Dover: An Optimal On-Line Scheduling Algorithm for Overloaded Uniprocessor Real-Time Systems
SIAM Journal on Computing
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Speed scaling to manage energy and temperature
Journal of the ACM (JACM)
Energy efficient online deadline scheduling
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Speed scaling for weighted flow time
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Energy-efficient algorithms for flow time minimization
ACM Transactions on Algorithms (TALG)
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
Improved Bounds for Speed Scaling in Devices Obeying the Cube-Root Rule
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Optimal speed scaling under arbitrary power functions
ACM SIGMETRICS Performance Evaluation Review
Communications of the ACM
How to schedule when you have to buy your energy
APPROX/RANDOM'10 Proceedings of the 13th international conference on Approximation, and 14 the International conference on Randomization, and combinatorial optimization: algorithms and techniques
Slow down and sleep for profit in online deadline scheduling
MedAlg'12 Proceedings of the First Mediterranean conference on Design and Analysis of Algorithms
Profitable scheduling on multiple speed-scalable processors
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
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We consider dynamic speed scaling on a single processor and study the tradeoff between throughput and energy for deadline scheduling. Specifically, we assume each job is associated with a user-defined value (or importance) and a deadline. We allow scheduling algorithms to discard some of the jobs (i.e., not finishing them) and the objective is to minimize the total energy usage plus the total value of jobs discarded. We give new online algorithms under both the unbounded-speed and bounded-speed models. When the maximum speed is unbounded, we give an O(1)-competitive algorithm. This algorithm relies on a key notion called the profitable speed, which is the maximum speed beyond which processing a job costs more energy than the value of the job. When the processor has a bounded maximum speed T, we show that no O(1)- competitive algorithm exists and more precisely, the competitive ratio grows with the penalty ratio of the input, which is defined as the ratio between the maximum profitable speed of a job to the maximum speed T. On the positive side, we give an algorithm with a competitive ratio whose dependency on the penalty ratio almost matches the lower bound.