2 citations · 7 across the 12 of their papers we have counts for
14 papers
FailBench: Evaluating Fault Tolerance Across Distributed Training Architectures
Khaled Aljbab, Amine Barrak
Distributed deep learning relies on data, pipeline, tensor, and hybrid parallelism, yet fault-tolerance mechanisms are typically evaluated only on the architecture for which they w…
ML in a Box: Analyzing Containerization Practices in Open Source ML Projects
Faten Jebari, Emna Ksontini, Amine Barrak +1
Containerization has become increasingly essential in the machine learning (ML) domain, providing reproducibility, portability, and environment consistency. While prior studies hav…
A Large-Scale Dataset of MCP Implementations on GitHub
Benny Toeppe, Amine Barrak, Emna Ksontini
The rapid emergence of the Model Context Protocol (MCP) has introduced a new standard for connecting large language models to external tools and services. Despite its rapid adoptio…
Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning
Amine Barrak
Federated learning (FL) aggregation on serverless platforms faces a hard scalability ceiling: existing architectures (lambda-FL, LIFL) partition clients across aggregators, but eve…
Traceability and Accountability in Role-Specialized Multi-Agent LLM Pipelines
Amine Barrak
Sequential multi-agent systems built with large language models (LLMs) can automate complex software tasks, but they are hard to trust because errors quietly pass from one stage to…
Cost-Performance Analysis: A Comparative Study of CPU-Based Serverless and GPU-Based Training Architectures
Amine Barrak, Fabio Petrillo, Fehmi Jaafar
The field of distributed machine learning (ML) faces increasing demands for scalable and cost-effective training solutions, particularly in the context of large, complex models. Se…
CARGO: A Framework for Confidence-Aware Routing of Large Language Models
Amine Barrak, Yosr Fourati, Michael Olchawa +2
As large language models (LLMs) proliferate in scale, specialization, and latency profiles, the challenge of routing user prompts to the most appropriate model has become increasin…
FaaSGuard: Secure CI/CD for Serverless Applications -- An OpenFaaS Case Study
Amine Barrak, Emna Ksontini, Ridouane Atike +1
Serverless computing significantly alters software development by abstracting infrastructure management and enabling rapid, modular, event-driven deployments. Despite its benefits,…
From REST to MCP: An Empirical Study of API Wrapping and Automated Server Generation for LLM Agents
Meriem Mastouri, Emna Ksontini, Amine Barrak +1
The Model Context Protocol (MCP) is emerging as a standard interface through which LLM agents invoke external tools, and a growing ecosystem of MCP servers now mediates access to v…
Scalable and Cost-Efficient ML Inference: Parallel Batch Processing with Serverless Functions
Amine Barrak, Emna Ksontini
As data-intensive applications grow, batch processing in limited-resource environments faces scalability and resource management challenges. Serverless computing offers a flexible…
SPIRT: A Fault-Tolerant and Reliable Peer-to-Peer Serverless ML Training Architecture
Amine Barrak, Mayssa Jaziri, Ranim Trabelsi +2
The advent of serverless computing has ushered in notable advancements in distributed machine learning, particularly within parameter server-based architectures. Yet, the integrati…
Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning
Amine Barrak, Ranim Trabelsi, Fehmi Jaafar +1
The increasing demand for computational power in big data and machine learning has driven the development of distributed training methodologies. Among these, peer-to-peer (P2P) net…
Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance
Amine Barrak, Fabio Petrillo, Fehmi Jaafar
Distributed Machine Learning refers to the practice of training a model on multiple computers or devices that can be called nodes. Additionally, serverless computing is a new parad…
Toward a traceable, explainable, and fairJD/Resume recommendation system
Amine Barrak, Bram Adams, Amal Zouaq
In the last few decades, companies are interested to adopt an online automated recruitment process in an international recruitment environment. The problem is that the recruitment…