supporting autonomic management of clouds: service-level-agreement, cloud monitoring and similarity...

Supporting Autonomic Management of

Clouds: Service-Level-Agreement, Cloud

Monitoring and Similarity Learning

byRafael Brundo Uriarterafael.uriarte@imtlucca.it

Under the Supervision of:Prof. Rocco De Nicola and Prof. Francesco Tiezzi

Doctoral Thesis Defense - March 30th, 2015 - Lucca, Italy

Contents

1 Introduction

2 SLA for Clouds

3 Cloud Monitoring

4 Similarity Learning

5 Polus Framework

6 Conclusions

Rafael Brundo Uriarte 1/51

Introduction

Introduction Rafael Brundo Uriarte 2/51

Cloud Computing

Introduction Rafael Brundo Uriarte 3/51

Cloud Characteristics

I Services

I Heterogeneity

I Virtualization

I Large-Scale

I Complexity

Introduction Rafael Brundo Uriarte 4/51

Autonomic Computing

Introduction Rafael Brundo Uriarte 5/51

Autonomic Computing

Introduction Rafael Brundo Uriarte 6/51

Knowledge for the Self-Management

I Policies

I Service Definition and Objectives

I Status of the Cloud and Services

I Specific Knowledge

Introduction Rafael Brundo Uriarte 7/51

Challenge and Scope

Introduction Rafael Brundo Uriarte 8/51

Research Questions

Research Question 1How to describe services and their objectives in the cloud

domain?

Research Question 2What is data, information, knowledge and wisdom in the

autonomic cloud domain?

Research Question 3How to collect and transform operational data into useful

knowledge without overloading the autonomic cloud?

Introduction Rafael Brundo Uriarte 9/51

Research Questions

Research Question 4How to produce a robust measure of similarity for services in

the domain and how can this knowledge be used?

Research Question 5How to integrate different sources of knowledge and feed the

autonomic managers?

Introduction Rafael Brundo Uriarte 10/51

SLA for Clouds

SLA for Clouds Rafael Brundo Uriarte 11/51

Service-Level-Agreement (SLA)

I Contract

I Service Description

I Quality-of-Service

I Formalism

I Guarantees!

SLA for Clouds Rafael Brundo Uriarte 12/51

SLA for Cloud Computing - SLAC

I Domain Specific

I Multi-Party

I Deployment Models

I Formalism

I Ease-of-Use

SLA for Clouds Rafael Brundo Uriarte 13/51

Yet Another SLA Definition Language?

Features WSOL WSLA SLAng WSA SLA* SLAC

General Deployment Models � � � � � �

Broker Support - - - - - �

Business

Pricing Schemes � � - � � �

Formal Semantics - - � - - �

Verification - - � - - �

“�” feature covered“�” feature partially covered

“-” no support

SLA for Clouds Rafael Brundo Uriarte 14/51

Main Concepts

I Predefined Metrics - Involved Parties and Unit

I Intervals for Metrics - Template and Variations

I Groups - Multiple Service, Community Cloud

I Constraint Solving Problem

SLA for Clouds Rafael Brundo Uriarte 15/51

Example

SLA for Clouds Rafael Brundo Uriarte 16/51

Business Aspects

I Business Actions

I Flat and Variable Models

I Pricing Schemes - Exchange, Auction, Tender,Bilateral, Fixed, Posted

SLA for Clouds Rafael Brundo Uriarte 17/51

Implementation

I Editor for SLAs (Ecplise-based using Xtext)

I SLA Evaluator (Z3 Solver)

I Integration with the Monitoring System

SLA for Clouds Rafael Brundo Uriarte 18/51

Cloud Monitoring

Cloud Monitoring Rafael Brundo Uriarte 19/51

DIKW in the Domain

I Data

I Information

I Knowledge

I Wisdom

Cloud Monitoring Rafael Brundo Uriarte 20/51

Cloud Monitoring

The Role of the Monitoring System in Clouds:

I Collect data and Provide Information andKnowledge

I No Wisdom - Related to Decision-Making

I Sensor of MAPE-K Loop

Cloud Monitoring Rafael Brundo Uriarte 21/51

Related Works

Property PCMONS Monalytics Lattice Wang

Cloud � - - -

Autonomic Integration - - - -

Scalability - � � �

Adaptability - � - �

Resilience - - - -

Timeliness - � - �

Extensibility � � � �

“�” feature covered“�” feature partially covered

“-” no support

Cloud Monitoring Rafael Brundo Uriarte 22/51

Panoptes

I Multi-agent system

I Monitoring in different levels

I Monitoring Modules - What needs to bemonitored and how to process the data

Cloud Monitoring Rafael Brundo Uriarte 23/51

Architecture

Cloud Monitoring Rafael Brundo Uriarte 24/51

Architecture

Communication:

I Publish/Subscribe

I Private Message

Adaptativeness:

I Priority for Modules

I Change of Roles

Cloud Monitoring Rafael Brundo Uriarte 25/51

Architecture: Autonomic Integration

I Urgency Mechanism

I Decentralised Architecture

I On-the-Fly Configuration

I Multiple Abstractions

Cloud Monitoring Rafael Brundo Uriarte 26/51

Experiments

I Self-Protection System

I Urgency Mechanism

I Scalability

Cloud Monitoring Rafael Brundo Uriarte 27/51

Similarity Learning

Similarity Learning Rafael Brundo Uriarte 28/51

Specific Knowledge

Generation of Knowledge for a Specific Purpose, i.e.not applicable in all clouds. For example, similarity.

But what is similarity?

