International Journal of Emerging Trends in Computer Science and Information Technology
type: channel
publisher: Eureka Vision Publication
language: en
publication name: International Journal of Emerging Trends in Computer Science and Information Technology
issn: 3050-9238
items: High-Throughput Data Processing Architectures on AWS for Logistics and Transportation Systems
items: The Software Industrial Revolution: Engineering Implications of AI Generated Software
items: Balancing Technical Debt Reduction and Feature Velocity in Legacy Front-End Systems
items: Small Language Models and Neuro-Symbolic AI in Zonal Architectures: The Rise of Small Language Models (SLMs) in Constrained Environments
items: Data Stewardship: How AI Agents Form the Pillars for Effective Data and AI Governance
items: Ontology and Future of Master Data Management in Respect to Data Quality
items: Optimizing Enterprise SPAs: Angular Standalone Components and Signals
items: Operationalizing Software Invariants: A DevOps-Driven Approach to Reliability in Cloud-Native Systems
items: Mitigating Algorithmic Complexity Attacks in Federated GraphQL Architectures: A Depth-Bounded Semantic Rate Limiting Approach for Open Banking
items: A Survey of Statistical Process Control and Its Applications in Manufacturing Quality Assurance
items: Networking Paradigms for Large-Scale IoT Deployments: A Survey on Reliability, Latency, and Scalability
items: Heat Dissipation Strategies for High-Performance Computing: A Review of Air and Liquid Cooling Efficiency
items: Serverless Architectures for Scalable Data Analytics Workflows in Cloud BI Systems
items: The Role of Event-Driven Architectures in Enterprise Digital Transformation Initiatives
items: Designing Zero-Downtime Migration Frameworks for Mission-Critical Legacy Systems to Microsoft Azure
items: AFP: An SLA-Aware Adaptive Freshness Protocol for Log Collection in Large-Scale Geographically Distributed Systems
items: Evaluating the Impact of Event Hub Based Streaming Architectures on Enterprise Decision Latency
items: 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
items: Operational Telemetry and Observability in Ingestion Pipelines
items: Ultra-Low Latency AI Systems: Leveraging Edge AI and Semiconductor Acceleration for Local Language Model Inference
items: REST/GraphQL APIs for Dynamic Analytics
items: Correspondent Banking and Nested Relationships: Managing Cross-Border AML/CTF Risk, Due Diligence, and Monitoring Expectations
items: Resource Scheduling Using AI in Cloud Environments
items: RevenuePilot: Operationalizing Agentic AI for Airline Revenue Management at Scale
items: Optimization Techniques for High-Performance computing on CPU Architectures
items: Cloud-Native Connectivity Paradigms: A Comparative Study of Managed Kafka Networking on AWS and Azure
items: A Proposed Hybrid Distributed Ledger Architecture for Cross-Border Payments: Design and Conceptual Framework
items: Evaluating the Impact of Shared UI Architectures on Reducing Time-to-Market and Defect Density in Multi-Product Organizations
items: Quality as Code: Operationalizing Policy, Risk, and Compliance through Executable Quality Engineering
items: Low-Code and Pro-Code Hybrid Architecture for Financial and Federal Regulatory Agencies

High-Throughput Data Processing Architectures on AWS for Logistics and Transportation Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: AWS
  topic: keywords
subject: High-Throughput Processing
  topic: keywords
subject: Logistics Systems
  topic: keywords
subject: Stream Processing
  topic: keywords
subject: Batch Processing
  topic: keywords
subject: Amazon Kinesis
  topic: keywords
subject: Scalability
  topic: keywords
subject: Iot Data Processing
  topic: keywords
creator: Mohammed Abdul Mannan Ansari
rights: Copyright (c) 2026
date: 2026-03-13
publication date: 2026-03-13
starting page: 300
ending page: 304
doi: 10.63282/3050-9246.IJETCSIT-V7I1P144

The Software Industrial Revolution: Engineering Implications of AI Generated Software
type: item
description: 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.
subject: Articles
  topic: section
subject: AI Generated Software
  topic: keywords
subject: Software Engineering
  topic: keywords
subject: Generative AI
  topic: keywords
subject: Software Architecture
  topic: keywords
subject: Automated Development
  topic: keywords
subject: AI Assisted Coding
  topic: keywords
creator: Bharath Kandati
rights: Copyright (c) 2026
date: 2026-03-12
publication date: 2026-03-12
starting page: 296
ending page: 299
doi: 10.63282/3050-9246.IJETCSIT-V7I1P143

