Book Synopsis On-line Deadline Scheduling Under Relaxed Metrics of Optimality by :
Download or read book On-line Deadline Scheduling Under Relaxed Metrics of Optimality written by and published by . This book was released on 2001 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: (Uncorrected OCR) Abstract of thesis entitled On-line Deadline Scheduling under Relaxed Metrics of Optimality submitted by Kar-Keung To for the degree of Doctor of Philosophy at The University of Hong Kong in August 2000. In this thesis, we study on-line algorithms that schedule jobs with deadlines to one or more processors. By the nature of job deadlines, such scheduling algorithms are classified into hard-deadline algorithms and firm-deadline algorithms. Traditionally, on-line algorithms are measured by metrics like optimality and competitiveness. They compare the on-line algorithm against an off-line adversary, which has full knowledge of the future. Previous results on on-line deadline scheduling were very negative, revealing that for most settings, no on-line algorithm can be optimal or achieves constant competitive ratio. This resulted in a situation that although algorithms for deadline scheduling are needed practically, none of them can provide theoretically sound performance guarantee. Kalyanasundaram and Pruhs [JACM 2000] proposed using relaxed metrics to analyze scheduling algorithms. Such metrics give the on-line algorithms more resources than the off-line adversary, compensating for the lack of future information experienced by the on-line algorithms. Although this direction seems plausible, the study of optimal deadline scheduling under such metrics has been limited to hard-deadline algorithms. This thesis furthers the study, showing better algorithms for optimal hard-deadline scheduling and extending the study to the more general firm-deadline setting. For multiprocessor hard-deadline scheduling, we give a new algorithm that achieves optimality with less speedy processors than previous algorithms. We also derive a new lower bound of processor speed required by any hard-deadline algorithm to be optimal. For the more general setting of firm-deadline scheduling, we analyze a simple algorithm Edf-ac, which augments EDF (the popular Earliest-Deadline-First.