Download - Service Clustering for Autonomic Clouds Using Random Forest

Service Clustering for AutonomicClouds Using Random Forest

Rafael Brundo UriarteIMT Lucca

Sotirios Tsaftaris Francesco TiezziIMT Lucca University of Camerino

CCGrid - 7th May 2015 - Shenzhen, China

Contents

1 Introduction

2 Requirements and Existing Solutions

3 RF+PAM

4 Evaluation

5 Conclusions

Uriarte, Tsaftaris and Tiezzi 1/29

Introduction

Introduction Uriarte, Tsaftaris and Tiezzi 2/29

Cloud Computing

I Everything-as-a-Service

I Dynamism

I Heterogeneity

I Virtualization

I Large-Scale


Autonomic Computing


Autonomic Clouds

I Restricted Knowledge

I Approaches to alleviate the problem:

I Machine LearningI Service Clustering


Applications in the Domain

I Anomalous Behaviour Detection

I Service Scheduling

I Application Profiling

I SLA Risk Assessment


Requirements and Existing Solutions

Requirements and Existing Solutions Uriarte, Tsaftaris and Tiezzi 7/29

Requirements

Characteristics Requirements

Security, Heterogeneity,Dynamism

Mixed Types ofFeatures

Large-Scale, Dynamism On-line Prediction

Large-Scale, Multi-AgentLoosely-Coupled

Parallelism

HeterogeneityLarge Number of

Features


Existing Approaches

I Solutions which handle mixed data types usually are notscalable (e.g. HClustream)

I Expert intervention is not feasible due to the dynamism

I Distance Metric Learning Approaches require labelled dataor are computationally expensive.


RF+PAM

RF+PAM Uriarte, Tsaftaris and Tiezzi 10/29

Random Forest

I Mixed Features

I Large Number of Features

I Efficient and Scales Well

I Easily Parallelizable


Random Forest

Clustering with Random Forest

I Originally Developed for Classification

I On-Line Random Forest

I Intrinsic Measure of Similarity

I Clustering Algorithm (e.g. PAM)


Similarity Using RF: Criteria


Problems

I Similarity Matrix (Big Memory Footprint)

I Re-cluster on Every New Observation


Solution: RF+PAM

I Off-line Training and On-line Prediction

I Similarity Learning and Standard Clustering


Solution: RF+PAM

Build Forest, Calculate Similarities, Cluster, Selectthe medoids and Store the references of the leaves.


Solution: RF+PAM

Parse service and Assign the cluster of the mostsimilar medoid to it.


Evaluation

Evaluation Uriarte, Tsaftaris and Tiezzi 18/29

Experiments

1. Cluster Quality

2. On-Line Prediction

3. Use Case


Cluster Quality

I Clustering quality compared to 2 otherapproaches (same dataset)

I Better results in all criteria

I Connectivity - Connectedness of the clustersI Dunn Index - Cluster density and SeparationI Silhouette - Confidence in the assignment


On-line Prediction

I On-Line vs Batch Mode

I K-Fold Cross-Validation

I Compared the Adjusted Rand Index (ARI) for 2datasets:

I Monitoring data of Google’s productionclouds - 12500 servers

I Requests of a grid of the Dutch UniversitiesResearch Testbed (DAS-2) - 200 servers


Results: ARI

K Google DAS-2

100 0.81 (0.32) 0.70 (0.23)

50 0.75 (0.19) 0.68 (0.17)

20 0.73 (0.09) 0.67 (0.11)

10 0.70 (0.06) 0.63 (0.09)

5 0.69 (0.05) 0.61 (0.07)


Use Case

I Schedules according to the Dissimilarity

I Similar services separated

I Algorithms:

1. Random2. Dissimilarity3. Isolated


Use Case

I 9 VMs

I Arrival Rates

I Types of Service

I Services’ SLA


Results


Conclusions

Conclusions Uriarte, Tsaftaris and Tiezzi 26/29

Summary

I We propose RF+PAM to alleviate the problemof limited knowledge in AC

I Validated RF+PAM with 3 Experiments

I Scheduling Algorithm


Future Works

I More Use Cases

I Better Implementation


Thank you!Questions?

Rafael Brundo [email protected]


Prune Trees

I Parsing is very fast and efficient

I Prune requires analysis (time consuming)


Retraining

Ratio of predictions/training services (user defined):

I Parallel training

I Trade-off between updating/prediction

Other solutions:

I Dissimilarity to Medoids

I On-line Clustering (Current Limitations andPrediction Speed)


Download - Service Clustering for Autonomic Clouds Using Random Forest

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