Balancing Technical Debt Reduction and Feature Velocity in Legacy Front-End Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Technical Debt
  topic: keywords
subject: Legacy Front-End Systems
  topic: keywords
subject: Feature Velocity
  topic: keywords
subject: Component Architecture
  topic: keywords
subject: Micro-Frontends
  topic: keywords
subject: Observability
  topic: keywords
subject: Enterprise Web Modernization
  topic: keywords
creator: Mounica Singireddy
rights: Copyright (c) 2026
date: 2026-03-11
publication date: 2026-03-11
starting page: 290
ending page: 295
doi: 10.63282/3050-9246.IJETCSIT-V7I1P142

Small Language Models and Neuro-Symbolic AI in Zonal Architectures: The Rise of Small Language Models (SLMs) in Constrained Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Small Language Models (Slms)
  topic: keywords
subject: Zonal Architecture
  topic: keywords
subject: Neuro-Symbolic AI
  topic: keywords
subject: Software Defined Vehicle (SDV)
  topic: keywords
subject: Edge Computing
  topic: keywords
subject: Adaptive AUTOSAR
  topic: keywords
subject: Deterministic Safety
  topic: keywords
subject: Real-Time Systems
  topic: keywords
subject: Knowledge Distillation
  topic: keywords
subject: ISO 26262
  topic: keywords
subject: Time-Sensitive Networking (TSN)
  topic: keywords
creator: Naresh Kalimuthu
rights: Copyright (c) 2026
date: 2026-03-10
publication date: 2026-03-10
starting page: 285
ending page: 289
doi: 10.63282/3050-9246.IJETCSIT-V7I1P141

Data Stewardship: How AI Agents Form the Pillars for Effective Data and AI Governance
type: item
description: 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.
subject: Articles
  topic: section
subject: Agentic AI
  topic: keywords
subject: Data Governance
  topic: keywords
subject: Data Quality
  topic: keywords
subject: Data Stewardship
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Master Data Management
  topic: keywords
subject: Metadata Management
  topic: keywords
subject: Responsible AI
  topic: keywords
creator: Susmit Sen
rights: Copyright (c) 2024
date: 2024-12-30
publication date: 2024-12-30
starting page: 151
ending page: 155
doi: 10.63282/3050-9246.IJETCSIT-V5I4P117

Ontology and Future of Master Data Management in Respect to Data Quality
type: item
description: 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.
subject: Articles
  topic: section
subject: Ontology
  topic: keywords
subject: Master Data Management (Mdm)
  topic: keywords
subject: Data Quality Management
  topic: keywords
subject: Semantic Data Integration
  topic: keywords
subject: Knowledge Graphs
  topic: keywords
subject: Data Governance
  topic: keywords
creator: Susmit Sen
rights: Copyright (c) 2024
date: 2024-06-30
publication date: 2024-06-30
starting page: 159
ending page: 163
doi: 10.63282/3050-9246.IJETCSIT-V5I2P117

Optimizing Enterprise SPAs: Angular Standalone Components and Signals
type: item
description: 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 s…
subject: Articles
  topic: section
subject: Angular Framework
  topic: keywords
subject: Single Page Applications
  topic: keywords
subject: Standalone Components
  topic: keywords
subject: Angular Signals
  topic: keywords
subject: Enterprise Web Applications
  topic: keywords
subject: Frontend Architecture
  topic: keywords
subject: Reactive Programming
  topic: keywords
subject: Performance Optimization
  topic: keywords
creator: Narendra Kumar Kuntamukkala
rights: Copyright (c) 2023
date: 2023-03-30
publication date: 2023-03-30
starting page: 189
ending page: 200
doi: 10.63282/3050-9246.IJETCSIT-V4I1P120

