ADVISE: a Framework for Evaluating

Cloud Service Elasticity Behavior

Georgiana Copil1, Demetris Trihinas

2, Hong−Linh Truong1, Daniel Moldovan

1,

George Pallis2, Schahram Dustdar

1, Marios Dikaiakos

2

1Distributed Systems Group, Vienna University of Technology

2Computer Science Department, University of Cyprus

12th International Conference on Service Oriented Computing

ICSOC 2014, 5 November, Paris 2

Overview

Motivation

Evaluating Cloud Service Behavior

– Learning process

– Determining expected elasticity behavior

Experiments

Conclusions and Future Work

ICSOC 2014, 5 November, Paris 3

Motivation – Cloud service runtime evolution

Complex

Cloud

Service

Elastic

Cloud

Service

(running)Deployment

process

Elasticity

control

process

Elasticity Control

Processes

What would be

the elasticity

behavior?

Elasticity

requirements

Elasticity controller

ICSOC 2014, 5 November, Paris 4

Motivation – Cloud service runtime evolution

Elasticity control

process enforced

now

Which will be the behavior?

ICSOC 2014, 5 November, Paris 5

Motivation – Cloud service runtime evolution

Possible requirements

violations

Elasticity control

process enforcedExpected impact

Expected cool-off

period

now

Which will be the behavior?

Which elasticity control process is most appropriate?

How a control process will affect metrics, e.g., throughput, of the

overall service and individually on each part of the cloud service?

ICSOC 2014, 5 November, Paris 6

Motivation – Cloud service behavior

Cloud service behavior is complex and can depend on:

– The structure of the cloud service

– The runtime resources used

– The workload of the cloud service

– The control processes enforced, e.g., by the controller

Capturing & using these types of information for

evaluating elasticity behavior

Service

Topology 1

Unit 1

Unit 2

Topology 2

Unit 3

Unit 4

𝑉𝑀𝑥1 𝑉𝑀𝑥2𝑉𝑀𝑥3 𝑉𝑀𝑥𝑛

ICSOC 2014, 5 November, Paris 7

Approach

Input:

– Cloud service structure

– Monitoring information of different service parts (e.g., service

units, service topologies)

– Elasticity control process 𝐸𝐶𝑃𝑖

Expected output:

– Metrics evolution, in time, for different service parts and 𝐸𝐶𝑃𝑠

Main mechanism:

– Creating behavior clusters

– Computing closest behavior centroids

ICSOC 2014, 5 November, Paris 8

Gathering information

Select relevant timeseries where 𝐸𝐶𝑃𝑖 was enforced before

𝐸𝐶𝑃𝑖 enforcement

Metric measurement

Relevant timeseries

ICSOC 2014, 5 November, Paris 9

Clustering elasticity behaviors

Transform relevant timeseries to multi-dimensional

points

Time𝑡1 𝑡2 𝑡𝑛…

Metric

𝑚𝑥

𝑀𝑒𝑡𝑟𝑖𝑐𝑉𝑎𝑙 (𝑡1)

𝑀𝑒𝑡𝑟𝑖𝑐𝑉𝑎𝑙 (𝑡2)

𝑀𝑒𝑡𝑟𝑖𝑐𝑉𝑎𝑙 (𝑡3)

𝑀𝑒𝑡𝑟𝑖𝑐𝑉𝑎𝑙 (𝑡4)

…

𝑀𝑒𝑡𝑟𝑖𝑐𝑉𝑎𝑙 (𝑡𝑛)

