
Daniel Wladdimiro is Assistant Professor of Computer Science at ESILV Engineering School at Paris la Défense. He did his PhD in Computer Science at Sorbonne University in 2024. His research focuses on distributed systems and data processing, specializing in the design of scalable system architectures. He specializes in identifying requirements, leveraging emerging web technologies, and employing advanced mathematical models to optimize system performance and throughput. His projects often address social and academic challenges, such as the development of platforms to support natural disaster response, which is mathematically modeled to optimize the allocation of computing resources.
Daniel Wladdimiro; Nicolás Hidalgo; Alessio Pagliari; Luciana Arantes; Pierre Sens; Erika Rosas; Victor Reyes
A Multi-Model Predictive Framework for Adaptive Resource Management in Stream Processing Systems Article de journal
Dans: Future Generation Computer Systems-The International Journal Of Escience, vol. 182, p. 108461, 2026.
@article{wladdimiro_4216,
title = {A Multi-Model Predictive Framework for Adaptive Resource Management in Stream Processing Systems},
author = {Daniel Wladdimiro and Nicolás Hidalgo and Alessio Pagliari and Luciana Arantes and Pierre Sens and Erika Rosas and Victor Reyes},
url = {https://doi.org/10.1016/j.future.2026.108461},
year = {2026},
date = {2026-09-01},
journal = {Future Generation Computer Systems-The International Journal Of Escience},
volume = {182},
pages = {108461},
abstract = {Stream Processing Systems (SPSs) are designed to process continuous streams of events, often under highly variable input rates. Although prior work has explored dynamic operator replication, many existing approaches lack generalizability and perform suboptimally across diverse scenarios. In this article, we present MMP-SPS, a predictive self-adaptive framework that extends extends our prior PA-SPS system by introducing a multi-window control loop, support for multiple prediction models, and online model selection via RMSE-based evaluation and multi-armed bandits. Targeting environments with high-volume and fluctuating data streams, such as social media analytics and network traffic monitoring, the framework dynamically selects the most suitable model based on real-time workload characteristics using a reinforcement learning (RL) strategy. To prove the validity of our system, we deployed an implementation of MMP-SPS based on Apache Storm, and we evaluated it on Google Cloud Platform against real-world datasets. Experimental results show substantial improvements in latency, throughput, and resource utilization compared to static and single-model baselines. These findings underscore the potential of multi-model predictive adaptation for scalable and robust stream processing under dynamic conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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