Operationalizing Software Invariants: A DevOps-Driven Approach to Reliability in Cloud-Native Systems
type: item
description: 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 en…
subject: Articles
  topic: section
subject: SRE
  topic: keywords
subject: Monitoring
  topic: keywords
subject: Alerting
  topic: keywords
subject: Slis/Slos
  topic: keywords
subject: Observability Design
  topic: keywords
subject: Incident Response Reliability Metrics
  topic: keywords
subject: Site Reliability Engineering
  topic: keywords
subject: Cloud Reliability Devops
  topic: keywords
subject: Distributed Systems Reliability
  topic: keywords
subject: Enterprise SRE
  topic: keywords
subject: Angular Observability
  topic: keywords
subject: Angular Performance Optimization
  topic: keywords
subject: Real User Monitoring (Angular) Angular Error Handling
  topic: keywords
subject: Core Web Vitals (Angular)
  topic: keywords
subject: Angular Telemetry
  topic: keywords
subject: Frontend Reliability Engineering
  topic: keywords
subject: Full-Stack Observability
  topic: keywords
creator: Sumith Thalary
creator: Narendra Kumar Kuntamukkala
rights: Copyright (c) 2022
date: 2022-12-30
publication date: 2022-12-30
starting page: 157
ending page: 168
doi: 10.63282/3050-9246.IJETCSIT-V3I4P115

Mitigating Algorithmic Complexity Attacks in Federated GraphQL Architectures: A Depth-Bounded Semantic Rate Limiting Approach for Open Banking
type: item
description: 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. Trad…
subject: Articles
  topic: section
subject: GraphQL Security
  topic: keywords
subject: Semantic Rate Limiting
  topic: keywords
subject: Algorithmic DoS Mitigation
  topic: keywords
subject: Query Complexity Analysis
  topic: keywords
subject: Federated Schema Stitching
  topic: keywords
subject: Recursive Depth Bounding
  topic: keywords
subject: Introspection Defense
  topic: keywords
subject: Token Bucket Algorithms
  topic: keywords
subject: Abstract Syntax Tree (AST) Parsing
  topic: keywords
subject: Open Banking Standards
  topic: keywords
subject: API Gateway Governance
  topic: keywords
subject: Resource Exhaustion Prevention
  topic: keywords
subject: Declarative Data Fetching
  topic: keywords
subject: Distributed Denial of Service (DDoS)
  topic: keywords
subject: Angular Framework
  topic: keywords
subject: Component-Based Architecture
  topic: keywords
subject: Single-Page Applications (SPA)
  topic: keywords
subject: TypeScript
  topic: keywords
subject: Dependency Injection
  topic: keywords
subject: Reactive Programming
  topic: keywords
subject: RxJS
  topic: keywords
subject: Modular Frontend Architecture
  topic: keywords
subject: Client-Side Rendering
  topic: keywords
subject: Web Application Development
  topic: keywords
subject: Cloud Infrastructure Automation
  topic: keywords
subject: CI/CD Pipeline Engineering
  topic: keywords
subject: Infrastructure as Code (Terraform/Bicep)
  topic: keywords
subject: Kubernetes & Container Orchestration
  topic: keywords
subject: Cloud Security & DevSecOps
  topic: keywords
subject: API Gateway Governance & Zero Trust
  topic: keywords
subject: Monitoring
  topic: keywords
subject: Observability & SRE Practices
  topic: keywords
subject: Distributed Systems Reliability Engineering
  topic: keywords
subject: Scalable Microservices Architecture
  topic: keywords
subject: Identity & Access Management (IAM)
  topic: keywords
subject: Cost Optimization & Cloud Governance
  topic: keywords
subject: Service Mesh & Network Policies
  topic: keywords
subject: Secrets Management & Compliance Automation
  topic: keywords
subject: Automated Deployment & Release Management
  topic: keywords
subject: High Availability & Disaster Recovery Engineering
  topic: keywords
creator: Anvesh Katipelly
creator: Narendra Kumar Kuntamukkala
rights: Copyright (c) 2022
date: 2022-09-30
publication date: 2022-09-30
starting page: 112
ending page: 121
doi: 10.63282/3050-9246.IJETCSIT-V3I3P112

A Survey of Statistical Process Control and Its Applications in Manufacturing Quality Assurance
type: item
description: 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.
subject: Articles
  topic: section
subject: Statistical Process Control
  topic: keywords
subject: Analysis Manufacturing Systems
  topic: keywords
subject: Variation Reduction
  topic: keywords
subject: Industrial Quality Management
  topic: keywords
subject: Continuous Improvement
  topic: keywords
creator: Muzaffer Hussain Syed
creator: Uday Kumar Ragireddy
creator: Prasanth Varma Addepalli
creator: Sridhar Reddy Bandaru
creator: Dhuli Shyam
creator: Prabu Manoharan
rights: Copyright (c) 2022
date: 2022-03-30
publication date: 2022-03-30
starting page: 127
ending page: 135
doi: 10.63282/3050-9246.IJETCSIT-V3I1P114

