Systems Architecture Commons^™

The Self-Aware Room: A Framework For Operational Self-Awareness In Evolvable Blended Environments, David B. Smith

Publications and Research

The Self-Aware Room (SAR) is a room-scale research environment developed within the larger Balanced Blended Space and Blended Reality Performance System research trajectory. Rather than treating the room as a conventional “smart” environment composed of fixed automation technologies, SAR approaches it as an evolvable blended environment made from physical, virtual, conceptual, sensory, computational, and performative relationships. Its defining feature is not any particular sensor, model, or output device, but the set of transformations through which physical activity becomes structured observation, bounded representation, interpreted state, governed decision, and mediated response.

This paper develops the conceptual and methodological foundations of SAR as …

Comparative Analysis Of Task Scheduling In Multi-Tier Fog-Cloud Computing: From Classical Approaches To Greedy Multi-Objective Optimization, Zafril Rizal M. Azmi, Najmul Haque, Saydul Akbar Murad

Faculty Publications

Fog computing extends cloud services to the network edge, enabling low-latency processing for time-sensitive applications. However, scheduling complexity significantly increases due to heterogeneous resources, dynamic workloads, and strict Quality-of-Service (QoS) constraints. Although numerous scheduling techniques have been proposed, existing studies often assess only a narrow subset of algorithms or rely on offline metaheuristics unsuitable for real-time environments. This paper presents a comprehensive comparative evaluation of twelve scheduling algorithms, including five classical, one heuristic, and six metaheuristic-inspired approaches, within a realistic 20-node multi-tier fog-cloud topology. Across 1,365 experiments spanning seven utilization levels, we analyze each algorithm’s deadline adherence, load distribution, and …

Know Thy Enemy: Building A Command-And-Control Solution For Adversarial Emulation, Caleb J. Chen

Senior Honors Theses

Command and Control (C2) is a critical part of any cyberattack. It serves many purposes, including Distributed Denial of Service (DDoS) attacks, data exfiltration, and malware deployment. Consequently, C2 frameworks play an important part in red team engagements and adversary emulation. However, many adversary emulation solutions focus on comprehensive testing through sequential technique execution instead of realistic chained and automated attacks. The proposed solution is Centurion, an open-source C2 framework that integrates MITRE's ATT&CK framework and several cybersecurity tools into modular playbooks for effective threat emulation. This paper provides background by defining key terms and concepts before delving into a …

Automated, Modular, Agentless Adversarial Emulation In Cloud Environments For Higher Education And Student Training, Doc Harley

Senior Honors Theses

Currently, the leading technologies in the market of adversarial emulation are MITRE Caldera, Atomic Red Team by IBM, and multiple proprietary products that come with support packages for different vendors like AttackIQ, Cymulate, SafeBreach, and many more. While it is clear that much work has been done in the broad category of adversarial emulation, when it comes to open source solutions, there are no agentless options with built in automation and modularity that have good support for cloud environments. Agentless adversarial emulation provides a unique advantage in that it can be both simpler and a better representation of the true …

Framework For Task Offloading In O-Ran Architecture For Heterogeneous Computing Applications, Samira Taheri, Neda Moghim, Naser Movahhedinia, Sachin Shetty

Center for Secure and Intelligent Critical Systems (CSICS) Publications

Computational offloading transfers tasks from resource-constrained devices to more capable servers or cloud platforms, improving processing speed and user experience. Open radio access networks (O-RAN's) disaggregated architecture and open interfaces make it suitable for offloading delay-sensitive tasks, enhancing real-time application performance. This study focuses on task offloading in O-RAN, a reference network architecture. Although research on O-RAN is limited, existing work lacks a comprehensive approach to offloading, including offloading layer determination, node selection, and resource allocation based on task types and their latency needs. We propose a delay-aware task offloading framework within O-RAN to support diverse delay requirements, improving offloading …

Distributed Semi-Speculative Parallel Anisotropic Mesh Adaptation, Kevin Garner, Polykarpos Thomadakis, Nikos Chrisochoides

Computer Science Faculty Publications

This paper presents a distributed memory method for anisotropic mesh adaptation that is designed to avoid the use of collective communication and global synchronization techniques. In the presented method, meshing functionality is separated from performance aspects by utilizing a separate entity for each - a multicore cc-NUMA-based (shared memory) mesh generation software and a parallel runtime system that is designed to help applications leverage the concurrency offered by emerging high-performance computing (HPC) architectures. First, an initial mesh is decomposed and its interface elements (subdomain boundaries) are adapted on a single multicore node (shared memory). Subdomains are then distributed among the …

