Logarithmic growth dynamics in software networks

In a recent paper, Krapivsky and Redner (Phys. Rev. E, 71 (2005) 036118) proposed a new growing network model with new nodes being attached to a randomly selected node, as well to all ancestors of the target node. The model leads to a sparse graph with an average degree growing logarithmically with the system size. Here we present compeling evidence for software networks being the result of a similar class of growing dynamics. The predicted pattern of network growth, as well as the stationary in- and out-degree distributions are consistent with the model. Our results confirm the view of large-scale software topology being generated through duplication-rewiring mechanisms. Implications of these findings are outlined.Comment: 7 pages, 3 figures, published in Europhysics Letters (2005

On-line Measurement of Paging Behavior by the

Abstract: An algorithm is presented that extracts the sequence of minimum memory capacities (MMCs) from the sequence of page references generated by a program as it is executed in a demand paging environment. The new algorithm combines the advantages of existing approaches in that the MMC’s are produced in a single pass, as is the output of the MIN algorithm for a single memory size, and the MMC sequence is identical to the optimum stack distances provided by the OPT algorithm, which requires two passes. A hardware implementation is outlined as an extension to existing page management mechanisms. The resulting device could be used to produce continuously the MMC information, while the (paging) machine executes the program at essentially full speed. The paper also discusses the possible impact of the algorithm on the study of program behavior and on the development of space sharing (paging) algorithms. Finally, a proof is provided that the algorithm in fact produces an output identical to that of OPT. 1

Online Companion Caching

This paper is concerned with online cachin al orithms for the (n, k)-companion cache, defined by Brehob et. al. [3]. In this model the cache is composed of two components: a k-way set-associative cache and a companion fu lly-associative cache of size n. We show that the deterministic competitive ratio for this problem is (n+1)(k+1)- 1, and the randomized competitive ratio is O(log n log k)and#(log n +logk)