The Cloud computing paradigm emerged by establishing innovative resources provisioning and consumption models. Together with the improvement of resource management techniques, these models have contributed to an increase in the number of application developers that are strong supporters of partially or completely migrating their application to a highly scalable and pay-per-use infrastructure. However, due to the continuous growth of Cloud providers and Cloud offerings, Cloud application developers nowadays must face additional application design challenges related to the efficient selection of such offerings to optimally distribute the application in a Cloud infrastructure. Focusing on the performance aspects of the application, additional challenges arise, as application workloads fluctuate over time, and therefore produce a variation of the infrastructure resources demands. In this research work we aim to define and realize the underpinning concepts towards supporting the optimal (re-)distribution of an application in the Cloud in order to handle fluctuating over time workloads.

1. Research
University of Stuttgart
Universitätsstr. 38
70569 Stuttgart
Germany
Phone +49-711-685 88337
Fax +49-711-685 88472
Santiago Gómez Sáez and Frank Leymann
Institute of Architecture of Application Systems
{gomez-saez,leymann}@iaas.uni-stuttgart.de
Design Support for
Performance-aware Cloud
Application (Re-)Distribution
ESOCC PhD 2014

2. Research
© Santiago Gómez Sáez 2
Agenda
 Motivation & Problem Statement
 Related Works
 Research Challenges
 Work in Progress
 Approach
 Experiments
 Research Plan

3. 33© Santiago Gómez Sáez
Research
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_mediu
mDB: 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 specific
Alternative node

4. 44© Santiago Gómez Sáez
Research
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.

5. 55© Santiago Gómez Sáez
Research
Related 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

6. 66© Santiago Gómez Sáez
Research
Research 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?

7. 77© Santiago Gómez Sáez
Research
 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

8. 88© Santiago Gómez Sáez
Research
WiP – Performance Aware Application (Re-)Distribution Process
Re-distribution
Model
Application
Topology
Enrich
Topology
Model
Derive WL
Model &
Topology
Alternatives
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
tradeoff
Gómez Sáez et al.: Towards Dynamic Application Distribution Support for Performance Optimization in the Cloud. Proceedings of CLOUD 2014.

9. 99© Santiago Gómez Sáez
Research
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_xlarge
DB: AWS_RDS
MySQL:
SQL_DBaaS
alt_hosted_on
hosted_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

10. 1010© Santiago Gómez Sáez
Research
WiP – Experimental Results
Legend
CL: Compute Low
CM: Compute Medium
CH: Compute High

11. 1111© Santiago Gómez Sáez
Research
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: gomez-saez@iaas.uni-stuttgart.de
Institute of Architecture of Applications Systems (IAAS)
University of Stuttgart (Germany)

12. 12
Thanks for your attention!!