Minimizing total flow time and total completion time with immediate dispatching
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
A scheduling model for reduced CPU energy
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Multi-processor scheduling to minimize flow time with ε resource augmentation
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
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
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
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
The bell is ringing in speed-scaled multiprocessor scheduling
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
Communications of the ACM
Optimality analysis of energy-performance trade-off for server farm management
Performance Evaluation
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
Nonclairvoyant sleep management and flow-time scheduling on multiple processors
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
Competitive online algorithms for multiple-machine power management and weighted flow time
CATS '13 Proceedings of the Nineteenth Computing: The Australasian Theory Symposium - Volume 141
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In large data centers, determining the right number of operating machines is often non-trivial, especially when the workload is unpredictable. Using too many machines would waste energy, while using too few would affect the performance. This paper extends the traditional study of online flow-time scheduling on multiple machines to take sleep management and energy into consideration. Specifically, we study online algorithms that can determine dynamically when and which subset of machines should wake up (or sleep), and how jobs are dispatched and scheduled. We consider schedules whose objective is to minimize the sum of flow time and energy, and obtain O(1)-competitive algorithms for two settings: one assumes machines running at a fixed speed, and the other allows dynamic speed scaling to further optimize energy usage. Like the previous work on the tradeoff between flow time and energy, the analysis of our algorithms is based on potential functions. What is new here is that the online and offline algorithms would use different subsets of machines at different times, and we need a more general potential analysis that can consider different match-up of machines.