Scheduling Periodic Jobs that Allow Imprecise Results
IEEE Transactions on Computers
Optimal Reward-Based Scheduling for Periodic Real-Time Tasks
IEEE Transactions on Computers
Intra-Task Voltage Scheduling for Low-Energy, Hard Real-Time Applications
IEEE Design & Test
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Maximizing rewards for real-time applications with energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
ICCAD '05 Proceedings of the 2005 IEEE/ACM International conference on Computer-aided design
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
Leakage-aware dynamic scheduling for real-time adaptive applications on multiprocessor systems
Proceedings of the 47th Design Automation Conference
Identifying the optimal energy-efficient operating points of parallel workloads
Proceedings of the International Conference on Computer-Aided Design
Optimizing quality of service in real-time systems under energy constraints
ACM SIGOPS Operating Systems Review
Rolling-horizon scheduling for energy constrained distributed real-time embedded systems
Journal of Systems and Software
Deadline and energy constrained dynamic resource allocation in a heterogeneous computing environment
The Journal of Supercomputing
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In designing energy-aware CPU scheduling algorithms for real-time embedded systems, dynamic slack reclamation techniques significantly improve system Quality-of-Service (QoS) and energy efficiency. However, the limited schemes in this domain either demand high complexity or can only achieve limited QoS. In this paper, we present a novel low complexity runtime scheduling algorithm for the Imprecise Computation (IC) modeled tasks. The target is to maximize system QoS under energy constraints. Our proposed algorithm, named Gradient Curve Shifting (GCS), is able to decide the best allocation of slack cycles arising at runtime, with very low complexity. We study both linear and concave QoS functions associated with IC modelde tasks, on non-DVS and DVS processors. Furthermore, we apply the intra-task DVS technique to tasks and achieve as large as 18% more of the system QoS compared to the conventional "optimal" solution which is inter-task DVS based.