A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Convex Optimization
Multiprocessor Energy-Efficient Scheduling with Task Migration Considerations
ECRTS '04 Proceedings of the 16th Euromicro Conference on Real-Time Systems
Dynamic Speed Scaling to Manage Energy and Temperature
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Speed scaling to manage energy and temperature
Journal of the ACM (JACM)
Energy Optimal Scheduling on Multiprocessors with Migration
ISPA '08 Proceedings of the 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications
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
The Design of Competitive Online Algorithms via a Primal-Dual Approach
The Design of Competitive Online Algorithms via a Primal-Dual Approach
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
Tradeoff between energy and throughput for online deadline scheduling
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
On multi-processor speed scaling with migration: extended abstract
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
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We present a new online algorithm for profit-oriented scheduling on multiple speed-scalable processors. Moreover, we provide a tight analysis of the algorithm's competitiveness. Our results generalize and improve upon work by Chan et al. [10], which considers a single speed-scalable processor. Using significantly different techniques, we can not only extend their model to multiprocessors but also prove an enhanced and tight competitive ratio for our algorithm. In our scheduling problem, jobs arrive over time and are preemptable. They have different workloads, values, and deadlines. The scheduler may decide not to finish a job but instead to suffer a loss equaling the job's value. However, to process a job's workload until its deadline the scheduler must invest a certain amount of energy. The cost of a schedule is the sum of lost values and invested energy. In order to finish a job the scheduler has to determine which processors to use and set their speeds accordingly. A processor's energy consumption is power Pα(s) integrated over time, where Pα(s) = sα is the power consumption when running at speed s. Since we consider the online variant of the problem, the scheduler has no knowledge about future jobs. This problem was introduced by Chan et al. [10] for the case of a single processor. They presented an online algorithm which is αα +2eα-competitive. We provide an online algorithm for the case of multiple processors with an improved competitive ratio of αα.