Temperature and deadline aware runtime resource management with workload prediction for heterogeneous multi-core platforms
Year: 2025
Type: article
Abstract: Contemporary embedded platforms necessitate advanced resource management techniques to effectively utilize their diverse computational resources . Usually these platforms encounter fluctuations in workloads, making workload prediction have the potential to enhance resource management efficiency. In addition, in modern multi-core systems, there is a discernible tendency for processing cores to decrease in size relative to power consumption . This reduction in core size contributes to higher power density within the chips, leading to elevated chip temperatures. Therefore, addressing the temperature issue becomes critical. This paper introduces a prediction-based and temperature-aware resource management heuristic designed to meet task deadlines, while simultaneously considering energy minimization . When evaluated on real-life workload traces, the proposed method achieves a 5.5% increase in acceptance rate with one-step-ahead prediction and an 8.9% increase with four-steps-ahead prediction in a temperature-aware context, compared to scenarios without prediction.
Source: Journal of Systems Architecture
Authors: Mina Niknafs, Petru Eles, Zebo Peng
Institution: Linköping University
Language: English
Topic: Real-Time Systems Scheduling
Subfield: Hardware and Architecture
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $2,540
OpenAlex ID: W4411593437