arXiv subjects

Hai Duc Nguyen

Publications and source records attributed to Hai Duc Nguyen.

11 recordsLinked to original sources

arXiv2026

Avatar: Toward Autonomous End-to-End Orchestration of Scientific Workflows using LLMs

Scientific workflow management (WMSs) systems automate execution, yet orchestrate using fixed, hand-tuned rules. LLM agents promise more autonomous orchestration, but it remains unclear where to introduce agentic reasoning, how to bound its risk, and when it actually helps. We present Avatar, an actor-based architecture comprising an orchestrator, an executor, and a provenance monitor. Each actor's decision policy is pluggable (rule-based or LLM-backed) via a single adapter-validated action catalog, so conventional and agentic control run on the same core across different WMSs. We present an implementation using the Academy framework and evaluate Avatar across three workloads. We observe that Avatar's rule mode reproduces native execution, with a single unchanged core running all three. Moreover, LLM-backed Avatar reports a reduction of compute wastage by $55\%$ and cuts GPU-busy time by $40\%$. Overall, we envision Avatar as a step toward workflow systems that reason about their own orchestration rather than follow pre-fixed rules.

cs.DC↗

arXiv2026

Empirical Analysis of Cloud-Edge Infrastructure Complexity: Practitioner Pain Points and Architectural Directions

The proliferation of cloud, edge, and Internet of Things (IoT) computing has created unprecedented opportunities for distributed applications. However, this architectural shift introduces profound infrastructural complexity, acting as a significant barrier to developer productivity and innovation. In this paper, we present an empirical analysis based on 101 semi-structured interviews across 86 organizations to investigate the state of cloud-native development practices, pain points, and expectations. Our findings quantitatively validate that deployment complexity (38.6%) and onboarding difficulty (35.6%) are the dominant operational bottlenecks, while developers heavily prioritize productivity (53.5%) and automation (44.6%) over raw performance optimization. Based on these empirical insights, we examine four architectural directions that address the validated pain points: unified object abstractions (Object-as-a-Service), platform engineering via Internal Developer Platforms, declarative AI/ML serving pipelines, and lightweight edge runtimes based on WebAssembly. Furthermore, we detail the multi-stakeholder ecosystem required for adopting novel infrastructure paradigms, emphasizing that security, operational integration, and strict multi-tenant isolation are prerequisites for production readiness. Our results demonstrate that the primary barrier to distributed computing adoption is not execution performance but infrastructural complexity, and that declaratively governed, higher-level abstractions across multiple paradigms offer viable architectural paths toward alleviating it.

cs.DC↗

arXiv2026

StreamGuard: Low-Overhead Resilience for Real-time HPC Data Streams

Real-time scientific workflows operate on continuous data streams and must produce timely, high-quality results despite executing on complex, failure-prone infrastructure. Hardware faults, network disruptions, and performance anomalies caused by resource contention or system heterogeneity can severely degrade performance and violate real-time constraints. We focus on strengthening the resilience of the producer-consumer streaming pattern, a fundamental building block of scientific streaming workflows. We present two complementary techniques: (i) a dynamic, asynchronous, non-blocking checkpointing mechanism that preserves progress without interrupting computation, and (ii) a progress-aware load redistribution strategy that detects slow workers and proactively rebalances tasks. Together, these mechanisms maintain forward progress and balanced execution even in highly error-prone environments. Experimental results show that our approach reduces the impact of failures and performance anomalies by up to 6x, while introducing less than 1% overhead in failure-free execution.

cs.DC↗

arXiv2026

Beyond Fixed Budgets: Characterizing the Inelasticity and Limitations of Tree-of-Thought Reasoning Strategies

Tree of Thought (ToT) search has become a promising direction for improving the reasoning capabilities of large language models, but deploying these methods in practice raises a question that has received little systematic attention: how do different search strategies behave under varying compute budgets, model sizes, and problem difficulties? In this work, we evaluate two representative ToT methods; DPTS, a Monte Carlo tree search based approach, and SSDP, a semantic deduplication based approach, across two mathematical reasoning benchmarks (Math500 and GSM8K), two model scales (Llama-3B and Llama-8B), and four token budgets (3k--10k). Our analysis reveals that the two methods exhibit limitations that pull in opposite directions. DPTS suffers from a cold-start bottleneck at low budgets: it requires sufficient exploration before its value estimates become reliable, making it a poor fit for resource-constrained settings despite strong scaling behavior at higher budgets. SSDP, on the other hand, reaches candidate solutions efficiently but is prone to frontier depletion; its aggressive node merging permanently discards unexplored paths, leaving it unable to improve regardless of how much budget remains. Together, these findings suggest that neither a fixed exploration strategy nor a fixed pruning strategy is sufficient across compute continuum. We argue that effective search for scientific reasoning agents requires strategies that can adapt their behavior based on search progress and available resources.

