Amortized efficiency of list update and paging rules
Communications of the ACM
On the power of randomization in online algorithms
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
Modern operating systems
Theoretical Computer Science - Special issue on dynamic and on-line algorithms
Approximating total flow time on parallel machines
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Minimizing the flow time without migration
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Operating System Projects Using Windows NT
Operating System Projects Using Windows NT
Speed is as powerful as clairvoyance [scheduling problems]
FOCS '95 Proceedings of the 36th Annual Symposium on Foundations of Computer Science
Minimizing flow time nonclairvoyantly
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
ICALP '02 Proceedings of the 29th International Colloquium on Automata, Languages and Programming
Non-clairvoyant Scheduling for Minimizing Mean Slowdown
STACS '03 Proceedings of the 20th Annual Symposium on Theoretical Aspects of Computer Science
Server scheduling in the Lp norm: a rising tide lifts all boat
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
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
Journal of Scheduling
Theoretical Computer Science - Special issue: Online algorithms in memoriam, Steve Seiden
Competitive online scheduling for server systems
ACM SIGMETRICS Performance Evaluation Review
A simpler proof of preemptive total flow time approximation on parallel machines
Efficient Approximation and Online Algorithms
Hi-index | 0.00 |
Scheduling a sequence of jobs released over time when the processing time of a job is only known at its completion is a classical problem in CPU scheduling in time sharing operating systems. A widely used measure for the responsiveness of the system is the average flow time of the jobs, i.e. the average time spent by jobs in the system between release and completion.The Windows NT and the Unix operating system scheduling policies are based on the Multi-level Feedback algorithm [12, 1]. In this paper we prove that a randomized version of the Multi-level Feedback algorithm is competitive for single and parallel machine systems, in our opinion providing one theoretical validation of the goodness of an idea that has been very effective in practice along the last two decades.The randomized Multi-level Feedback algorithm (RMLF) was first proposed by Kalyanasundaram and Pruhs [7] for a single machine achieving an O(\log n \log\log n) competitive ratio to minimize the average flow time against the on-line adaptive adversary, where n is the number of jobs that are released. We present a version of RMLF working for any numberm of parallel machines. We show for RMLF a first O(\log n\log \frac{n}{m}) competitiveness result against the oblivious adversary on parallel machines. We also show that the same RMLF algorithm surprisingly achieves a tight O(\log n) competitive ratio against the oblivious adversary on a single machine, therefore matching the lower bound of [10].