Networking Paradigms for Large-Scale IoT Deployments: A Survey on Reliability, Latency, and Scalability
type: item
description: 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.
subject: Articles
  topic: section
subject: Internet of Things (IoT)
  topic: keywords
subject: Large-Scale IoT Networks
  topic: keywords
subject: Zero-Trust Security
  topic: keywords
subject: AI-Driven IoT Security
  topic: keywords
subject: Gateway Placement
  topic: keywords
subject: Botnet Detection
  topic: keywords
creator: Sajay Dasari
creator: Venkata Kishore Chilakapati
creator: Srikanth Reddy Keshireddy
creator: Venkata Teja Nagumotu
creator: Harsha Vardhan Reddy Kavuluri
creator: Akhil Kumar Pathani
rights: Copyright (c) 2022
date: 2022-03-30
publication date: 2022-03-30
starting page: 136
ending page: 144
doi: 10.63282/3050-9246.IJETCSIT-V3I1P115

Heat Dissipation Strategies for High-Performance Computing: A Review of Air and Liquid Cooling Efficiency
type: item
description: 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.
subject: Articles
  topic: section
subject: High-Performance Computing
  topic: keywords
subject: Heat Dissipation
  topic: keywords
subject: Air Cooling
  topic: keywords
subject: Liquid Cooling
  topic: keywords
subject: Energy Efficiency
  topic: keywords
subject: Thermal Management
  topic: keywords
creator: Prathik Kumar Jannu
creator: Javed Ali Mohammad
creator: Sri Harsha Panchali
creator: Usha Mohani kavirayani
creator: Krishna Bhardwaj Mylavarapu
creator: Jenitha Pilli
rights: Copyright (c) 2022
date: 2022-03-30
publication date: 2022-03-30
starting page: 145
ending page: 153
doi: 10.63282/3050-9246.IJETCSIT-V3I1P116

Serverless Architectures for Scalable Data Analytics Workflows in Cloud BI Systems
type: item
description: 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, statefu…
subject: Articles
  topic: section
subject: Serverless Computing
  topic: keywords
subject: Cloud Bi Systems
  topic: keywords
subject: Scalable Data Analytics
  topic: keywords
subject: Event-Driven Architectures
  topic: keywords
subject: Distributed Data Processing
  topic: keywords
subject: Workflow Orchestration
  topic: keywords
subject: Elastic Resource Management
  topic: keywords
subject: Function-As-A-Service (Faas)
  topic: keywords
subject: Multi-Tenant Analytics Platforms
  topic: keywords
subject: Observability In Serverless Systems
  topic: keywords
subject: Cost-Aware Execution
  topic: keywords
subject: Cloud-Native Data Pipelines
  topic: keywords
subject: Fault Tolerance And Reliability
  topic: keywords
subject: Performance Optimization
  topic: keywords
subject: Metadata-Driven Analytics
  topic: keywords
creator: Milan Gupta
rights: Copyright (c) 2025
date: 2025-12-30
publication date: 2025-12-30
starting page: 226
ending page: 242
doi: 10.63282/3050-9246.IJETCSIT-V6I4P134

The Role of Event-Driven Architectures in Enterprise Digital Transformation Initiatives
type: item
description: 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.
subject: Articles
  topic: section
subject: Event-Driven Architecture
  topic: keywords
subject: Digital Transformation
  topic: keywords
subject: Enterprise Operating Model
  topic: keywords
subject: Domain-Driven Design
  topic: keywords
subject: Event Governance
  topic: keywords
subject: Observability
  topic: keywords
subject: Legacy Modernization
  topic: keywords
subject: Microservices
  topic: keywords
subject: Asynchronous Communication
  topic: keywords
subject: Distributed Systems
  topic: keywords
creator: Pradeep Kachakayala
rights: Copyright (c) 2022
date: 2022-06-03
publication date: 2022-06-03
starting page: 127
ending page: 131
doi: 10.63282/3050-9246.IJETCSIT-V3I2P114

