FR-EAHTS: federated reinforcement learning for enhanced task scheduling with hierarchical load balancing and dynamic power adjustment in multi-core systems

Mohd Farooq (), Aasim Zafar () and Abdus Samad ()
Additional contact information
Mohd Farooq: Aligarh Muslim University
Aasim Zafar: Aligarh Muslim University
Abdus Samad: Aligarh Muslim University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 2, No 8, 23 pages

Abstract: Abstract Scheduling tasks in multi-core systems is essential for achieving high performance and energy efficiency in data centers and portable devices. Traditional scheduling methods struggle with challenges such as communication overhead, varying core activity levels, and dynamic environments, leading to inefficiencies in processing and energy consumption. To address these issues, this work introduces the Federated Reinforced Energy-Aware Heterogeneous Task Scheduling (FR-EAHTS) method, that integrates transformer-based models with reinforcement learning in a federated environment. The key innovation lies in leveraging TransformX for enhanced state-action representation and Adaptive Proximal Policy Optimization (APPO) for efficient policy optimization, improving both adaptability and scheduling performance in heterogeneous multi-core systems. Real-time load distribution and task granularity adjustments are achieved through the integration of Dynamic Heuristic Load Distribution (DHLD) and Parameter Dynamic Adjustment (PDA), enabling adaptive scheduling decisions. Additionally, Dynamic Voltage and Frequency Scaling (DVFS) is incorporated to optimize power efficiency, ensuring improved energy-aware scheduling in heterogeneous multi-core systems. To further enhance stability and convergence in reinforcement learning, the proposed approach integrates a Dynamic Learning Rate (DLR) mechanism, which adjusts learning rates based on task execution variability. Unlike fixed learning rates, DLR ensures adaptive learning adjustments, preventing unstable updates and improving scheduling efficiency in rapidly changing environments. This approach effectively coordinates policy changes, adapts to core workload heterogeneity, and dynamically adjusts to system characteristics. Experiments demonstrate the proposed work’s efficiency using three key evaluation metrics: makespan, energy consumption, and overhead, achieving a 25% reduction in makespan task completion time, 30% energy savings, and 20% lower scheduling overhead, making it highly effective for multi-core systems, data centers, and mobile environments.

Keywords: Task scheduling; Multi-core systems; Machine learning; Load balancing; Federated learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11235-025-01276-0 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:88:y:2025:i:2:d:10.1007_s11235-025-01276-0

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-025-01276-0

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().