Computer and Systems Architecture Commons^™

Comparison Of Kafka Operators: Strimzi Vs Koperator Vs Confluent: A Comprehensive Industry Analysis, Ankit Anthony, Gary Clynch

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Comparison of Kafka Operators: Strimzi vs Koperator vs Confluent: A Comprehensive Industry Analysis

Analysis Of Impact Of Configuration Choice Upon Azure Service Bus Performance, Brian Skehan, Mary Rose Donnelly

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Analysis of impact of configuration choice upon Azure Service Bus performance.

Comparative Analysis Of Mysql And Mongodb In A High-Concurrency System, Gabriel Solares, Cormac Keogh

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Comparative Analysis of MySQL and MongoDB in a High-Concurrency System

Analysis Of Functional Programming Languages For Use In Serverless Lambda Functions On Aws Platform, William Spain, Gary Clynch

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Analysis of functional programming languages for use in serverless lambda functions on AWS platform

A Comparison Of Terraform And Bicep Quality Attributes, Colm O'Hara, Kevin Bayliss

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A Comparison of Terraform and BICEP Quality Attributes

Comparing Kubernetes And Nomad For Hosting .Net Legacy Workloads In Azure, Luke Osbourne, Omar Portillo

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Comparing Kubernetes and Nomad for hosting .NET legacy workloads in Azure

Evaluating Kubernetes Security Mechanisms For Dos Prevention: A Comparative Analysis Of Multi-Layer Protection Strategies, Sergej Dikun, Omar Portillo

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Evaluating Kubernetes Security Mechanisms for DOS Prevention: A Comparative Analysis of Multi-layer Protection Strategies

Improving Iac Script Quality: Evaluating Static Analysis Tools And Establishing Best Practices, Isha Rai, Kevin Bayliss

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Improving IaC Script Quality: Evaluating Static Analysis Tools and Establishing Best Practices

Comparative Cost And Capacity Analysis Of Managed Service And Non-Managed Service Api Gateway Architectures On Leading Cloud Platforms, George Brown, Cormac Keogh

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Comparative Cost and Capacity Analysis of Managed Service and Non-Managed Service API Gateway Architectures on Leading Cloud Platforms

The Irish Food Manufacturing Industry’S Preparedness For Nis2 Cybersecurity, Niall Mcgowan, Mary Rose Donnelly

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The Irish Food Manufacturing Industry’s Preparedness for NIS2 Cybersecurity

A Comparison Of Aks And K3s On Vmss, Craig Dillon, Omar Portillo

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A Comparison of AKS and K3s on VMSS

Comparison Of Asynchronous Architectural Patterns On Aws & Gcp Using Terraform, Alan Mcgee, Mary Rose Donnelly

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Comparison of Asynchronous Architectural Patterns on AWS & GCP Using Terraform

Event-Based Histogram Of Gradients For Lane Detection, Ganesh Gupta

Computer Science and Engineering Theses - Archive

In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras …

Training Data Privacy In Machine Learning: A Systematization Of Attacks And Defenses, Mohammad Sufyaan Saeed

Computer Science and Engineering Theses - Archive

Training and deploying Machine Learning (ML) models introduce significant data confidentiality risks, as modern models can inadvertently memorize and leak information about their training data. While attacks such as membership inference and model inversion are well studied, the literature remains fragmented, with inconsistent threat models and unclear relationships across attack classes and defenses. This work presents a Systematization of Knowledge (SoK) that unifies the landscape of training-data privacy attacks and defenses, aligning them with the NIST Adversarial Machine Learning (AML) taxonomy to enable standardized threat modeling and comparison. Our analysis shows that, despite significant progress in characterizing attack vectors, defenses …

Methods Of Optimizing Storage And Retrieval Of Structured Data, Neelim Haider

Computer Science and Engineering Theses - Archive

Storing and retrieving large amounts of data reliably is becoming more and more important as time goes on. There are high demands to store highly personal information such as social security numbers, bank account information, and residence information to rapidly changing data such as employee information, inventory information, and stock information. Therefore, the ability of a system to store, remove, and update such information efficiently and correctly is critical. There are different types of data that database systems can potentially hold: structured, unstructured, and semistructured data. Various database models have been developed to provide a framework that allows designers to …

Diversity-Driven Xor Secret Sharing: Reliable And Secure Multi-Path Transmission, Richard M. Olu Jordan

Computer Science and Engineering Theses - Archive

The growing reliance on distributed storage and multipath communication sys- tems has intensified the need for security mechanisms that remain robust even when individual nodes or channels are compromised. Secret sharing provides an information- theoretic approach to achieving both confidentiality and availability, and XOR-based constructions in particular offer lightweight and highly structured designs. This thesis develops a unified analytical framework for understanding and evalu- ating XOR-based secret sharing schemes across multiple operational settings, includ- ing plaintext storage, encrypted-data scenarios, and noisy binary symmetric chan- nels (BSCs). Building on a general (t, n) system model, we examine five threshold configurations—(2, 3), …

An Slo-Aware, Multi-Pronged Approach To Enhancing Resource And Energy Efficiency In Server Applications, Ning Li

Computer Science and Engineering Dissertations - Archive

Server applications operating in oversubscribed cloud environments face the dual challenges of meeting strict Quality-of-Service (QoS) requirements and improving resource and energy efficiency. As the number of user connections and workload diversity continue to grow, existing scheduling mechanisms struggle to balance QoS guarantees, fairness, resource efficiency, and power consumption. This dissertation presents a unified, cross-layer framework to address these challenges through three key contributions: AppleS, UTSLO, and REEF.

