ArticleScientific reports2025
Energy efficient task scheduling for heterogeneous multicore processors in edge computing.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Topology-aware adaptive scheduling algorithm for heterogeneous AI-PC collaborative computing environments.Scientific reports · 2026Article
- Adaptive and intelligent customized deep Q-network for energy-efficient task offloading in mobile edge computing environments.Scientific reports · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Edge computing faces challenges in energy-efficient task scheduling for heterogeneous multicore processors (HMPs). Existing solutions focus on reactive workload adaptation and energy prediction but fail to effectively integrate dynamic voltage and frequency scaling (DVFS). This paper proposes a novel algorithm integrating task prioritization, core-aware mapping, and predictive DVFS. Our approach outperforms state-of-the-art methods, reducing energy consumption by 20.9% while maintaining a low 2.4% deadline miss rate. Experiments on real HMP platforms demonstrate the algorithm's scalability and adaptability to varying workloads. This work advances energy-efficient edge computing, balancing performance and power constraints.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.