Non-Clairvoyant Scheduling for Minimizing Mean Slowdown

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Non-Clairvoyant Scheduling for Minimizing Mean Slowdown
We consider the problem of scheduling dynamically arriving jobs in a non-clairvoyant setting, that is, when the size of a job in remains unknown until the job finishes execution. Our focus is on minimizing the mean slowdown, where the slowdown of a job (also known as stretch) is defined as the ratio of flow time to the size of the job. We use resource augmentation in terms of allowing a faster processor to the online algorithm to make up for its lack of knowledge of job sizes. Our main result is that the Multi-level Feedback (MLF) algorithm [14, 16], used in the Windows NT and Unix operating system scheduling policies is an (1+ )-speed O((1/ )5 log2 B)-competitive algorithm for minimizing mean slowdown non-clairvoyantly, when B is the ratio between the largest and smallest job sizes. In a sense, this provides a theoretical justification of the effectiveness of an algorithm widely used in practice. On the other hand, we also show that any O(1)-speed algorithm, deterministic or randomiz...
Nikhil Bansal, Kedar Dhamdhere, Jochen Könema
Added 16 Dec 2010
Updated 16 Dec 2010
Type Journal
Year 2004
Authors Nikhil Bansal, Kedar Dhamdhere, Jochen Könemann, Amitabh Sinha
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