Designing Zero-Downtime Migration Frameworks for Mission-Critical Legacy Systems to Microsoft Azure
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Migration
  topic: keywords
subject: Microsoft Azure
  topic: keywords
subject: Zero-Downtime
  topic: keywords
subject: Change Data Capture
  topic: keywords
subject: Mission-Critical Systems
  topic: keywords
subject: Strangler Fig Pattern
  topic: keywords
subject: Transactional Integrity
  topic: keywords
subject: Distributed Systems
  topic: keywords
subject: High Availability
  topic: keywords
creator: Pradeep Kachakayala
rights: Copyright (c) 2020
date: 2020-12-30
publication date: 2020-12-30
starting page: 79
ending page: 85
doi: 10.63282/3050-9246.IJETCSIT-V1I4P109

AFP: An SLA-Aware Adaptive Freshness Protocol for Log Collection in Large-Scale Geographically Distributed Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Adaptive Freshness
  topic: keywords
subject: SLA-Aware Log Collection
  topic: keywords
subject: Geo-Distributed Systems
  topic: keywords
subject: Per-Stream Optimization
  topic: keywords
subject: Shared Logs
  topic: keywords
subject: Graceful Degradation
  topic: keywords
creator: Milan Gupta
rights: Copyright (c) 2025
date: 2025-03-24
publication date: 2025-03-24
starting page: 157
ending page: 167
doi: 10.63282/3050-9246.IJETCSIT-V6I1P119

Evaluating the Impact of Event Hub Based Streaming Architectures on Enterprise Decision Latency
type: item
description: 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.
subject: Articles
  topic: section
subject: Azure Event Hubs
  topic: keywords
subject: Decision Latency
  topic: keywords
subject: Stream Analytics
  topic: keywords
subject: Change Data Capture
  topic: keywords
subject: Aiops
  topic: keywords
subject: Kappa Architecture
  topic: keywords
subject: Real-Time Analytics
  topic: keywords
subject: Enterprise Resource Planning
  topic: keywords
subject: Business Intelligence
  topic: keywords
creator: Pradeep Kachakayala
rights: Copyright (c) 2026
date: 2026-03-08
publication date: 2026-03-08
starting page: 267
ending page: 271
doi: 10.63282/3050-9246.IJETCSIT-V7I1P139

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
type: item
description: 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.
subject: Articles
  topic: section
subject: Apache Kafka
  topic: keywords
subject: Apache Flink
  topic: keywords
subject: Spark Structured Streaming
  topic: keywords
subject: stream processing
  topic: keywords
subject: multi-cloud
  topic: keywords
subject: Delta Lake
  topic: keywords
subject: benchmarking
  topic: keywords
subject: fault tolerance
  topic: keywords
subject: exactly-once semantics
  topic: keywords
subject: reproducibility
  topic: keywords
creator: Sai Kiran Yadav Battula
rights: Copyright (c) 2026
date: 2026-03-06
publication date: 2026-03-06
starting page: 254
ending page: 266
doi: 10.63282/3050-9246.IJETCSIT-V7I1P138

Operational Telemetry and Observability in Ingestion Pipelines
type: item
description: 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.
subject: Articles
  topic: section
subject: Operational Telemetry
  topic: keywords
subject: Data Pipeline Observability
  topic: keywords
subject: Ingestion Pipeline Monitoring
  topic: keywords
subject: Open Telemetry
  topic: keywords
creator: Shreyansh Sharma
rights: Copyright (c) 2026
date: 2026-03-04
publication date: 2026-03-04
starting page: 245
ending page: 253
doi: 10.63282/3050-9246.IJETCSIT-V7I1P137

Ultra-Low Latency AI Systems: Leveraging Edge AI and Semiconductor Acceleration for Local Language Model Inference
type: item
description: 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 t…
subject: Articles
  topic: section
subject: Edge Artificial Intelligence
  topic: keywords
subject: Ultra Low Latency Systems
  topic: keywords
subject: Semiconductor Acceleration
  topic: keywords
subject: Local Language Models
  topic: keywords
subject: On Device AI Inference
  topic: keywords
subject: Neural Processing Units
  topic: keywords
subject: Edge Computing Architecture
  topic: keywords
subject: Hardware Accelerated Machine Learning
  topic: keywords
creator: Rohit Chandrakant Kulkarni
rights: Copyright (c) 2026
date: 2026-03-09
publication date: 2026-03-09
starting page: 272
ending page: 284
doi: 10.63282/3050-9246.IJETCSIT-V7I1P140

