Name
Abstract | ||
|---|---|---|
With the growth of computation-intensive real-time applications on multi-core embedded systems, energy-efficient real-time scheduling becomes crucial. Multi-core processors enable intra-task parallelism, and there has been much progress on exploiting that, while there has been only a little progress on energy-efficient multi-core real-time scheduling as yet. In this work, we study energy-efficient real-time scheduling of constrained deadline sporadic parallel tasks, where each task is represented as a directed acyclic graph (DAG). We consider a clustered multi-core platform where processors within the same cluster run at the same speed at any given time. A new concept named speed-profile is proposed to model per-task and per-cluster energy-consumption variations during run-time to minimize the expected long-term energy consumption. To our knowledge, no existing work considers energy-aware real-time scheduling of DAG tasks with constrained deadlines, nor on a clustered multi-core platform. The proposed energy-aware realtime scheduler is implemented upon an ODROID XU-3 board to evaluate and demonstrate its feasibility and practicality. To complement our system experiments in large-scale, we have also conducted simulations that demonstrate a CPU energy saving of up to 57% through our proposed approach compared to existing methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/RTAS.2019.00021 | 2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS) |
Keywords | Field | DocType |
Parallel task, Real-time scheduling, Energy minimization, Cluster-based platform | Scheduling (computing),Efficient energy use,Computer science,Directed acyclic graph,Energy consumption,Multi-core processor,Energy minimization,Distributed computing | Conference |
ISSN | ISBN | Citations |
1545-3421 | 978-1-7281-0679-3 | 1 |
PageRank | References | Authors |
0.35 | 29 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhishan Guo | 1 | 329 | 34.04 |
| Ashikahmed Bhuiyan | 2 | 20 | 2.99 |
| Di Liu | 3 | 1 | 0.68 |
| Aamir Khan | 4 | 1 | 0.35 |
| Abusayeed Saifullah | 5 | 721 | 32.31 |
| Nan Guan | 6 | 95 | 21.53 |