TY - GEN
T1 - Maintaining arrays of contiguous objects
AU - Bender, Michael A.
AU - Fekete, Sándor P.
AU - Kamphans, Tom
AU - Schweer, Nils
PY - 2009
Y1 - 2009
N2 - In this paper we consider methods for dynamically storing a set of different objects ("modules") in a physical array. Each module requires one free contiguous subinterval in order to be placed. Items are inserted or removed, resulting in a fragmented layout that makes it harder to insert further modules. It is possible to relocate modules, one at a time, to another free subinterval that is contiguous and does not overlap with the current location of the module. These constraints clearly distinguish our problem from classical memory allocation. We present a number of algorithmic results, including a bound of on physical sorting if there is a sufficiently large free space and sum up NP-hardness results for arbitrary initial layouts. For online scenarios in which modules arrive one at a time, we present a method that requires O(1) moves per insertion or deletion and amortized cost per insertion or deletion, where m i is the module's size, is the size of the largest module and costs for moves are linear in the size of a module.
AB - In this paper we consider methods for dynamically storing a set of different objects ("modules") in a physical array. Each module requires one free contiguous subinterval in order to be placed. Items are inserted or removed, resulting in a fragmented layout that makes it harder to insert further modules. It is possible to relocate modules, one at a time, to another free subinterval that is contiguous and does not overlap with the current location of the module. These constraints clearly distinguish our problem from classical memory allocation. We present a number of algorithmic results, including a bound of on physical sorting if there is a sufficiently large free space and sum up NP-hardness results for arbitrary initial layouts. For online scenarios in which modules arrive one at a time, we present a method that requires O(1) moves per insertion or deletion and amortized cost per insertion or deletion, where m i is the module's size, is the size of the largest module and costs for moves are linear in the size of a module.
UR - https://www.scopus.com/pages/publications/70350657082
U2 - 10.1007/978-3-642-03409-1_3
DO - 10.1007/978-3-642-03409-1_3
M3 - Conference contribution
AN - SCOPUS:70350657082
SN - 364203408X
SN - 9783642034084
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 14
EP - 25
BT - Fundamentals of Computation Theory - 17th International Symposium, FCT 2009, Proceedings
T2 - 17th International Symposium on Fundamentals of Computation Theory, FCT 2009
Y2 - 2 September 2009 through 4 September 2009
ER -