Design_Support_Cloud_Application_Redistribution

ResearchResearch

University of StuttgartUniversitätsstr. 3870569 StuttgartGermany

Phone +49-711-685 88337 Fax +49-711-685 88472

Santiago Gómez Sáez and Frank LeymannInstitute of Architecture of Application Systems

{gomez-saez,leymann}@iaas.uni-stuttgart.de

Design Support for Performance-aware Cloud

Application (Re-)Distribution

ESOCC PhD 2014

Rese

arc

h

© Santiago Gómez Sáez 2

Agenda

Motivation & Problem Statement Related Works Research Challenges Work in Progress

Approach Experiments

Research Plan

33© Santiago Gómez Sáez

Rese

arc

h

Motivation – Efficient Application Distribution

WebShop: WAR

Apache_Tomcat:Servlet_Container

Ubuntu10.04:Virt_Linux_OS

IBM_Server:Physical_Server

Product_DB:SQL_DB

MySQL: SQL_RDBMS_Server

AWS_EC2_m1.xlarge:

AWS_EC2

Ubuntu13.10:Virt_Linux_OS

MySQL: SQL_RDBMS_Server

AWS_RDS_mediumDB: AWS_RDS

MySQL: SQL_DBaaS

AWS_EC2_m1.medium:

AWS_EC2

Ubuntu13.0:Virt_Linux_OS

AWS_Elastic_BeansTalk: Application_Container

Partial vs. Complete Migration Multiple deployment options Multi-dimensional & Evolving problem Application workload behavior fluctuations Resources Demands Evolution

Performance-aware specification

Workload specification

alt_hosted_on

hosted_on

interacts-with

Andrikopoulos et al.: Optimal Distribution of Applications in the Cloud. In: Proceedings of CAiSE’14

Application specificAlternative node

44© Santiago Gómez Sáez

Rese

arc

h

Motivation - Perspectives & Approaches

Performance-aware Specification

Workload Model Derivation & Characterization

Application Workload Evolution Monitoring

Top-down Bottom-up

Gómez Sáez et al.: Towards Dynamic Application Distribution Support for Performance Optimization in the Cloud. Proceedings of CLOUD 2014.

55© Santiago Gómez Sáez

Rese

arc

hRelated Works

Cloud application design topology specification -> TOSCA, Blueprints, AWS Cloud Formation workload specification -> GT-CWSL

Cloud application design decision support MADCAT Palladio Component Model CloudMig MOCCA

Elasticity VM auto scaling techniques & algorithms proactive vs. reactive

Performance expectation vs. resource allocation specification

66© Santiago Gómez Sáez

Rese

arc

hResearch Challenges

How to partially or completely specify during the design phase the Cloud Application topology and its performance-aware aspects?

Alternative topologies space derivation & pruning How to analyze the application workload behavior &

evolution towards deriving & assessing

an efficient application (re-)distribution & profitable resources configuration?

77© Santiago Gómez Sáez

Rese

arc

h

Participants Application Developer

Application design and realization Application topology specification, e.g. using TOSCA

Application Cloud Distribution Design Support System Application (Re-)distribution towards proactively react to fluctuating workloads

Topology+ : enriched application topology model with performance awareness (e.g. expected throughput/operation or

component, resource consumption) application workload characteristics (e.g. probability matrix of operations)

Topology*: viable topology describing application distribution alternatives and specifying Cloud services dynamic resource adaptation configurations

WiP – Performance Aware Application (Re-)Distribution Process

88© Santiago Gómez Sáez

Rese

arc

hWiP – Performance Aware Application (Re-)Distribution Process

Re-distribution

Model Applicatio

n Topology

Enrich Topology

Model

Derive WL Model & Topology Alternativ

es

Deployment &

Production

Evaluate (Re-)

Distribution

Performance Evolution

Performance

Registration

Monitor &

Analysis

Functional annotation

Performance-aware annotation

Legend

• Requirements• Capabilities• Constraints

• Expected Performance• Workload Behavior• Topology+

• Workload Model Derivation• Cloud Offerings Matching• Similarity & utility-based Analysis• Topologies*

• Topology* instance• Synthetic Workload Generation• Distribution Performance Evaluation

• Demanded vs. Provided Performance• Workload behavior Patterns• Statistical Classification

Collaborative Loop• Proactiveness• Workload Fluctuation• Application Performance Evolution• Optimize performance vs. cost

tradeoffGómez Sáez et al.: Towards Dynamic Application Distribution Support for Performance Optimization in the Cloud. Proceedings of CLOUD 2014.

99© Santiago Gómez Sáez

Rese

arc

h

WebShop: WAR

…

Ubuntu10.04:Virt_Linux_OS

IBM_Server:Physical_Server

Product_DB:SQL_DB

MySQL: SQL_RDBMS_Server

AWS_EC2_m1.xlarge:

AWS_EC2

Ubuntu13.10:Virt_Linux_OS

AWS_RDS_xlargeDB: AWS_RDS

MySQL: SQL_DBaaS

alt_hosted_onhosted_on

WL Specification

Ubuntu10.04:Virt_Linux_OS

FlexiScale4vCPU:FlexiScale_VM

on-premise

Interacts-with

IaaS DBaaS

WiP - Experiments

Gómez Sáez et al.: Towards Dynamic Application Distribution Support for Performance Optimization in the Cloud. Proceedings of CLOUD 2014.

TPC-H Benchmark as the basis Workload model derivation Different workload characteristics

1010© Santiago Gómez Sáez

Rese

arc

hWiP – Experimental Results

LegendCL: Compute LowCM: Compute MediumCH: Compute High

1111© Santiago Gómez Sáez

Rese

arc

h

Research Plan

Flesh out the individual process tasks application topology specification application topology enrichment application workload analysis & generation relationship between developer preferences & application performance …

Performance experiments on application upper layers WordPress MediaWiki

Performance evaluation & analysis of the overall process distribution vs. redistribution re-configuration Santiago Gómez Sáez

E-mail: [email protected] of Architecture of Applications Systems (IAAS)University of Stuttgart (Germany)

12

Research

Thanks for your attention!!