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...

• Type: Journal
• Year: 2004
• Where: ALGORITHMICA
• Authors: Nikhil Bansal, Kedar Dhamdhere, Jochen Könemann, Amitabh Sinha
• Keywords: Algorithm, ALGORITHMICA 2004, Job Sizes, Mean Slowdown