Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Approximability and nonapproximability results for minimizing total flow time on a single machine
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Approximating total flow time on parallel machines
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Optimal time-critical scheduling via resource augmentation (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Scheduling to minimize average completion time: off-line and on-line approximation algorithms
Mathematics of Operations Research
Minimizing the flow time without migration
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Approximation techniques for average completion time scheduling
SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Flow and stretch metrics for scheduling continuous job streams
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Scheduling to minimize average stretch without migration
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Speed is as powerful as clairvoyance
Journal of the ACM (JACM)
Algorithms for minimizing weighted flow time
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Approximation schemes for preemptive weighted flow time
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Improved algorithms for stretch scheduling
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Non-clairvoyant Scheduling for Minimizing Mean Slowdown
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Minimizing total flow time and total completion time with immediate dispatching
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures
Server scheduling in the Lp norm: a rising tide lifts all boat
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Minimizing flow time nonclairvoyantly
Journal of the ACM (JACM)
Online Scheduling to Minimize Average Stretch
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Approximation Schemes for Minimizing Average Weighted Completion Time with Release Dates
FOCS '99 Proceedings of the 40th 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
Nonclairvoyant scheduling to minimize the total flow time on single and parallel machines
Journal of the ACM (JACM)
Minimizing flow time on a constant number of machines with preemption
Operations Research Letters
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
Server Scheduling to Balance Priorities, Fairness, and Average Quality of Service
SIAM Journal on Computing
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
Shortest-Elapsed-Time-First on a multiprocessor
MedAlg'12 Proceedings of the First Mediterranean conference on Design and Analysis of Algorithms
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We consider the problem of minimizing the total weighted flow time on a single machine with preemptions. We give an online algorithm that is O(k)-competitive for k weight classes. This implies an O(log W)-competitive algorithm, where W is the maximum to minimum ratio of weights. This algorithm also implies an O(log n + log P)-approximation ratio for the problem, where P is the ratio of the maximum to minimum job size and n is the number of jobs. We also consider the nonclairvoyant setting where the size of a job is unknown upon its arrival and becomes known to the scheduler only when the job meets its service requirement. We consider the resource augmentation model, and give a (1 + ϵ)-speed, (1 +1/ϵ)-competitive online algorithm.