Battery-aware static scheduling for distributed real-time embedded systems
Proceedings of the 38th annual Design Automation Conference
Real-time dynamic voltage scaling for low-power embedded operating systems
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Battery-conscious task sequencing for portable devices including voltage/clock scaling
Proceedings of the 39th annual Design Automation Conference
Battery Life Estimation of Mobile Embedded Systems
VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Energy management for battery-powered embedded systems
ACM Transactions on Embedded Computing Systems (TECS)
IEEE Transactions on Parallel and Distributed Systems
Battery Model for Embedded Systems
VLSID '05 Proceedings of the 18th International Conference on VLSI Design held jointly with 4th International Conference on Embedded Systems Design
Shortest-path algorithms for real-time scheduling of FIFO tasks with minimal energy use
ACM Transactions on Embedded Computing Systems (TECS)
An efficient dynamic task scheduling algorithm for battery powered DVS systems
Proceedings of the 2005 Asia and South Pacific Design Automation Conference
IntellBatt: towards smarter battery design
Proceedings of the 45th annual Design Automation Conference
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Battery lifetime, a primary design constraint for mobile embedded systems, has been shown to depend heavily on the load current profile. This paper explores how scheduling guidelines from battery models can help in extending battery capacity. It then presents a 'Battery-Aware Scheduling' methodology for periodically arriving taskgraphs with real time deadlines and precedence constraints. Scheduling of even a single taskgraph while minimizing the weighted sum of a cost function has been shown to be NP-Hard [6]. The presented methodology divides the problem in to two steps. First, a good DVS algorithms dynamically determines the minimum frequency of execution. Then, a greedy algorithm allows a near optimal priority function [5] to choose the task which would maximize slack recovery. The methodology also ensures adherence of real time deadlines independent of the choice of the DVS algorithm and priority function used, while following battery guidelines to maximize battery lifetime. Battery simulations carried out on the profile generated by our methodology for a large set of taskgraphs show that battery life time is extended up to 23.3% as compared to existing dynamic scheduling schemes.