Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Daniel Lezcano

dblp:57/7804 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
1since 2021 · last 2024
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1 · 1 first-author · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Embedded and real-time systems · 50% Energy-efficient computing · 50%
Software engineering, system software, and programming languages
1 paper
Operating systems · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Embedded and real-time systems
mobile computing
0.812024
O/S Level Interrupt Prediction for Performance and Energy Management on Android · IEEE Trans. Mob. Comput. 2024
Energy-efficient computing
power management
0.812024
O/S Level Interrupt Prediction for Performance and Energy Management on Android · IEEE Trans. Mob. Comput. 2024
Operating systems › kernel
linux kernel
0.212024
O/S Level Interrupt Prediction for Performance and Energy Management on Android · IEEE Trans. Mob. Comput. 2024

Methods — techniques the papers use, named apart from their topics

kernel-level performance evaluation · 1.5
YearPublicationVenuePosition
2024 O/S Level Interrupt Prediction for Performance and Energy Management on Android
abstract
Billions smartphones and smart objects battery-powered use Android, i.e. on the Linux kernel. To save energy, the main kernel leverage is to put processors in a low power state as soon as they are idle. It predicts the next event to estimate the sleep duration and choose a sleep state accordingly. Several wake-up sources (interrupts, events...) impact this prediction which is usually done considering them as a single source. The resulting signal is nearly random and difficult to predict. Processors are recently supporting deeper idle states but the prediction paradigm was never challenged. We propose to predict the next event by splitting the wake-up source signal into simpler event patterns. We describe a fast and efficient algorithm and its kernel-level performance evaluation. We compare our approach with multiple reference sleep state selection algorithms on actual ARM and x86 boards using classical mobile workloads. Our proposal detects correctly (up to 20% improved correctness leading to 5% reduced energy consumption) the time of next interrupt, and thus the right sleep level for the processor. We show and discuss the energy impact of the tested prediction algorithm and we compare it with the different generations of sleep level managers in the Linux kernel.
Daniel Lezcano, Georges Da Costa
IEEE Trans. Mob. Comput.1