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Workload characterization is used to characterize, analyze and select benchmarks within a benchmark suite. In addition, it is used to study the behavior of ...
Workload Characterization Using Hierarchical PCA. Lina Sawalha. Department of Electrical and Computer Engineering. Western Michigan University. Kalamazoo, MI.
In this paper, we propose a hierarchical principal component analysis (HPCA), a more accurate methodology for workload characterization.
Jul 5, 2020 · In the paper, the author is using the GIC sectors to order the stocks (center), we can see that we can find a better ordering with a hierarchical clustering.
Missing: Workload Characterization
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Nov 30, 2021 · Is it appropriate if I conduct principal component analysis (PCA) and hierarchical clustering simultaneously for time-series data?
Missing: Workload | Show results with:Workload
Take a sample, that is, a subset of workload components. 2. Select workload parameters. 3. Select a distance measure. 4. Remove outliers.
[PDF] Workload Characterization Techniques - Washington University
www.cs.wustl.edu › cse567-08 › ftp
Hierarchical Techniques: ➢ Agglomerative: Start with n clusters and merge. ➢ Divisive: Start with one cluster and divide. ❑ ...
Both PCA and hierarchical clustering are unsupervised methods, meaning that no information about class membership or other response variables are used to ...
In this paper, we survey workload characterization techniques used for several types of computer systems. We identify significant issues and concerns ...
Hierarchical. Start with 1 cluster, divide until k; Start with n clusters, combine until k. Ex: minimum spanning tree.