Workload Characterization Techniques
This lecture covers the following topics:
- Terminology
- Components and Parameter Selection
- Workload Characterization Techniques
- Averaging
- Case Study: Program Usage in Educational Environments
- Characteristics of an Average Editing Session
- Single Parameter Histograms
- Multi-parameter Histograms
- Principal Component Analysis
- Finding Principal Factors
- Principal Component Example
- Principal Component Example
- Markov Models
- Transition Probability
- Clustering
- Clustering Steps
- 1. Sampling
- 2. Parameter Selection
- 3. Transformation
- 4. Outliers
- 5. Data Scaling
- Distance Metric
- Clustering Techniques
- Minimum Spanning Tree-Clustering Method
- Minimum Spanning Tree Example
- Dendogram
- Nearest Centroid Method
- Cluster Interpretation
- Problems with Clustering
- Summary
- Exercise 6.1
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