cs.AI↗

arXiv2026

A Periodic Space of Distributed Computing: Vision & Framework

Advances in networking and computing technologies throughout the early decades of the 21st century have transformed long-standing dreams of pervasive communication and computation into reality. These technologies now form a rapidly evolving and increasingly complex global infrastructure that will underpin the next aspiration of computing: supporting intelligent systems with human-level or even superhuman capabilities. We examine how today's distributed computing landscape can evolve to meet the demands of future users, intelligent systems, and emerging application domains. We propose a "periodic framework" for characterizing the distributed computing landscape, inspired by the systematic structure and explanatory power of the "periodic table" in chemistry. This framework provides a structured way to describe, compare, and reason about the behaviors and design choices of different distributed computing solutions. Using this framework, we can identify patterns in key system properties, such as responsiveness and availability, across the distributed computing landscape. We also explain how the framework can help in predicting future trajectories in the field. Lastly, we synthesize insights from leading researchers worldwide regarding the desired properties, design principles, and implications of emerging areas in the forthcoming distributed computing landscape and in relation to the periodic framework. Together, these perspectives shed light on the considerations that will shape the distributed computing landscape underpinning future intelligent systems.

cs.DC↗

arXiv2026

Benchmarking Message Brokers for IoT Edge Computing: A Comprehensive Performance Study

Asynchronous messaging is a cornerstone of modern distributed systems, enabling decoupled communication for scalable and resilient applications. Today's message queue (MQ) ecosystem spans a wide range of designs, from high-throughput streaming platforms to lightweight protocols tailored for edge and IoT environments. Despite this diversity, choosing an appropriate MQ system remains difficult. Existing evaluations largely focus on throughput and latency on fixed hardware, while overlooking CPU and memory footprint and the effects of resource constraints, factors that are critical for edge and IoT deployments. In this paper, we present a systematic performance study of eight prominent message brokers: Mosquitto, EMQX, HiveMQ, RabbitMQ, ActiveMQ Artemis, NATS Server, Redis (Pub/Sub), and Zenoh Router. We introduce mq-bench, a unified benchmarking framework to evaluate these systems under identical conditions, scaling up to 10,000 concurrent client pairs across three VM configurations representative of edge hardware. This study reveals several interesting and sometimes counter-intuitive insights. Lightweight native brokers achieve sub-millisecond latency, while feature-rich enterprise platforms incur 2-3X higher overhead. Under high connection loads, multi-threaded brokers like NATS and Zenoh scale efficiently, whereas the widely-deployed Mosquitto saturates earlier due to its single-threaded architecture. We also find that Java-based brokers consume significantly more memory than native implementations, which has important implications for memory-constrained edge deployments. Based on these findings, we provide practical deployment guidelines that map workload requirements and resource constraints to appropriate broker choices for telemetry, streaming analytics, and IoT use cases.

cs.DC↗

arXiv2026

Exploring Challenges in Developing Edge-Cloud-Native Applications Across Multiple Business Domains

As the convergence of cloud computing and advanced networking continues to reshape modern software development, edge-cloud-native paradigms have become essential for enabling scalable, resilient, and agile digital services that depend on high-performance, low-latency, and reliable communication. This study investigates the practical challenges of developing, deploying, and maintaining edge-cloud-native applications through in-depth interviews with professionals from diverse domains, including IT, finance, healthcare, education, and industry. Despite significant advancements in cloud technologies, practitioners, particularly those from non-technical backgrounds-continue to encounter substantial complexity stemming from fragmented toolchains, steep learning curves, and operational overhead of managing distributed networking and computing, ensuring consistent performance across hybrid environments, and navigating steep learning curves at the cloud-network boundary. Across sectors, participants consistently prioritized productivity, Quality of Service, and usability over conventional concerns such as cost or migration. These findings highlight the need for operationally simplified, SLA-aware, and developer-friendly platforms that streamline the full application lifecycle. This study contributes a practice-informed perspective to support the alignment of edge-cloud-native systems with the realities and needs of modern enterprises, offering critical insights for the advancement of seamless cloud-network convergence.