First, we propose AppleS, a user-space QoS-aware fine-grained I/O scheduling framework that delivers fair and efficient service to concurrent client connections. AppleS introduces a QoS-centric metric that guides admission control and scheduling …

Fair And Sustainable Machine Learning: A Holistic Approach To Data Quality, Efficiency, And Resource-Aware Training, Zahidur Rahim Talukder

Computer Science and Engineering Dissertations - Archive

The increasing reliance on distributed, privacy-sensitive data has driven the emergence of Federated Learning (FL) as a transformative paradigm for collaborative machine learning. By enabling multiple client devices to train a shared global model without transferring raw data, FL offers significant privacy advantages. However, real-world deployments of FL are constrained by critical challenges such as data heterogeneity, client unreliability, and hardware disparities. These factors lead to uneven model convergence, degraded global accuracy, and fairness issues that threaten FL's scalability and inclusivity in diverse environments.

This dissertation investigates these challenges and proposes three novel algorithmic frameworks to advance the state-of-the-art in …

Exploring Emerging Memory Technologies For Enhancing Data Center Applications, Zhen Lin

Computer Science and Engineering Dissertations - Archive

The rapid evolution of memory and storage technologies is fundamentally reshaping the design of operating systems and data management. Emerging devices such as persistent memory, NVMe SSDs, and Compute Express Link (CXL)--enabled hybrid memory modules introduce new opportunities for high-performance, cost-efficient data management, yet they also expose limitations in traditional software abstractions. File systems, originally designed for slow block-based devices, incur excessive overhead on ultra-low-latency media, while block-level caches suffer from metadata and eviction inefficiencies. Moreover, hardware-managed tiering provides transparency but restricts adaptability across workloads. These challenges highlight the need to rethink caching and tiered memory management across multiple system …

Optimizing Architecture And Software For Next-Generation Memory Systems, Lingfeng Xiang

Computer Science and Engineering Dissertations - Archive

The rapid advancement of memory technologies presents new challenges and opportunities for system software and architectural design. This dissertation investigates how to optimize modern computing systems for next-generation memory, mostly focusing on persistent memory and Compute Express Link (CXL)-based memory. First, we conduct a detailed characterization of Intel Optane DC Persistent Memory, identifying the distinct behaviors of its on-DIMM read and write buffers and analyzing their impact on application performance. These insights motivate optimizations that decouple read and write paths, revealing that random read latency—especially in pointer-chasing workloads—is a dominant performance bottleneck. Second, we present NOMAD, a page management framework …

Breaking Granularity Barriers: Overcoming I/O Abstraction Limitations For High-Performance Storage Systems, Chen Zhong

Computer Science and Engineering Dissertations - Archive

Modern storage hierarchies exhibit performance differences spanning eight orders of magnitude. Each tier in the hierarchy presents fundamentally different optimal access sizes and patterns. Most systems read and write data in fixed-size units across different tiers, typically power-of-2 sizes. It simplifies data management but also suffers from large write amplification when handling small accesses in many real-world workloads. This fundamental disconnect between fixed-size I/O design assumption and real-world usage patterns represents the core challenge.

The storage community has recognized the limitations of fixed-size I/O abstractions and is exploring alternatives, like the new emerging NVMe-KV interface and object-based storage, which aim …

Examining Terraform And Bicep: A Comparative Analysis Of Infrastructure As Code Tools For Provisioning Azure Environments, Richard Coffey, Kevin Bayliss

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Examining Terraform and Bicep: A Comparative Analysis of Infrastructure as Code Tools for Provisioning Azure Environments

Nomad Vs. Kubernetes, Alexandru Constantin Cardas, Omar Portillo

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Nomad vs. Kubernetes

Investigation Into Finops Techniques To Optimise Cost In Aws Cloud Deployments, Denis Parker, Kevin Bayliss

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Investigation Into FinOps Techniques To Optimise Cost in AWS Cloud Deployments

The Business-Day Cloud: A Hybrid Kubernetes And Serverless Solution For Sustainable Scaling With Predictable Load Patterns, Brendan Burnside, David White

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The Business-Day Cloud: A Hybrid Kubernetes and Serverless solution for Sustainable Scaling with Predictable Load Patterns

An Evaluation Of Zero Trust Principles In Modern Software Development, Cezar Vararu, David White

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An evaluation of Zero Trust Principles in modern software development

Cost Optimization In Open Telemetry, Niksa Jadric, Cormac Keogh

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Cost Optimization in Open Telemetry

Exploring Rust’S Performance In A Serverless Environment, Saoirse Mullen, Gary Clynch

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Exploring Rust’s Performance in a Serverless Environment

Performance Evaluation Of Zabbix And Azure Monitor In Hybrid It Infrastructure, Ivan Godoy, Cormac Keogh

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Performance Evaluation of Zabbix and Azure Monitor in Hybrid IT Infrastructure

Ai-Based Predictive Analytics For Network Operations, Timur Nikisin, David White

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AI-Based Predictive Analytics for Network Operations