CloudCluster: Unearthing the Functional Structure of a Cloud Service
Talkat 19th USENIX Symposium on Networked Systems Design and Implementation (NSDI '22) · 2022-04-04..04-06·
summary
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.
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chapters (24)
- 0:00Intro
- 0:54Anatomy of a Cloud Service: The Customer View
- 1:24Anatomy of a Cloud Service: The Provider View
- 2:35Today: Monitoring at Different levels of Abstract
- 3:00A New Abstraction: The Functional Structure
- 3:48What do we mean by Functional Structure?
- 4:39The Provider Does Not Know the Functional Stru
- 4:56Extracting the Functional Structure
- 5:29Hypothesis
- 6:28Approach: Use Clustering!
- 6:43Cloud Cluster: Challenges
- 7:17The Problem
- 7:48Insight: Exploit Redundancy in VM-to-VM Traffic
- 8:09Dimensionality Reduction
- 8:46Agglomerative Clustering
- 9:15Generating the Clusters: Where to cut the dendre
- 9:28Use Cluster Inconsistency to Decide!
- 9:43Evaluating Clustering Performance
- 10:28Cloud Cluster. Excellent Agreement with Ground
- 11:32What is the Functional Structure Useful For?
- 12:06Detecting Traffic Shifts with Cloud Cluster
- 12:38Detecting Structural Changes
- 13:20Anomaly Detection
- 14:04Summary
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