Speed Scaling Functions for Flow Time Scheduling Based on Active Job Count
ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Weighted flow time does not admit O(1)-competitive algorithms
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Proceedings of the forty-first annual ACM symposium on Theory of computing
Sleep with Guilt and Work Faster to Minimize Flow Plus Energy
ICALP '09 Proceedings of the 36th International Colloquium on Automata, Languages and Programming: Part I
Communications of the ACM
Energy efficient scheduling via partial shutdown
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
Scalably scheduling power-heterogeneous processors
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Speed Scaling for Weighted Flow Time
SIAM Journal on Computing
Sleep management on multiple machines for energy and flow time
ICALP'11 Proceedings of the 38th international colloquim conference on Automata, languages and programming - Volume Part I
Resource augmentation for weighted flow-time explained by dual fitting
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
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We consider online job scheduling together with power management on multiple machines. In this model, jobs with arbitrary sizes and weights arrive online, and each machine consumes different amount of energy when it is processing a job, idling or sleeping. A scheduler has to maintain a good balance of the states of the machines to avoid energy wastage, while giving an efficient schedule of the jobs. We consider a recently well-studied objective of minimizing the total weighted flow time of the jobs plus the total energy usage. For the special case where all jobs have the same weight, competitive algorithms have been obtained (Lam et al. 2009, Chan et al. 2011). This paper gives a non-trivial potential analysis of a weighted generalization of the power management algorithm in (Chan et al. 2011), coupled with a classic scheduling algorithm HDF. This leads to the first competitive result for minimizing weighted flow time plus energy. The result can be extended to the dynamic speed scaling model where the scheduler can vary the speed of individual machines to process the jobs and the energy usage depends on the speed of the machines.