Large scale distributed systems require replication of resources to amplify availability and to provide fault tolerance. The placement of replicated resources significantly impacts performance. This paper considers local k-placements: Each node of a network has to place k replicas of a resource among its direct neighbors. The load of a node in a given local k-placement is the number of replicas it stores. The local k-placement problem is to achieve a preferably homogeneous distribution of the loads. We present a novel self-stabilizing, distributed, asynchronous, scalable algorithm for the k-placement problem such that the standard deviation of the distribution of the loads assumes a local minimum. © 2012 Springer-Verlag.
CITATION STYLE
Köhler, S., Turau, V., & Mentges, G. (2012). Self-stabilizing local k-placement of replicas with minimal variance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7596 LNCS, pp. 16–30). https://doi.org/10.1007/978-3-642-33536-5_2
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