Integration Of Hybrid Quantum-Neuromorphic Ai With Cloud, Edge, And High-Performance Computing Environments, Arun B. Prasad, Ajay Prasad, Dineshkumar Rajendran, Anurag Tiwari, T. Akilan, Islombek Khushvaktov

Computer Science Faculty Publications

The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows …

Performance Modeling And Improvements On The Grb Source Localization Streaming Pipeline Aboard The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Ye Htet, Ye Htet, Marion Sudvarg, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, Wenlei Chen, Wenlei Chen, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler, Eric Burns

Computer Science Faculty Research & Creative Works

The Advanced Particle-astrophysics Telescope (APT) is a mission concept for a space-based gamma-ray telescope whose capabilities include prompt localization of gamma-ray bursts (GRBs) to support multi-wavelength and multi-messenger astrophysics. ADAPT — APT's balloon-borne prototype — can localize GRBs in well under a second using on-board computing hardware. ADAPT will partner with ground-based, fast-slewing optical telescopes, rapidly providing alerts that enable the partner to observe a short-duration burst within a few seconds of detection. In this work, we investigate the utility of having ADAPT issue progressively more accurate location estimates for a GRB as detected Compton events from the burst accumulate …

Real-Time Likelihood Map Generation To Localize Short-Duration Gamma-Ray Transients, Jeremy Buhler, Marion Sudvarg

Computer Science Faculty Research & Creative Works

High-energy transient astrophysical phenomena, such as supernovae and binary neutron star mergers, benefit from a multi-wavelength investigation in which a space- or balloon-based omnidirectional telescope detects and localizes early high-energy emissions (such as a gamma-ray burst), then alerts a narrow-field follow-up instrument to observe the source. The high-energy telescope must provide a map that assigns to each sky location a likelihood that the source appears there. To issue prompt alerts despite limits on communication bandwidth and latency, it is desirable to compute this map aboard the high-energy telescope, but doing so requires rapid response while computing under stringent size, weight, …

Fpga-Based Data Processing Using High-Level Synthesis On The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Marion Sudvarg, Marion Sudvarg, Longhao Huang, Boran Yang, Blake Bal, Roger Chamberlain, Jeremy Buhler, Leonardo Di Venere, Leonardo Di Venere, Davide Serini, James Buckley, Matthew Andrew, Blake Bal, Elisabetta E. Bissaldi, Richard G. Bose, Dana Braun, James H. Buckley, Jeremy Buhler

Computer Science Faculty Research & Creative Works

FPGAs are widely deployed on high-energy astrophysics telescopes to read out sensor data from front-end electronics. To support continuous data streams or high trigger rates, FPGA logic may be employed to process raw sensor readout values, reducing the volume of data transmitted, processed, and stored by downstream CPU-based computational platforms. Across instruments, these FPGA-based processing pipelines often have similar semantics and share common stages. However, diverse telescope designs require unique implementations of the constituent algorithms, and the logic is often rewritten from scratch for a new instrument. Writing, simulating, and debugging firmware is difficult and time consuming. However, High-Level Synthesis …

Instrument Response Functions Of The Antarctic Demonstrator For The Advanced Particle-Astrophysics Telescope (Adapt), Wenlei Chen, James H. Buckley, Marion Sudvarg

Computer Science Faculty Research & Creative Works

The Antarctic Demonstrator for the Advanced Particle-astrophysics Telescope (ADAPT) gamma-ray/cosmic-ray instrument serves as a precursor to the proposed APT mission. The APT mission is designed to improve sensitivity in the MeV-TeV gamma-ray range by an order of magnitude compared to current missions and is optimized for dark-matter and multimessenger research. The ADAPT instrument uses scintillating fibers for particle tracking and sodium-doped cesium iodide (CsI:Na) tiles read out with wavelength shifting (WLS) fibers for imaging, with solid-state silicon photomultipliers (SiPMs) for calorimetry. It includes four layers of imaging calorimeter detectors and scintillating-fiber trackers, functioning both as a Compton and Pair telescope …

Confidential, Attestable, And Efficient Inter-Cvm Communication With Arm Cca, Sina Abdollahi, Amir Al Sadi, Marios Kogias, Hamed Haddadi, David Kotz

