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CloudCluster: Unearthing the Functional Structure of a Cloud Service

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CloudCluster extracts a service’s functional groups by clustering the VM-to-VM traffic matrix, using feature scaling, dimensionality reduction (via singular value decomposition), and agglomerative hierarchical clustering so no K is required. It scales to projects of thousands to tens of thousands of VMs, finds clusters that match ground truth (over 92% homogeneity and 94% completeness), detects 25 real structural anomalies (all detected), and enables operational actions such as a reported 40% cost reduction for one functional group.

AI-written summary · about

  1. 0:00Intro
  2. 0:54Anatomy of a Cloud Service: The Customer View
  3. 1:24Anatomy of a Cloud Service: The Provider View
  4. 2:35Today: Monitoring at Different levels of Abstract
  5. 3:00A New Abstraction: The Functional Structure
  6. 3:48What do we mean by Functional Structure?
  7. 4:39The Provider Does Not Know the Functional Stru
  8. 4:56Extracting the Functional Structure
  9. 5:29Hypothesis
  10. 6:28Approach: Use Clustering!
  11. 6:43Cloud Cluster: Challenges
  12. 7:17The Problem
  13. 7:48Insight: Exploit Redundancy in VM-to-VM Traffic
  14. 8:09Dimensionality Reduction
  15. 8:46Agglomerative Clustering
  16. 9:15Generating the Clusters: Where to cut the dendre
  17. 9:28Use Cluster Inconsistency to Decide!
  18. 9:43Evaluating Clustering Performance
  19. 10:28Cloud Cluster. Excellent Agreement with Ground
  20. 11:32What is the Functional Structure Useful For?
  21. 12:06Detecting Traffic Shifts with Cloud Cluster
  22. 12:38Detecting Structural Changes
  23. 13:20Anomaly Detection
  24. 14:04Summary
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