Online Scheduling for Sorting Buffers
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Reordering buffers for general metric spaces
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Exploiting locality: approximating sorting buffers
Journal of Discrete Algorithms
Improved online algorithms for the sorting buffer problem
STACS'07 Proceedings of the 24th annual conference on Theoretical aspects of computer science
Evaluation of online strategies for reordering buffers
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
Online sorting buffers on line
STACS'06 Proceedings of the 23rd Annual conference on Theoretical Aspects of Computer Science
Reordering buffer management for non-uniform cost models
ICALP'05 Proceedings of the 32nd international conference on Automata, Languages and Programming
Offline sorting buffers on line
ISAAC'06 Proceedings of the 17th international conference on Algorithms and Computation
Optimal online buffer scheduling for block devices
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
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A sequence of objects that are characterized by their color has to be processed. Their processing order influences how efficiently they can be processed: Each color change between two consecutive objects produces costs. A reordering buffer, which is a random access buffer with storage capacity for k objects, can be used to rearrange this sequence online in such a way that the total costs are reduced. This concept is useful for many applications in computer science and economics. The strategy with the best-known competitive ratio is MAP. An upper bound of O(log k) on the competitive ratio of MAP is known and a nonconstant lower bound on the competitive ratio is not known. Based on theoretical considerations and experimental evaluations, we give strong evidence that the previously used proof techniques are not suitable to show an o(&sqrt;log k) upper bound on the competitive ratio of MAP. However, we also give some evidence that in fact MAP achieves a competitive ratio of O(1). Further, we evaluate the performance of several strategies on random input sequences experimentally. MAP and its variants RC and RR clearly outperform the other strategies FIFO, LRU, and MCF. In particular, MAP, RC, and RR are the only known strategies whose competitive ratios do not depend on the buffer size. Furthermore, MAP achieves the smallest competitive ratio.