arXiv subjects

Óscar González de Dios

Publications and source records attributed to Óscar González de Dios.

6 recordsLinked to original sources

arXiv2026

Optimizing power by selective IP card shutdown using transport slicing

The increasing energy demands of upcoming sixth-generation (6G) mobile networks and networks supporting AI applications pose significant challenges for network operators in terms of operational costs and environmental impact. To address these challenges, this paper proposes a novel IP-based network slicing strategy that optimizes energy efficiency through a dual-slice approach. The proposed solution consists of a Day Slice, designed to meet high-performance requirements during peak traffic hours, and a Night Slice, optimized for energy savings by deactivating excess line-cards in card-based routers during periods of low traffic demand. The traffic is switched between the Day and Night Slices at predefined times, assuming appropriate traffic engineering mechanisms are in place to minimize disruption and support session continuity. We apply Pareto-based evolutionary algorithms (NSGA-II, CTAEA, and AGE-MOEA) to jointly optimize energy consumption and latency. Experiments conducted on the SNDlib india35 topology demonstrate that multi-objective optimization can deactivate over 40% of line cards during low-traffic periods, providing significant energy savings while maintaining acceptable performance. Additionally, a multi-service extension using AGE-MOEA introduces differentiated QoS constraints, maintaining latency below 7 ms for premium traffic while preserving substantial energy savings.

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arXiv2025

Introducing Large Language Models as the Next Challenging Internet Traffic Source

This article explores the growing impact of large language models (LLMs) and Generative AI (GenAI) tools on Internet traffic, focusing on their role as a new and significant source of network load. As these AI tools continue to gain importance in applications ranging from virtual assistants to content generation, the volume of traffic they generate is expected to increase massively. These models use the Internet as the global infrastructure for delivering multimedia messages (text, voice, images, video, etc.) to users, by interconnecting users and devices with AI agents typically deployed in the cloud. We believe this represents a new paradigm that will lead to a considerable increase in network traffic, and network operators must be prepared to address the resulting demands. To support this claim, we provide a proof-of-concept and source code for measuring traffic in remote user-agent interactions, estimating the traffic generated per prompt for some of the most popular open-source LLMs in 2025. The average size of each prompt query and response is 7,593 bytes, with a standard deviation of 369 bytes. These numbers are comparable with email and web browsing traffic. However, we envision AI as the next "killer application" that will saturate networks with traffic, such as Peer-to-Peer traffic and Video-on-demand dominated in previous decades.

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arXiv2024

A Queuing Envelope Model for Estimating Latency Guarantees in Deterministic Networking Scenarios

Accurate estimation of queuing delays is crucial for designing and optimizing communication networks, particularly in the context of Deterministic Networking (DetNet) scenarios. This study investigates the approximation of Internet queuing delays using an M/M/1 envelope model, which provides a simple methodology to find tight upper bounds of real delay percentiles. Real traffic statistics collected at large Internet Exchange Points (like Amsterdam and San Francisco) have been used to fit polynomial regression models for transforming packet queuing delays into the M/M/1 envelope models. We finally propose a methodology for providing delay percentiles in DetNet scenarios where tight latency guarantees need to be assured.

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arXiv2024

Count-Min sketches for Telemetry: analysis of performance in P4 implementations

Monitoring streams of packets at 100~Gb/s and beyond requires using compact and efficient hashing-techniques like HyperLogLog (HLL) or Count-Min Sketch (CMS). In this work, we evaluate the uses and applications of Count-Min Sketch for Metro Networks employing P4-based packet-optical nodes. We provide dimensioning rules for CMS at 100~Gb/s and 400~Gb/s and evaluate its performance in a real implementation testbed.

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arXiv2024

On optimizing Inband Telemetry systems for accurate latency-based service deployments

The power of Machine Learning and Artificial Intelligence algorithms based on collected datasets, along with the programmability and flexibility provided by Software Defined Networking can provide the building blocks for constructing the so-called Zero-Touch Network and Service Management systems. However, the fuel towards this goal relies on the availability of sufficient and good-quality data collected from measurements and telemetry. This article provides a telemetry methodology to collect accurate latency measurements, as a first step toward building intelligent control planes that make correct decisions based on precise information.

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arXiv2024

Reinforcement-Learning based routing for packet-optical networks with hybrid telemetry

This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error rate and propagation delay) and the link layer (link load) to configure a set of latency-based rewards and penalties based on such measurements. Then, the algorithm executes Q-learning based on this set of rewards for finding the optimal routing strategies. It is further shown that the algorithm dynamically adapts to changing network conditions by re-calculating optimal policies upon either link load changes or link degradation as measured by pre-FEC BER.

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