Optimal voltage allocation techniques for dynamically variable voltage processors
Proceedings of the 40th annual Design Automation Conference
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
On energy-optimal voltage scheduling for fixed-priority hard real-time systems
ACM Transactions on Embedded Computing Systems (TECS)
Dynamic Speed Scaling to Manage Energy and Temperature
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
An Efficient Algorithm for Computing Optimal Discrete Voltage Schedules
SIAM Journal on Computing
Speed scaling to manage energy and temperature
Journal of the ACM (JACM)
Competitive online scheduling for server systems
ACM SIGMETRICS Performance Evaluation Review
Speed scaling on parallel processors
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
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
Speed Scaling with a Solar Cell
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
LATIN'08 Proceedings of the 8th Latin American conference on Theoretical informatics
Speed scaling to manage temperature
STACS'05 Proceedings of the 22nd annual conference on Theoretical Aspects of Computer Science
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
Tradeoff between energy and throughput for online deadline scheduling
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
Speed Scaling for Weighted Flow Time
SIAM Journal on Computing
Speed scaling to manage temperature
TAPAS'11 Proceedings of the First international ICST conference on Theory and practice of algorithms in (computer) systems
On multi-processor speed scaling with migration: extended abstract
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
Multiprocessor speed scaling for jobs with arbitrary sizes and deadlines
TAMC'11 Proceedings of the 8th annual conference on Theory and applications of models of computation
Race to idle: new algorithms for speed scaling with a sleep state
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
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
The Bell Is Ringing in Speed-Scaled Multiprocessor Scheduling
Theory of Computing Systems
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Speed scaling is a power management technique that involves dynamically changing the speed of a processor. This gives rise to dual-objective scheduling problems, where the operating system both wants to conserve energy and optimize some Quality of Service (QoS) measure of the resulting schedule. In the most investigated speed scaling problem in the literature, the QoS constraint is deadline feasibility, and the objective is to minimize the energy used. The standard assumption is that the power consumption is the speed to some constant power *** . We give the first non-trivial lower bound, namely e *** *** 1/*** , on the competitive ratio for this problem. This comes close to the best upper bound which is about 2e *** + 1. We analyze a natural class of algorithms called qOA, where at any time, the processor works at q *** 1 times the minimum speed required to ensure feasibility assuming no new jobs arrive. For CMOS based processors, and many other types of devices, *** = 3, that is, they satisfy the cube-root rule. When *** = 3, we show that qOA is 6.7-competitive, improving upon the previous best guarantee of 27 achieved by the algorithm Optimal Available (OA). So when the cube-root rule holds, our results reduce the range for the optimal competitive ratio from [1.2, 27] to [2.4, 6.7]. We also analyze qOA for general *** and give almost matching upper and lower bounds.