REST/GraphQL APIs for Dynamic Analytics
type: item
description: 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.
subject: Articles
  topic: section
subject: REST API
  topic: keywords
subject: Graphql
  topic: keywords
subject: Dynamic Analytics
  topic: keywords
subject: API Architectures
  topic: keywords
subject: Graphql Aggregation
  topic: keywords
creator: Ramesh Kasarla
rights: Copyright (c) 2026
date: 2026-03-03
publication date: 2026-03-03
starting page: 237
ending page: 244
doi: 10.63282/3050-9246.IJETCSIT-V7I1P136

Correspondent Banking and Nested Relationships: Managing Cross-Border AML/CTF Risk, Due Diligence, and Monitoring Expectations
type: item
description: 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.
subject: Articles
  topic: section
subject: Correspondent Banking
  topic: keywords
subject: Nested Relationships
  topic: keywords
subject: Cross-Border Banking
  topic: keywords
subject: International Financial Transactions
  topic: keywords
subject: Foreign Correspondent Accounts
  topic: keywords
subject: Payable-Through Accounts
  topic: keywords
subject: Intermediary Banking
  topic: keywords
subject: Anti-Money Laundering (AML)
  topic: keywords
subject: Counter-Terrorist Financing (CTF)
  topic: keywords
subject: Financial Crime Compliance
  topic: keywords
subject: Regulatory Compliance
  topic: keywords
creator: Mallikarjun Reddy Gouni
rights: Copyright (c) 2024
date: 2024-03-30
publication date: 2024-03-30
starting page: 149
ending page: 171
doi: 10.63282/3050-9246.IJETCSIT-V5I1P116

Resource Scheduling Using AI in Cloud Environments
type: item
description: 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.
subject: Articles
  topic: section
subject: Cloud Computing
  topic: keywords
subject: Resource Scheduling
  topic: keywords
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Task Scheduling
  topic: keywords
subject: Dynamic Resource Allocation
  topic: keywords
subject: Virtual Machine (VM) Allocation
  topic: keywords
subject: Container Orchestration
  topic: keywords
subject: Load Balancing
  topic: keywords
subject: Workflow Scheduling
  topic: keywords
subject: Auto-Scaling
  topic: keywords
subject: Quality of Service (QoS)
  topic: keywords
subject: Service Level Agreement (SLA)
  topic: keywords
subject: Energy-Efficient Computing
  topic: keywords
subject: Cost Optimization
  topic: keywords
subject: Predictive Analytics
  topic: keywords
subject: Reinforcement Learning
  topic: keywords
subject: Heuristic Optimization
  topic: keywords
subject: Metaheuristic Algorithms
  topic: keywords
subject: Distributed Systems
  topic: keywords
creator: Ramadevi Sannapureddy
creator: Sanketh Nelavelli
rights: Copyright (c) 2023
date: 2023-12-30
publication date: 2023-12-30
starting page: 193
ending page: 208
doi: 10.63282/3050-9246.IJETCSIT-V4I4P122

RevenuePilot: Operationalizing Agentic AI for Airline Revenue Management at Scale
type: item
description: 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.
subject: Articles
  topic: section
subject: Airline Revenue Management
  topic: keywords
subject: Agentic AI
  topic: keywords
subject: Large Language Models
  topic: keywords
subject: Dynamic Pricing
  topic: keywords
subject: Inventory Control
  topic: keywords
subject: Demand Forecasting
  topic: keywords
subject: Multi-Agent Systems
  topic: keywords
subject: EMSR
  topic: keywords
subject: Real-Time Optimization
  topic: keywords
creator: Sandeep Nutakki
rights: Copyright (c) 2026
date: 2026-03-01
publication date: 2026-03-01
starting page: 230
ending page: 236
doi: 10.63282/3050-9246.IJETCSIT-V7I1P135

Optimization Techniques for High-Performance computing on CPU Architectures
type: item
description: 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.
subject: Articles
  topic: section
subject: CPU Optimization
  topic: keywords
subject: Compiler Optimization
  topic: keywords
subject: Vectorization
  topic: keywords
subject: Linux Libraries
  topic: keywords
subject: High-Performance Computing
  topic: keywords
subject: SIMD Instructions
  topic: keywords
subject: Performance Profiling
  topic: keywords
creator: Rajalakshmi Srinivasaraghavan
rights: Copyright (c) 2026
date: 2026-02-28
publication date: 2026-02-28
starting page: 226
ending page: 229
doi: 10.63282/3050-9246.IJETCSIT-V7I1P134