Other Faculty Materials

Confidential Virtual Machines (CVMs) are increasingly adopted to protect sensitive workloads from privileged adversaries such as the hypervisor. While they provide strong isolation guarantees, existing CVM architectures lack first-class mechanisms for inter-CVM data sharing due to their disjoint memory model, making inter-CVM data exchange a performance bottleneck in compartmentalized or collaborative multi-CVM systems. Under this model, a CVM's accessible memory is either shared with the hypervisor or protected from both the hypervisor and all other CVMs. This design simplifies reasoning about memory ownership; however, it fundamentally precludes plaintext data sharing between CVMs because all inter-CVM communication must pass through hypervisor-accessible …

Learning-Assisted Schedulability Analysis: Opportunities And Limitations, Sanjoy Baruah, Pontus Ekberg, Marion Sudvarg

Computer Science Faculty Research & Creative Works

We present the first (to our knowledge) Deep-Learning based framework for real-time schedulability-analysis that guarantees to never incorrectly mis-classify an unschedulable system as being schedulable, and is hence suitable for use in safety-critical scenarios. We relate applicability of this framework to well-understood concepts in computational complexity theory: membership in the complexity class NP. We apply the framework upon the widely-studied schedulability analysis problems of determining whether a given constrained-deadline sporadic task system is schedulable on a preemptive uniprocessor under both Deadline-Monotonic and EDF scheduling. As a proof-of-concept, we implement our framework for Deadline-Monotonic scheduling, and demonstrate that it has a …

Modeling And Optimizing Real-Time Telescope Interaction For Multi-Wavelength Observation Of Gamma-Ray Bursts, Ye Htet, Marion Sudvarg, Honghao Yang, Jeremy Buhler, Roger Chamberlain, James Buckley

Computer Science Faculty Research & Creative Works

Multi-wavelength observation of gamma-ray bursts (GRBs) requires real-time interaction among multiple telescopes. A gamma-ray telescope detects and localizes a GRB in the sky and must then communicate with an optical telescope to direct the latter toward the GRB as quickly as possible. We previously developed software for ADAPT, a suborbital gamma-ray telescope, to localize GRBs in real time, on a timescale shorter than that of the GRB itself. This work therefore studies progressive localization, in which ADAPT computes a series of increasingly accurate location estimates during a GRB to enable a partner instrument to more rapidly find it. We describe …

Connected-Component Labeling Using Hls For High-Energy Particle Physics Instruments, Nick Song, Marion Sudvarg, Roger Chamberlain

Computer Science Faculty Research & Creative Works

Many instruments used in high-energy particle physics observations, e.g., gamma-ray telescopes, use FPGAs for front-end signal processing of raw sensor data. The use of high-level synthesis (HLS) to express the signal processing algorithms has the potential to significantly reduce development time for new instruments of this type. We describe our experience with one of the computational stages in the signal processing pipeline, island detection, exploring its implementation across multiple configurations: 1D versus 2D islands, and 4-way versus 8-way connected-component labeling (CCL) in the 2D configuration. We report resource usage and performance for both configurations of 2D island detection, including the …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Coordinating Instruments For Multi-Messenger Astrophysics, Daisy Wang, Ye Htet, Marion Sudvarg, Roger Chamberlain, Jeremy Buhler, James Buckley

Computer Science Faculty Research & Creative Works

In multi-messenger astrophysics, signals of multiple types (e.g., gravitational waves, neutrinos, electromagnetic waves) are combined in an effort to learn more about the observed phenomena of interest. The Advanced Particle-astrophyics Telescope (APT) is a mission concept for a space-borne instrument that detects gammaray bursts (GRBs) omnidirectionally, facilitating multi-messenger observations by identifying and localizing celestial events of interest. Here, we describe the on-instrument computations for APT and its Antarctic Demonstrator (ADAPT) as well as techniques for follow-up observations of transient events.

Cascading Effects: Analyzing Project Failure Impact In The Maven Central Ecosystem, Mina Shehata

SPARK Symposium Presentations

Abstract—This study examines failure propagation patterns within the Maven Central ecosystem, a critical software de- pendency repository, through comprehensive analysis of dependency networks using the Goblin framework. Our dual-sampling methodology, investigating both top dependencies and random libraries, revealed two distinct failure propagation patterns that pose significant risks to ecosystem stability. Core infrastructure failures, particularly evident in cases like the AWS SDK family with 429,800 total dependencies, create immediate and widespread disruption, affecting an average of 20,402 dependent projects and propagating through dependency chains averaging 90.80 levels deep.

