Amortized efficiency of list update and paging rules
Communications of the ACM
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
Flow and stretch metrics for scheduling continuous job streams
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Operating System Projects Using Windows NT
Operating System Projects Using Windows NT
Improved algorithms for stretch scheduling
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Minimizing the Flow Time Without Migration
SIAM Journal on Computing
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
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
Online Scheduling to Minimize Average Stretch
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Average stretch without migration
Journal of Computer and System Sciences
Average-Case and Smoothed Competitive Analysis of the Multilevel Feedback Algorithm
Mathematics of Operations Research
Approximating total flow time on parallel machines
Journal of Computer and System Sciences
ACM Transactions on Algorithms (TALG)
An Optimal Strategy for Online Non-uniform Length Order Scheduling
AAIM '08 Proceedings of the 4th international conference on Algorithmic Aspects in Information and Management
Scalably scheduling processes with arbitrary speedup curves
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
Speed scaling of processes with arbitrary speedup curves on a multiprocessor
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Improved results for scheduling batched parallel jobs by using a generalized analysis framework
Journal of Parallel and Distributed Computing
A priori parallel machines scheduling
Computers and Industrial Engineering
Provably efficient two-level adaptive scheduling
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Scheduling jobs with varying parallelizability to reduce variance
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
Minimizing maximum flowtime of jobs with arbitrary parallelizability
WAOA'10 Proceedings of the 8th international conference on Approximation and online algorithms
Server Scheduling to Balance Priorities, Fairness, and Average Quality of Service
SIAM Journal on Computing
On-Line algorithms, real time, the virtue of laziness, and the power of clairvoyance
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
An online scalable algorithm for minimizing lk-norms of weighted flow time on unrelated machines
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Online scheduling on identical machines using SRPT
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
Scalably scheduling processes with arbitrary speedup curves
ACM Transactions on Algorithms (TALG)
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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, that is, 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 Multilevel Feedback algorithm. In this article, we prove that a randomized version of the Multilevel 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 proven effective in practice along the last two decades.The randomized Multilevel Feedback algorithm (RMLF) was first proposed by Kalyanasundaram and Pruhs 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 number m of parallel machines. We show for RMLF a first O(log n log 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 for this case.