Cloud-Native Connectivity Paradigms: A Comparative Study of Managed Kafka Networking on AWS and Azure
type: item
description: 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.
subject: Articles
  topic: section
subject: AWS Private link
  topic: keywords
subject: Azure Private Link
  topic: keywords
subject: Cloud-Native Architecture
  topic: keywords
subject: Confluent Cloud
  topic: keywords
subject: Event Streaming
  topic: keywords
subject: Managed Apache Kafka
  topic: keywords
subject: Multi-Cloud Networking
  topic: keywords
subject: Zero-Trust Security
  topic: keywords
creator: Girish Rameshbabu
rights: Copyright (c) 2026
date: 2026-02-28
publication date: 2026-02-28
starting page: 218
ending page: 225
doi: 10.63282/3050-9246.IJETCSIT-V7I1P133

A Proposed Hybrid Distributed Ledger Architecture for Cross-Border Payments: Design and Conceptual Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: Distributed Ledger Technology
  topic: keywords
subject: Cross-Border Payments
  topic: keywords
subject: Architecture Design
  topic: keywords
subject: Hyperledger Fabric
  topic: keywords
subject: R3 Corda
  topic: keywords
subject: System Proposal
  topic: keywords
subject: Payment Systems
  topic: keywords
subject: Blockchain Interoperability
  topic: keywords
creator: Anath Bandhu Chatterjee
rights: Copyright (c) 2026
date: 2026-02-26
publication date: 2026-02-26
starting page: 210
ending page: 217
doi: 10.63282/3050-9246.IJETCSIT-V7I1P132

Evaluating the Impact of Shared UI Architectures on Reducing Time-to-Market and Defect Density in Multi-Product Organizations
type: item
description: 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.
subject: Articles
  topic: section
subject: Shared UI Architectures
  topic: keywords
subject: Design Systems
  topic: keywords
subject: Component Reuse
  topic: keywords
subject: Cross-Platform UI
  topic: keywords
subject: Time-To-Market
  topic: keywords
subject: Defect Density
  topic: keywords
subject: Software Quality
  topic: keywords
subject: Multi-Product Organizations
  topic: keywords
creator: Somraju Gangishetti
rights: Copyright (c) 2026
date: 2026-02-25
publication date: 2026-02-25
starting page: 205
ending page: 209
doi: 10.63282/3050-9246.IJETCSIT-V7I1P131

Quality as Code: Operationalizing Policy, Risk, and Compliance through Executable Quality Engineering
type: item
description: 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.
subject: Articles
  topic: section
subject: Quality as Code
  topic: keywords
subject: Executable Governance
  topic: keywords
subject: Policy as Code
  topic: keywords
subject: Risk Management
  topic: keywords
subject: Compliance
  topic: keywords
subject: Integrated Quality Engineering
  topic: keywords
subject: Shared Memory
  topic: keywords
subject: Validation Packs
  topic: keywords
subject: Quality Gates
  topic: keywords
subject: CI/CD
  topic: keywords
subject: Telemetry
  topic: keywords
subject: Decision Lineage
  topic: keywords
subject: Explainability
  topic: keywords
subject: Confidence Governance
  topic: keywords
subject: Auditability
  topic: keywords
creator: Ameen Shahid Kolothum Thodika
rights: Copyright (c) 2026
date: 2026-02-24
publication date: 2026-02-24
starting page: 201
ending page: 204
doi: 10.63282/3050-9246.IJETCSIT-V7I1P130

Low-Code and Pro-Code Hybrid Architecture for Financial and Federal Regulatory Agencies
type: item
description: 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
subject: Articles
  topic: section
subject: Lowcode
  topic: keywords
subject: Pro Code
  topic: keywords
subject: Microservices
  topic: keywords
subject: API’s
  topic: keywords
subject: CI/CD Pipelines
  topic: keywords
subject: Security
  topic: keywords
subject: Dataintegration
  topic: keywords
subject: Federal compliance and regualatory systems
  topic: keywords
subject: Case investigation
  topic: keywords
subject: Workflows
  topic: keywords
subject: SAR
  topic: keywords
subject: Audit trails
  topic: keywords
creator: Sriramakrishna Vadlamudi
rights: Copyright (c) 2026
date: 2026-02-23
publication date: 2026-02-23
starting page: 197
ending page: 200
doi: 10.63282/3050-9246.IJETCSIT-V7I1P129