TY - GEN
T1 - The itinerant list update problem
AU - Olver, Neil
AU - Pruhs, Kirk
AU - Schewior, Kevin
AU - Sitters, René
AU - Stougie, Leen
PY - 2018
Y1 - 2018
N2 - 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(log2n) -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(logn) 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.
AB - 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(log2n) -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(logn) 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.
UR - https://www.scopus.com/pages/publications/85058460377
UR - https://www.scopus.com/pages/publications/85058460377#tab=citedBy
U2 - 10.1007/978-3-030-04693-4_19
DO - 10.1007/978-3-030-04693-4_19
M3 - Conference contribution
AN - SCOPUS:85058460377
SN - 9783030046927
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 310
EP - 326
BT - Approximation and Online Algorithms
A2 - Epstein, Leah
A2 - Erlebach, Thomas
PB - Springer - Verlag
T2 - 16th Workshop on Approximation and Online Algorithms, WAOA 2018
Y2 - 23 August 2018 through 24 August 2018
ER -