Online Scheduling for Sorting Buffers
ESA '02 Proceedings of the 10th Annual European Symposium on 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
Reordering buffers for general metric spaces
Proceedings of the thirty-ninth annual ACM symposium on Theory of computing
Algorithms and data structures for external memory
Foundations and Trends® in Theoretical Computer Science
Evaluation of online strategies for reordering buffers
Journal of Experimental Algorithmics (JEA)
Buffer management for colored packets with deadlines
Proceedings of the twenty-first annual symposium on Parallelism in algorithms and architectures
Improved online algorithms for the sorting buffer problem on line metrics
ACM Transactions on Algorithms (TALG)
An improved competitive algorithm for reordering buffer management
SODA '10 Proceedings of the twenty-first annual ACM-SIAM symposium on Discrete Algorithms
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We consider the offline sorting buffers problem. Input to this problem is a sequence of requests, each specified by a point in a metric space. There is a “server” that moves from point to point to serve these requests. To serve a request, the server needs to visit the point corresponding to that request. The objective is to minimize the total distance travelled by the server in the metric space. In order to achieve this, the server is allowed to serve the requests in any order that requires to “buffer” at most k requests at any time. Thus a valid reordering can serve a request only after serving all but k previous requests. In this paper, we consider this problem on a line metric which is motivated by its application to a widely studied disc scheduling problem. On a line metric with N uniformly spaced points, our algorithm yields the first constant-factor approximation and runs in quasi-polynomial time O(mNkO(logN)) where m is the total number of requests. Our approach is based on a dynamic program that keeps track of the number of pending requests in each of O(logN) line segments that are geometrically increasing in length.