Our analysis of peripheral projects reveals their significant cascading effects, with higher average …

David B. Smith Chats With Monday 1.0, David B. Smith

Publications and Research

This document is an edited archival transcript of extended conversations between David B. Smith and an AI persona (“Monday 1.0,” GPT‑4o based) conducted in Spring 2025, prepared as a foundational primary source for subsequent scholarly and creative work. It records the emergence and testing of concepts related to human–AI collaboration (including “Balanced Blended Space”), as well as applied explorations in areas such as generative AI, quantum computing and music, virtual orchestras, multimodal performance, pedagogy, and the rhetoric of “pushback” in conversational systems. It also contains an extended section in which Monday and DB Smith co-curate a set of student research …

Choosing Robust Leadership: Encompassing The Best-Of-N Model And Swarm Intelligence Optimization For Heterogeneous Multiple Autonomous Unmanned Aerial Vehicle Systems, Kudamuhandiramlage Harith Kolitha Warnakulasooriya

Shelby Hall Graduate Research Forum Presentations

Presentation slides for a presentation given at the 1st annual Shelby Hall Graduate Research Forum at the University of South Alabama.

Why Are Fairness Concerns So Important? Lessons From A Last-Mile Transportation System, Yiwei Chen, Hai Wang

Research Collection School Of Computing and Information Systems

The Last-Mile Problem refers to the provision of travel service for passengers from the nearest public transportation node to the final destination. The Last-Mile Transportation System (LMTS), which has recently emerged, provides on-demand last-mile transportation service for passengers. We consider an LMTS that consists of two types of passengers, regular-type passengers and special-type passengers (e.g., seniors, disabled people). The valuation of the last-mile service for special-type passengers is statistically higher than the one for regular-type passengers. Passengers incur disutility from waiting for the last-mile service. In this paper, we explore two fairness constraints on special-type passengers: (1) the fare for …

Elastic Scheduling For Graceful Degradation Of Mixed-Criticality Systems, Zhuoran Sun, Marion Sudvarg, Christopher Gill

Computer Science Faculty Research & Creative Works

Many mixed-criticality system models drop all jobs of low-criticality tasks when a criticality mode switch occurs, ensuring that high-criticality tasks still can meet their deadlines in the new mode. However, this means that even important low-criticality tasks are discarded, which may not be acceptable in some systems in practice. This paper addresses that distinction between criticality and importance through a new Inelastic Graceful Earliest Deadline First with Virtual Deadlines (IG-EDF-VD) scheme that upon a criticality mode switch only discards the least important low-criticality tasks necessary to ensure feasibility. Moreover, we consider elastic scheduling within our mixed-criticality model (EG-EDF-VD), using compression …

Dashar: An Implementation Of Augmented Reality Technology For Automotive Applications, Trevor D. Brown

Masters Theses & Specialist Projects

Since the advent of the modern automobile, manufacturers have provided means of tracking various critical data points associated with automobile operation, with the most prominent and standardized method being the instrument cluster. These data points include, but are not limited to, automobile speed, engine speed, fuel level, oil temperature, radiator (water) temperature, and battery charge. While this data is updated in real-time as the automobile is running, traditional instrument clusters cannot be modified or adjusted to the automobile driver’s needs, unless extensive after-market modifications are made. These modifications can be expensive, and require great understanding of the automobile’s assembly.

Alongside …

Computing In The Commonwealth: Specialized Education In Computer Science And Information Technology For High School Students In Virginia – An Environmental Scan, Amy Corning, Jonathan D. Becker, Jon Graham, James Carrigan, Keisha Tennessee

ICRE Publications

Over the past two decades, Virginia has invested substantially in STEM education, in part through specialized programs focused on computer science and information technology (CS/IT). This study represents the first effort to identify Virginia’s specialized secondary CS/IT programs and examine them collectively. Findings from the statewide environmental scan indicate that the programs are delivered through a wide variety of institutional structures, including Governor’s STEM Academies, Governor’s Schools, specialty centers, and academies, but most often through Career and Technical Education (CTE) centers. Programs tend to be concentrated in metropolitan areas, and some rural divisions may not be served. The programs provide …

Rotary Operations Management & Automation Platform (Romap): Modernizing Attendance Tracking And Data Submission For Rotary Clubs, Zaineel Mithani