Behavior Point

BP

K-means𝐶𝑙𝑢𝑠𝑡𝑒𝑟1𝑚𝑥

𝐶𝑙𝑢𝑠𝑡𝑒𝑟2𝑚𝑥

𝐶𝑙𝑢𝑠𝑡𝑒𝑟𝑐 𝑚𝑥𝐶1𝑚𝑥

𝐶2𝑚𝑥

𝐶𝑐 𝑚𝑥

ICSOC 2014, 5 November, Paris 10

Computing expected behavior

𝐶𝑙𝑢𝑠𝑡𝑒𝑟1𝑚𝑥

𝐶1𝑚𝑥

𝐶𝑙𝑢𝑠𝑡𝑒𝑟2𝑚𝑥

𝐶2𝑚𝑥

𝐶𝑙𝑢𝑠𝑡𝑒𝑟1𝑚𝑦

𝐶1𝑚𝑦

𝐶𝑙𝑢𝑠𝑡𝑒𝑟𝑝 𝑚𝑦

𝐶𝑝𝑚𝑦

𝐶𝑙𝑢𝑠𝑡𝑒𝑟1𝑚𝑥 𝐶𝑙𝑢𝑠𝑡𝑒𝑟2𝑚𝑥𝐶𝑙𝑢𝑠𝑡𝑒𝑟𝑟 𝑚𝑥

𝐶𝑙𝑢𝑠𝑡𝑒𝑟1𝑚𝑦a b -

𝐶𝑙𝑢𝑠𝑡𝑒𝑟𝑝 𝑚𝑦c - d

Co-occurrence matrix

Current values

𝐵𝑃𝑚𝑦𝐵𝑃𝑚𝑥

Compute centroids

closest to the 𝐵𝑃𝑖

𝐶𝑝𝑚𝑦

𝐶1𝑚𝑥

Transform

to timeseries

𝑚𝑥𝑚𝑦

𝐶𝑙𝑢𝑠𝑡𝑒𝑟𝑟 𝑚𝑥

𝐶𝑟 𝑚𝑥

ICSOC 2014, 5 November, Paris 11

Experiment Settings [1/3]

Setting:

– M2M service

– Video Service

ICSOC 2014, 5 November, Paris 12

Experiment Settings [2/3]

Setting:

– Running on public Flexiant cloud FCO

– MELA & JCatascopia for monitoring cloud services

– Randomly apply ECPs of random type for collecting behavioral

information

– “Interesting” metrics

ICSOC 2014, 5 November, Paris 13

Experiment Settings [3/3]

ICSOC 2014, 5 November, Paris 14

Experiments – Video Service

Video Service – effect of 𝐸𝐶𝑃1 on Application Server

𝐸𝐶𝑃1 - scale in application server tier – select instance to remove,

stop the video streaming service, remove instance from load

balancer, stop JCatascopia monitoring agent, delete instance

ICSOC 2014, 5 November, Paris 15

Experiments – M2M Service [1/2]

M2M Service – effect of 𝐸𝐶𝑃7 on the entire cloud service

𝐸𝐶𝑃7 - scale in data node service unit – copy data from the instance

to be removed, remove recursively virtual machine

ICSOC 2014, 5 November, Paris 16

Experiments – M2M Service [2/2]

M2M Service – effect on Data End Controller of enforcing 𝐸𝐶𝑃8𝐸𝐶𝑃8 - scale out data node service unit – create new network

interface, create new instance, assign token to node, set cluster

controller

ICSOC 2014, 5 November, Paris 17

Experiments –

Quality of Results

𝑉𝑎𝑟𝑖𝑎𝑛𝑐𝑒𝑚

= 𝑛𝑏𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛𝑠 𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛𝑆𝑖𝑧𝑒(𝑒𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑑𝑀𝑒𝑡𝑟𝑖𝑐𝑚 − 𝑜𝑏𝑠𝑒𝑟𝑣𝑒𝑑𝑀𝑒𝑡𝑟𝑖𝑐𝑚)

2

𝑛𝑏𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑖𝑜𝑛𝑠 − 1

The more random the workload, of the service,

the more difficult to estimate the behavior Lower abstraction layer

=> better estimations

Complex,

unpredictable

metrics => very low

degree of accuracy

ICSOC 2014, 5 November, Paris 18

Conclusions and Future Work

Conclusions

– When controlling a complex cloud service, we need to consider

the impact elasticity control processes have on different service

parts

– ADVISE is indeed able to "advise" elasticity controllers about

cloud service behavior

Future work

– Integrating with rSYBL (https://github.com/tuwiendsg/rSYBL)

– Adapting the control mechanisms of rSYBL to use such

information

ADVISE

– More experiments available at http://tuwiendsg.github.io/ADVISE

– Prototype https://github.com/tuwiendsg/ADVISE

ICSOC 2014, 5 November, Paris 19

Thank you!

Georgiana Copil

e.copil@dsg.tuwien.ac.athttp://dsg.tuwien.ac.at/staff/ecopil/

Distributed Systems GroupVienna University of Technology

Austria