Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Minimizing flow time nonclairvoyantly
Journal of the ACM (JACM)
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
The Cauchy-Schwarz Master Class: An Introduction to the Art of Mathematical Inequalities
The Cauchy-Schwarz Master Class: An Introduction to the Art of Mathematical Inequalities
Energy-efficient algorithms for flow time minimization
ACM Transactions on Algorithms (TALG)
Competitive non-migratory scheduling for flow time and energy
Proceedings of the twentieth annual symposium on Parallelism in algorithms and architectures
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
Weighted flow time does not admit O(1)-competitive algorithms
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Speed scaling of processes with arbitrary speedup curves on a multiprocessor
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Optimal speed scaling under arbitrary power functions
ACM SIGMETRICS Performance Evaluation Review
Non-clairvoyant scheduling for weighted flow time and energy on speed bounded processors
CATS '10 Proceedings of the Sixteenth Symposium on Computing: the Australasian Theory - Volume 109
Speed Scaling for Weighted Flow Time
SIAM Journal on Computing
Scheduling heterogeneous processors isn't as easy as you think
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Non-clairvoyant weighted flow time scheduling on different multi-processor models
WAOA'11 Proceedings of the 9th international conference on Approximation and Online Algorithms
Non-clairvoyant weighted flow time scheduling with rejection penalty
Proceedings of the twenty-fourth annual ACM symposium on Parallelism in algorithms and architectures
Scheduling for weighted flow time and energy with rejection penalty
Theoretical Computer Science
Nonclairvoyant sleep management and flow-time scheduling on multiple processors
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Dual techniques for scheduling on a machine with varying speed
ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part I
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We study online job scheduling on a processor that can vary its speed dynamically to manage its power. We attempt to extend the recent success in analyzing total unweighted flow time plus energy to total weighted flow time plus energy. We first consider the nonclairvoyant setting where the size of a job is only known when the job finishes. We show an online algorithm WLAPS that is 8α2-competitive for weighted flow time plus energy under the traditional power model, which assumes the power P(s) to run the processor at speed s to be sα for some α 1. More interestingly, for any arbitrary power function P(s), WLAPS remains competitive when given a more energy-efficient processor; precisely, WLAPS is 16(1 + 1/ε)2-competitive when using a processor that, given the power P(s), can run at speed (1 + ε)s for some ε 0. Without such speedup, no non-clairvoyant algorithm can be O(1)-competitive for an arbitrary power function [8]. For the clairvoyant setting (where the size of a job is known at release time), previous results on minimizing weighted flow time plus energy rely on scaling the speed continuously over time [5-7]. The analysis of WLAPS has inspired us to devise a clairvoyant algorithm LLB which can transform any continuous speed scaling algorithm to one that scales the speed at discrete times only. Under an arbitrary power function, LLB can give an 4(1 + 1/ε)-competitive algorithm using a processor with (1 + ε)-speedup.