Controlling Cloud Services Elasticity in Heterogeneous Clouds - UCC 2014 - CloudControl6

Controlling Cloud Services Elasticity

in Heterogeneous Clouds

Georgiana Copil, Daniel Moldovan, Hong-Linh Truong,

Schahram Dustdar

Vienna University of Technology

7th IEEE/ACM International Conference on Utility and Cloud Computing

6th Cloud Control Workshop

UCC 2014, 8-11 December, London 2

Overview

Background

Motivation

Approach

Results

Conclusions


Background

Control Application

Monitoring

Control

processes

Metrics

monitoring

information

Elasticity

requirements

Application

description

Aggregated

information

Elasticity

behavior

analysis

Application model: structural and runtime information


Motivation

Single application: the controller has an end-to-end

view of the application

Should we control it uniformly or leverage

cloud heterogeneity?

How can we take into consideration relationships among

components deployed on different cloud providers?


Research problems and approach

(Q) Should we control it uniformly or leverage cloud

heterogeneity?

– Heterogeneous control – understand a variety of control

primitives and protocols from a variety of providers

(Q) How can we take into consideration relationships

among components from different cloud providers?

– Model relationship types, and consider them in the control

process


Enforcement on heterogeneous clouds

Extend rSYBL to support heterogeneous cloud control

– Supporting multiple enforcement plugins simultaneously

– Parallelization of actions on multiple enforcement plugins


Application model for heterogeneous clouds

Extending the model for heterogeneous cloud application

control



Extending the infrastructure system description for

heterogeneous cloud application control

– Supporting the description of complex systems - various virtual

resources from multiple cloud providers



Elasticity relationships description for understanding control

dependencies

– Single way load dependency

• a change in the antecedent load causes

a similar change in consequent load

– Two way load dependency

• a change in one side causes a similar

change in the other

– Instantiation dependency

• for the instantiation of the consequent

the antecedent should to exist

– Data dependency

• the specified data should to be

transferred among the two

– Polynomial relationship

• a polynomial function describes the

connection between the two components


Decision on heterogeneous clouds

Elasticity

Requirements

Elasticity

Relationships

Metrics

monitoring

information

Decision process

Simulate

relationships

effects

Evaluate

control actionsAffected

componentsComponents

to re-evaluate

Application level controlCloud resources

control


Experiments - Settings

M2MDaaS:

STRATEGY CASE avgBufferSize<5 :

minimize(cost)

CONSTRAINT avgBufferSize<50 CONSTRAINT responseTime<50 ms

STRATEGY CASE

responseTime<40ms AND

throughput<20ops/s : scalein

Load relationship

requestsbufferSize


Experiments – Results

Action enforced on local

processing

due to elasticity requirement

Buffer size expected to increase

Compensation action enforced


Experiments – Results

Application cost evolving to accommodate varying load

on two providers


Conclusions and Future Work

Profiting from clouds heterogeneity

– We can fix a variety of issues using the same tools

– Controlling applications as a whole when running over multiple

clouds strengthens the decision capacity

Understanding application distribution

– Understanding relationships which may appear across

distributed components

Ongoing work

– Determining elasticity relationships

– Improving the analysis/decision process


Thank you!

Georgiana Copil

[email protected]://dsg.tuwien.ac.at/staff/ecopil/

Prototypes available http://tuwiendsg.github.io

Distributed Systems GroupVienna University of Technology

Austria

mailto:[email protected]

http://dsg.tuwien.ac.at/staff/ecopil/

http://tuwiendsg.github.io/


Backup slide – sensitivity analysis