Proper compiler selection, use of optimized dependencies such as Open BLAS, and application of manual vectorization techniques are shown to yield performance improvements of 10-20 times over baseline implementations. Validation is provided through practical examples, including matrix multiplication libraries, which demonstrate measurable improvements in FLOPS and overall throughput. These findings offer actionable guidance for developers aiming to maximize CPU utilization in performance-critical Linux applications.
Cloud-Native Connectivity Paradigms: A Comparative Study of Managed Kafka Networking on AWS and Azure
The paradigm of real-time data streaming has transitioned from the operational overhead of self-managed Apache Kafka clusters to the abstraction of Managed Service Provider (MSP) models, exemplified by the Confluent Cloud "Kora" engine. While this transition eliminates the infrastructure management of broker orchestration and stateful scaling, it introduces significant challenges in the secure orchestration of data ingress and egress within multi-cloud environments. In enterprise deployments on Amazon Web Services (AWS) and Microsoft Azure, standard public endpoints are frequently precluded by stringent security mandates and the necessity to prevent data exfiltration. This study classifies and evaluates the dominant connectivity paradigms utilized to facilitate private communication between consumer applications and managed Kafka clusters. By analyzing the architectural trade-offs between bi-directional peering models and modern, unidirectional endpoint-based solutions, this paper highlights the nuances in DNS resolution and routing logic. We focus on AWS PrivateLink and Azure Private Link as the primary mechanisms for achieving logical isolation. The objective of this research is to provide a formal framework for selecting networking topologies that mitigate CIDR overlap conflicts while maintaining robust, private-path communication for mission-critical event streams.
A Proposed Hybrid Distributed Ledger Architecture for Cross-Border Payments: Design and Conceptual Framework
This paper presents a conceptual architecture for a hybrid distributed ledger system designed to address challenges in cross-border payments and remittances. Current payment infrastructure suffers from high costs, slow settlement times, and limited transparency. While cryptocurrency-based public blockchains and permissioned enterprise systems have been proposed separately, a comprehensive framework integrating both approaches with traditional payment rails remains lacking. We propose a multi-layered architectural design that combines Hyperledger Fabric for inter-bank settlement, R3 Corda for bilateral agreements, and existing payment systems for retail transactions. The proposed system incorporates consensus optimization strategies, atomic cross-ledger transaction protocols, and automated regulatory compliance mechanisms. Through architectural analysis and comparison with existing systems, we identify key design principles for scalability, interoperability, and regulatory compliance. This work contributes a detailed architectural specification and identifies critical research challenges requiring future investigation, including performance optimization, security validation, and regulatory framework development. The proposed architecture serves as a foundation for future implementation and empirical evaluation.
Evaluating the Impact of Shared UI Architectures on Reducing Time-to-Market and Defect Density in Multi-Product Organizations
Multi-product software organizations increasingly adopt shared user interface (UI) architectures to improve development efficiency, ensure consistency, and reduce quality issues across product portfolios. This paper presents a comprehensive literature-based evaluation of shared UI architectures—including component-based UI libraries, enterprise design systems, and cross-platform UI frameworks—and analyzes their impact on two critical software engineering outcomes: time-to-market (TTM) and defect density (DD). Through a systematic review and meta-synthesis of peer-reviewed research and large-scale industrial reports published between 2010 and 2024, we identify consistent evidence that shared UI architectures reduce development cycle time by 20–50% and UI-related defect density by up to 35% when supported by appropriate governance and automation. We further propose a conceptual impact model, identify moderating organizational factors, and formulate testable hypotheses to guide future empirical validation. The results provide actionable insights for researchers and practitioners evaluating shared UI strategies in multi-product environments.
Quality as Code: Operationalizing Policy, Risk, and Compliance through Executable Quality Engineering
Enterprises increasingly treat quality, policy, and compliance as runtime concerns, yet most governance still lives in documents, wikis, and manual checklists. This paper advances the concept of Quality as Code (QaC): policies and risk controls expressed as executable artifacts embedded into CI/CD pipelines, shared memory layers, and operational telemetry. By converting policies into guardrails and quality gates enforced through validation packs, policy tags, and evidence-linked decisions organizations can measure and continuously improve trust. We present reference architecture, implementation patterns, and adoption roadmap that demonstrate how to shift governance left and right simultaneously: left into design and pipelines, right into runtime assurance. Results include faster audit readiness, reduced decision variance, and verifiable lineage across regulated workflows.
Low-Code and Pro-Code Hybrid Architecture for Financial and Federal Regulatory Agencies
Financial institutions and federal regulatory agencies are under growing pressure to modernize information systems while maintaining high standards of security, reliability, and regulatory compliance. Traditional pro-code software development approaches offer flexibility and control but often result in long development cycles and high costs. Conversely, low-code platforms provide rapid application development and enable non-traditional developers to participate in solution design, yet they may lack the depth required for mission-critical and highly regulated environments. This paper examines a hybrid architectural approach that integrates low-code and pro-code paradigms to leverage the strengths of both. It analyzes regulatory drivers, architectural design principles, security and governance requirements, and organizational impacts. The study proposes a reference architecture suitable for financial and federal regulatory agencies and evaluates its benefits, risks, and implementation strategies. The paper concludes that hybrid architectures can significantly improve agility and efficiency while preserving the control and auditability required in regulated sectors
Federal compliance and regualatory systems