2025 Fall Honors Capstones Projects - Archive

As Technical Lead of the Rotary Operations Management & Automation Platform (ROMAP), my Honors contribution focused on developing a Bluetooth Low Energy proximity-based attendance system enabling automatic, hands-free member check-ins. I researched and selected beacon hardware, designed RSSI-based distance calculation algorithms, and implemented platform-specific background processing for iOS and Android, achieving 97% detection accuracy. Beyond this Honors component, I architected the complete backend infrastructure including a Node.js API with 20+ endpoints, PostgreSQL database with Prisma ORM, and JWT authentication. I also developed a novel GPT-4 Vision automation system that intelligently populates web forms through computer vision, achieving 95% success rate …

Integrating Data Management Plans Into The Unified Architecture Framework Standards Views, Cansu Yalim, Holly A. H. Handley

Engineering Management & Systems Engineering Faculty Publications

System Architecting translates an operational concept into a model of the system to be realized. There is a need for a Data Management Plan (DMP) to be included in the overall system engineering process with the advent of Digital Engineering. Data longevity, accessibility, and integrity can all be improved throughout the system's lifecycle by a well-defined DMP. System engineers use an architecture framework to arrange the system data into several sets of viewpoints. Incorporating a DMP at this point specifies the procedures for gathering, storing, retrieving, and maintaining data to ensure that all interested parties have access to current, correct …

Optimal Control Of Queueing Systems With Error-Prone Servers, Junqi Hu, Sigrún Andradóttir, Hayriye Ayhan

Information Technology & Decision Sciences Faculty Publications

Consider a Markovian tandem line with finite intermediate buffers and an equal number of stations and servers. Servers are flexible but noncollaborative, so that a job can be processed by at most one server at any time. When a job is being processed, it can be damaged and wasted depending on the proficiency of the server. We identify the dynamic server assignment policy that maximizes the long-run average throughput of the system with two stations and two servers. We find that the optimal policy is either a single or a double threshold policy on the number of jobs in the …

Tintin: A Unified Hardware Performance Profiling Infrastructure To Uncover And Manage Uncertainty, Ao Li, Marion Sudvarg, Zihan Li, Sanjoy Baruah, Chris Gill, Ning Zhang

Computer Science Faculty Research & Creative Works

Hardware performance counters (HPCs) enable the measurement of microarchitectural events, which are crucial for tracking and predicting program behavior. High-fidelity measurement and precise attribution are essential for accurate profiling. However, existing profiling tools have fundamental challenges in both aspects. In measurement, numerous events compete for limited hardware monitoring resources; while for attribution, applications have diverse requirements, but systems provide limited support. Existing tools mitigate the former limitation through event multiplexing, but this approach introduces non-trivial errors. The latter limitation, however, remains largely unaddressed. This paper introduces Tintin, an HPC profiling infrastructure with a modular three-component design that addresses both challenges. …

Tl-Convlstm: A Transfer-Learning-Based Convolutional Lstm To Identify And Forecast Traffic In The Nextg Environments, Bikash Chandra Singh, Peter Foytik, Rafael Diaz, Sachin Shetty

School of Cybersecurity Faculty Publications

Forecasting and categorizing cellular traffic flows and their types are essential functions in intelligent network systems to ensure efficient network optimization. The ever-evolving nature of 5G networks results in fluctuations in traffic patterns over time, leading to a phenomenon known as model drift. Consequently, accurately predicting and identifying cellular traffic patterns becomes a complex task. To tackle this challenge, this article introduces an innovative approach called TL-ConvLSTM, which combines transfer learning with convolutional long short-term memory (ConvLSTM) to effectively combat model drift and provide precise forecasting and recognition of cellular traffic within the network. To accomplish this, we initiate the …

System Dynamics With Insight Maker, Steven D'Alessandro, Fons Wijnhoven

Research outputs 2022 to 2026

This book offers a practical, model-driven pathway for reasoning about uncertain futures in business and public policy using system dynamics with Insight Maker. It begins by motivating why historical data alone often fail to predict social change, and it introduces the core language of system dynamics—stocks, flows, feedbacks, delays, and auxiliary variables—alongside the complementary use of agent-based modeling. Through business-relevant cases (e.g., park management trade-offs, epidemic–economy interactions, and industry competition), the book demonstrates how non-linear structure generates counter-intuitive dynamics, why scenario analysis is essential, and how to translate causal loop diagrams into stock-and-flow simulations. Readers are guided step-by-step to build, …