I How much an object (service) resembles other

Similarity Learning Rafael Brundo Uriarte 29/51

Applications of Similarity

Cluster Services:

I Group Similar Services

I Different Algorithms(K-Means, PAM, EM)

Applications in the Domain:

I Anomalous Behaviour Detections

I Service Scheduling

I Application Profiling

I SLA Risk Assessment

Similarity Learning Rafael Brundo Uriarte 30/51

Domain Requirements

I Categorical Characteristics of Services

I On-line Prediction

I Large Number of Characteristics

I Fast Prediction

Similarity Learning Rafael Brundo Uriarte 31/51

Random Forest

Clustering with Random Forest

I Originally Developed for Classification

I Calculate the Similarity

I Clustering Algorithm (PAM)

Similarity Learning Rafael Brundo Uriarte 32/51

Similarity Using RF: Criteria

Similarity Learning Rafael Brundo Uriarte 33/51

Problems

I Similarity Matrix (Big Memory Footprint)

I Re-cluster on Every New Observation

I Cannot be Used in the Domain

Similarity Learning Rafael Brundo Uriarte 34/51

Solution: RF+PAM

Similarity Learning Rafael Brundo Uriarte 35/51

Solution: RF+PAM

Similarity Learning Rafael Brundo Uriarte 36/51

Experiments

I Compared the performance of our algorithm toother 2 methodologies

I Compared the performance of RF+PAM withthe standard off-line similarity learning

I Use Case:

I Scheduler deploys together the mostdissimilar services

I Similarity based on their SLAs

Similarity Learning Rafael Brundo Uriarte 37/51

Polus Framework

Polus Framework Rafael Brundo Uriarte 38/51

Polus Framework

Polus Framework Rafael Brundo Uriarte 39/51

Use Case

Polus Framework Rafael Brundo Uriarte 40/51

Use Case

Polus Framework Rafael Brundo Uriarte 41/51

Use Case

Polus Framework Rafael Brundo Uriarte 42/51

Conclusions

Conclusions Rafael Brundo Uriarte 43/51

Summary

Conclusions Rafael Brundo Uriarte 44/51

Research Questions

Research Question 1How to describe services and their objectives in the cloud

domain?

SLAC

Research Question 2What is data, information, knowledge and wisdom in the

autonomic cloud domain?

DIKW Hierarchy

Research Question 3How to collect and transform operational data into useful

knowledge without overloading the autonomic cloud?

Panoptes

Conclusions Rafael Brundo Uriarte 45/51

Research Questions

Research Question 4How to produce a robust measure of similarity for services in

the domain and how can this knowledge be used?RF+PAM

Research Question 5How to integrate different sources of knowledge and feed the

autonomic managers?Polus Framework

Conclusions Rafael Brundo Uriarte 46/51

Limitations

I Intelligence of Autonomic Managers

I Wide Range of Specific Knowledge

I Off-line Training of RF+PAM

Conclusions Rafael Brundo Uriarte 47/51

Contributions

I A theoretical and practical framework for thegeneration and provision of knowledge for theautonomic management of clouds (PolusFramework):

I SLAC - SLA Definition and EvaluationI Panoptes - MonitoringI RF+PAM - Similarity Learning

Conclusions Rafael Brundo Uriarte 48/51

Publications

1. R. B. Uriarte, S. Tsaftaris and F. Tiezzi. Service Clustering forAutonomic Clouds Using Random Forest. In Proc. of the 15thIEEE/ACM CCGrid [In Press], 2015.

2. R.B. Uriarte, F. Tiezzi, R. De Nicola, SLAC: A FormalService-Level-Agreement Language for Cloud Computing. In IEEE/ACM7th International Conference on Utility and Cloud Computing (UCC),2014.

3. R.B. Uriarte, C.B. Westphall, Panoptes: A monitoring architecture andframework for supporting autonomic Clouds, In Proc. of the 16thIEEE/IFIP Network Operations and Management Symposium (NOMS),2014.

4. R.B. Uriarte, S.A. Chaves, C.B. Westphall, Towards an Architecture forMonitoring Private Clouds. In IEEE Communications Magazine, 49,pages 130-137, 2011.

Conclusions Rafael Brundo Uriarte 49/51

Future Works

I Dynamic SLAs

I Negotiation of SLAs

I Cloud Case Study

Conclusions Rafael Brundo Uriarte 50/51

Thank you!Questions?

Rafael Brundo Uriarterafael.uriarte@gmail.com

Conclusions Rafael Brundo Uriarte 51/51

SLAC - Expressivity

I Core Language

I Extensions - Business Aspects

I Formal Definition for Extensions

Conclusions Rafael Brundo Uriarte 51/51

SLAC - Implementation

Compatibility only with OpenNebula

I Toy Implementation

I Easily adapted

Conclusions Rafael Brundo Uriarte 51/51

SLAC - Cloud Metrics

DTMF Cloud Computing Service MetricsDescription

I Recent Document (Still a Draft)

I Creation of a Model for the Definition of Metrics

I The SLAC Metrics can be Adapted for thisModel

Conclusions Rafael Brundo Uriarte 51/51

SLAC Violation

I Violation and Penalty are Separated Concepts

I “Violation” Concept Flexible

I Easy to Understand

Conclusions Rafael Brundo Uriarte 51/51

Panoptes - Scalability

I Designed to be scalable

I Adapt itself

I Experiments suggest it is scalable

I More experiments for future works

Conclusions Rafael Brundo Uriarte 51/51

Panoptes - Analysis of Apache Broklyn

I Not Focused on Monitoring

I Does Not Process the Data

Conclusions Rafael Brundo Uriarte 51/51

Panoptes - Analysis with CSPARQL

I Data is not Decorated (e.g. RDF)

I Impact of Decorated Monitoring Data(Scalability)

I Very Interesting Option

Conclusions Rafael Brundo Uriarte 51/51