1. The itinerant list update problem
- Author
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Olver, Neil, Pruhs, Kirk, Schewior, Kevin, Sitters, René, Stougie, Leen, Epstein, Leah, Erlebach, Thomas, Epstein, Leah, Erlebach, Thomas, Free University of Amsterdam, Department of Computer Science - University of Pittsburgh, University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE)-Pennsylvania Commonwealth System of Higher Education (PCSHE), Technische Universität Munchen - Université Technique de Munich [Munich, Allemagne] (TUM), Vrije Universiteit Amsterdam [Amsterdam] (VU), Centrum Wiskunde & Informatica (CWI), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Amsterdam Business Research Institute, Tinbergen Institute, and Econometrics and Operations Research
- Subjects
Online and offline ,List update problem ,Theoretical computer science ,010201 computation theory & mathematics ,Computer science ,Pointer (computer programming) ,0202 electrical engineering, electronic engineering, information engineering ,[INFO]Computer Science [cs] ,0102 computer and information sciences ,02 engineering and technology ,01 natural sciences ,020202 computer hardware & architecture - Abstract
We introduce the itinerant list update problem (ILU), which is a relaxation of the classic list update problem in which the pointer no longer has to return to a home location after each request. The motivation to introduce ILU arises from the fact that it naturally models the problem of track memory management in Domain Wall Memory. Both online and offline versions of ILU arise, depending on specifics of this application. First, we show that ILU is essentially equivalent to a dynamic variation of the classical minimum linear arrangement problem (MLA), which we call DMLA. Both ILU and DMLA are very natural, but do not appear to have been studied before. In this work, we focus on the offline ILU and DMLA problems. We then give an O ( log 2 n ) -approximation algorithm for these problems. While the approach is based on well-known divide-and-conquer approaches for the standard MLA problem, the dynamic nature of these problems introduces substantial new difficulties. We also show an Ω ( log n ) lower bound on the competitive ratio for any randomized online algorithm for ILU. This shows that online ILU is harder than online LU, for which O(1)-competitive algorithms, like Move-To-Front, are known.
- Published
- 2018