cs.DC↗

arXiv2026

EdgeWeaver: Accelerating IoT Application Development Across Edge-Cloud Continuum

The rise of complex, latency-sensitive IoT applications across the Edge-Cloud continuum exposes the limitations of current Function-as-a-Service (FaaS) platforms in seamlessly addressing the complexity, heterogeneity, and intermittent connectivity of Edge-Cloud environments. Developers are left to manage integration and Quality of Service (QoS) enforcement manually, rendering application development complicated and costly. To overcome these limitations, we introduce the EdgeWeaver platform that offers a unified "object" abstraction that is seamlessly distributed across the continuum to encapsulate application logic, state, and QoS. EdgeWeaver automates "class" deployment across edge and cloud by composing established distributed algorithms (e.g., Raft, CRDTs)-enabling developers to declaratively express QoS (e.g., availability and consistency) desires that, in turn, guide internal resource allocation, function placement, and runtime adaptation to fulfill them. We implement a prototype of EdgeWeaver and evaluate it under diverse settings and using human subjects. Results show that EdgeWeaver boosts development productivity by 31%, while declaratively enforcing strong consistency and achieving 9 nines availability, 10,000X higher than the current standard, with negligible performance impact.

cs.DC↗

arXiv2025

RADAR-Radio Afterglow Detection and AI-driven Response: A Federated Framework for Gravitational Wave Event Follow-Up

The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various post-merger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential -- especially as the next generation of GW detectors promise a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.

astro-ph.HE↗

arXiv2025

D-Rex: Heterogeneity-Aware Reliability Framework and Adaptive Algorithms for Distributed Storage

The exponential growth of data necessitates distributed storage models, such as peer-to-peer systems and data federations. While distributed storage can reduce costs and increase reliability, the heterogeneity in storage capacity, I/O performance, and failure rates of storage resources makes their efficient use a challenge. Further, node failures are common and can lead to data unavailability and even data loss. Erasure coding is a common resiliency strategy implemented in storage systems to mitigate failures by striping data across storage locations. However, erasure coding is computationally expensive and existing systems do not consider the heterogeneous resources and their varied capacity and performance when placing data chunks. We tackle the challenges of using erasure coding with distributed and heterogeneous nodes, aiming to store as much data as possible, minimize encoding and decoding time, and meeting user-defined reliability requirements for each data item. We propose two new dynamic scheduling algorithms, D-Rex LB and D-Rex SC, that adaptively choose erasure coding parameters and map chunks to heterogeneous nodes. D-Rex SC achieves robust performance for both storage utilization and throughput, at a higher computational cost, while D-Rex LB is faster but with slightly less competitive performance. In addition, we propose two greedy algorithms, GreedyMinStorage and GreedyLeastUsed, that optimize for storage utilization and load balancing, respectively. Our experimental evaluation shows that our dynamic schedulers store, on average, 45% more data items without significantly degrading I/O throughput compared to state-of-the-art algorithms, while GreedyLeastUsed is able to store 21% more data items while also increasing throughput.

cs.DC↗

arXiv2024

Streamlining Cloud-Native Application Development and Deployment with Robust Encapsulation

Current Serverless abstractions (e.g., FaaS) poorly support non-functional requirements (e.g., QoS and constraints), are provider-dependent, and are incompatible with other cloud abstractions (e.g., databases). As a result, application developers have to undergo numerous rounds of development and manual deployment refinements to finally achieve their desired quality and efficiency. In this paper, we present Object-as-a-Service (OaaS) -- a novel serverless paradigm that borrows the object-oriented programming concepts to encapsulate business logic, data, and non-functional requirements into a single deployment package, thereby streamlining provider-agnostic cloud-native application development. We also propose a declarative interface for the non-functional requirements of applications that relieves developers from daunting refinements to meet their desired QoS and deployment constraint targets. We realized the OaaS paradigm through a platform called Oparaca and evaluated it against various real-world applications and scenarios. The evaluation results demonstrate that Oparaca can enhance application performance by 60X and improve reliability by 50X through latency, throughput, and availability enforcement -- all with remarkably less development and deployment time and effort.

cs.DC↗