An Integrated Approach for Applying Dynamic Voltage Scaling to Hard Real-Time Systems
RTAS '03 Proceedings of the The 9th IEEE Real-Time and Embedded Technology and Applications Symposium
Maximizing rewards for real-time applications with energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
Power-aware QoS Management in Web Servers
RTSS '03 Proceedings of the 24th IEEE International Real-Time Systems Symposium
Profit-driven uniprocessor scheduling with energy and timing constraints
Proceedings of the 2004 ACM symposium on Applied computing
Procrastination scheduling in fixed priority real-time systems
Proceedings of the 2004 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Dynamic voltage scaling for systemwide energy minimization in real-time embedded systems
Proceedings of the 2004 international symposium on Low power electronics and design
Maximizing the system value while satisfying time and energy constraints
IBM Journal of Research and Development
Optimal procrastinating voltage scheduling for hard real-time systems
Proceedings of the 42nd annual Design Automation Conference
Power reduction by varying sampling rate
ISLPED '05 Proceedings of the 2005 international symposium on Low power electronics and design
Procrastination for leakage-aware rate-monotonic scheduling on a dynamic voltage scaling processor
Proceedings of the 2006 ACM SIGPLAN/SIGBED conference on Language, compilers, and tool support for embedded systems
GRACE-1: Cross-Layer Adaptation for Multimedia Quality and Battery Energy
IEEE Transactions on Mobile Computing
Optimized Slowdown in Real-Time Task Systems
IEEE Transactions on Computers
Multi-version scheduling in rechargeable energy-aware real-time systems
Journal of Embedded Computing - Real-Time Systems (Euromicro RTS-03)
A design framework for real-time embedded systems with code size and energy constraints
ACM Transactions on Embedded Computing Systems (TECS)
Optimal service level allocation in environmentally powered embedded systems
Proceedings of the 2009 ACM symposium on Applied Computing
Power management in energy harvesting embedded systems with discrete service levels
Proceedings of the 14th ACM/IEEE international symposium on Low power electronics and design
An energy management framework for energy harvesting embedded systems
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Dynamic power management in environmentally powered systems
Proceedings of the 2010 Asia and South Pacific Design Automation Conference
Maximum utility rate allocation for energy harvesting wireless sensor networks
Proceedings of the 14th ACM international conference on Modeling, analysis and simulation of wireless and mobile systems
Energy efficient scheduling of parallel tasks on multiprocessor computers
The Journal of Supercomputing
The Journal of Supercomputing
An efficient energy and schedule length model for multiprocessor computers
International Journal of Computer Applications in Technology
An optimal energy and power model for dynamic voltage scaled multiprocessor systems
International Journal of Business Information Systems
International Journal of Business Information Systems
| Hi-index | 0.00 |
Typical real-time scheduling theory has addressed deadline and energy constraints as well as deadline and reward constraints simultaneously in the past. However, we believe that embedded devices with varying applications typically have three constraints that need to be addressed: energy, deadline, and reward. These constraints play important roles in the next generation of embedded devices, since they provide users with a variety of QoS-aware trade-offs. An optimal scheme would allow the device to run the most critical and valuable applications, without depleting the energy source while still meeting all deadlines. In this paper we propose a solution to this problem for typical control systems, such as frame-based task sets. We devise two algorithms that closely approximate the optimal solution while taking onlya fraction of the runtime of an optimal solution.