A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Synthesis Techniques for Low-Power Hard Real-Time Systems on Variable Voltage Processors
RTSS '98 Proceedings of the IEEE Real-Time Systems Symposium
Algorithmic problems in power management
ACM SIGACT News
An Efficient Algorithm for Computing Optimal Discrete Voltage Schedules
SIAM Journal on Computing
Speed scaling for weighted flow time
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Scheduling for Speed Bounded Processors
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Speed Scaling Functions for Flow Time Scheduling Based on Active Job Count
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Speed scaling with an arbitrary power function
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Energy-Efficient algorithms for flow time minimization
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Analysis of energy reduction on dynamic voltage scaling-enabled systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Min-energy scheduling for aligned jobs in accelerate model
Theoretical Computer Science
Speed scaling problems with memory/cache consideration
TAMC'12 Proceedings of the 9th Annual international conference on Theory and Applications of Models of Computation
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Dynamic voltage scaling technique provides the capability for processors to adjust the speed and control the energy consumption. We study the pessimistic accelerate model where the acceleration rate of the processor speed is at most K and jobs cannot be executed during the speed transition period. The objective is to find a min-energy (optimal) schedule that finishes every job within its deadline. The job set we study in this paper is aligned jobs where earlier released jobs have earlier deadlines. We start by investigating a special case where all jobs have common arrival time and design an O(n 2) algorithm to compute the optimal schedule based on some nice properties of the optimal schedule. Then, we study the general aligned jobs and obtain an O(n 2) algorithm to compute the optimal schedule by using the algorithm for the common arrival time case as a building block. Because our algorithm relies on the computation of the optimal schedule in the ideal model (K = 驴), in order to achieve O(n 2) complexity, we improve the complexity of computing the optimal schedule in the ideal model for aligned jobs from the currently best known O(n 2logn) to O(n 2).