From 7be00c5a3baf10295069d805a7d3765a91f8f20d Mon Sep 17 00:00:00 2001 From: "Julian M. Kunkel" Date: Fri, 4 Dec 2020 16:04:35 +0000 Subject: [PATCH] Kl. verbesserung --- paper/main.tex | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/paper/main.tex b/paper/main.tex index 0bbe14a..10f4508 100644 --- a/paper/main.tex +++ b/paper/main.tex @@ -205,17 +205,17 @@ They differ in the way data similarity is defined; either the time series is enc B-all determines similarity between binary codings by means of Levenshtein distance. B-aggz is similar to B-all, but computes similarity on binary codings where subsequent segments of zero activities are replaced by just one zero. Q-lev determines similarity between quantized codings by using Levensthein distance. -Q-native uses a performance-aware similarity function, i.e., distance for a metric is $\frac{|m_{job1} - m_{job2}|}{16}$. +Q-native uses a performance-aware similarity function, i.e., the distance between two jobs for a metric is $\frac{|m_{job1} - m_{job2}|}{16}$. For jobs with different lengths, we apply a sliding-windows approach which finds the location for the shorter job in the long job with the highest similarity. Q-phases extract phase information and performs a phase-aware and performance-aware similarity computation. The Q-phases algorithm extracts I/O phases and computes the similarity between the most similar I/O phases of both jobs. -In this paper, we add a new similarity definition based on Kolmogorov-Smirnov-Test that compares the probability distribution of the observed values which we describe in the following. -In brief, KS concatenates individual node data (instead of averaging) and computes similarity be means of Kolmogorov-Smirnov-Test. +In this paper, we add a similarity definition based on Kolmogorov-Smirnov-Test that compares the probability distribution of the observed values which we describe in the following. +%In brief, KS concatenates individual node data and computes similarity be means of Kolmogorov-Smirnov-Test. \paragraph{Kolmogorov-Smirnov (KS) algorithm} % Summary For the analysis, we perform two preparation steps. -Dimension reduction by computing means across the two file systems and by concatenating the time series data of the individual nodes. +Dimension reduction by computing means across the two file systems and by concatenating the time series data of the individual nodes (instead of averaging) them. This reduces the four-dimensional dataset to two dimensions (time, metrics). % Aggregation