A comparison of two-level and multi-level modelling for cloud-based applications

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Technology for a better society

A comparison of two-level

and multi-level modelling

for cloud-based applications

Alessandro Rossini

Nikolay Nikolov

Juan de Lara

Esther Guerra

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22 Feb 2014

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users

register to Telegram

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Self-adaptive cloud-based applications

Cloud-based applications that self-adapt to

changes in environment and requirements

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Cloud-based applications

Virtualisa)on

Servers

Storage

Network

IaaS

Run-‐)me

OS

PaaS

Applica)ons

Data

SaaS

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Models@run-time

Reasoning engine

Models

@run-‐-me

Target

model

Diﬀ

Adapta0on

engine

Current

model

Running system

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Deployment model:

design-time

[loca)on: NO]

haProxy1

sensApp1

[instances range: 1..4]

tomcat1

ubuntu1

Types

HAProxy

SensApp

Tomcat

Ubuntu

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Deployment model:

run-time

[instances range: 1..4, instances=3]

OpenStack

[loca)on: NO]

OpenStack

[loca)on: NO]

haProxy1

sensApp1

[instances range: 1..4, instances=2]

tomcat1

ubuntu1

haProxy1

sensApp1

tomcat1

ubuntu1

OpenStack

[loca)on: NO]

OpenStack

[loca)on: NO]

haProxy1

sensApp11

tomcat11

ubuntu11

[CPU load = 99]

sensApp12

tomcat12

ubuntu12

[CPU load = 90]

haProxy1

sensApp11

tomcat11

ubuntu11

[CPU load = 80]

sensApp12

tomcat12

ubuntu12

[CPU load = 70]

sensApp13

tomcat13

ubuntu13

[CPU load = 60]

Before adapta)on AXer adapta)on

Before adapta)on

AXer adapta)on

Implicit instances

_{Explicit instances}

If CPU load of a VM is above 90%, then scale out

Types

HAProxy

SensApp

Tomcat

Ubuntu

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CloudML abstract syntax:

two levels

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model

metamodel

CompType

hostedCompType

*

type

CompInst

currentInst: Int minInst: Int maxInst: Int

hostedCompInst

*

VMType

type

VMInst

cpuLoadAvg: Double

SensApp

ontological

_typing

sensApp1

currentInst = 2 minInst = 1 maxInst = 4

st1

_{currentInst = 2}

tomcat1

minInst = 1 maxInst = 4

tu1

ubuntu1

currentInst = 2 minInst = 1 maxInst = 4 cpuLoadAvg = 94.5

ST

_Tomcat

TU

_Ubuntu

linguis1c

typing

C1: self.hostedCompType-‐>forAll(t | CompInst.allInstances()-‐>exists(i | i.type = self and

i.hostedCompInst-‐>exists(ci | ci.type = t))) C2: self.currentInst >= self.minInst C3: self.maxInst <> -‐1 implies self.currentInst <= self.maxInst C4: self.hostedCompInst-‐>forAll(i | self.type.hostedCompType -‐>includes(i.type))

Deployment model:

implicit instances

OpenStack*

[loca.on:*NO]* haProxy1* sensApp1*[instances range: 1..4,

instances=2]

tomcat1* ubuntu1*

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model

metamodel

CompType

hostedCompType

*

type

_CompInst

hostedCompInst

*

VMType

/cpuLoadAvg: Double = let loads:Set = VMInst.allInstances()-‐>select(m | m.type=self) -‐>collect(cpuLoad) in loads-‐>sum() / loads-‐>size()

type

VMInst

cpuLoad: Int

SensApp

_sensApp12

st1

tomcat12

_tu1

ubuntu12

cpuLoad = 90

ST

_Tomcat

TU

Ubuntu

/cpuLoadAvg = 94.5 C5: self.hostedCompType-‐>forAll(t | CompInst.allInstances()-‐>exists(i | i.type = self and

i.hostedCompInst-‐>exists(ci | ci.type = t))) C6: self.hostedCompInst-‐>forAll(i | self.type.hostedCompType -‐>includes(i.type))

Deployment model:

explicit instances

OpenStack*

[loca.on:*NO]* haProxy1* sensApp11* tomcat11* ubuntu11* [CPU load = 99] sensApp12* tomcat12* ubuntu12* [CPU load = 90]

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Deployment model:

implicit

and

explicit inst.

model

metamodel

CompType

hostedCompType * type

CompTemp

currentInst:3Int minInst:3Int maxInst:3Int hostedCompTemp *

VMType

type

VMTemp

/cpuLoadAvg:3Double3=3…

SensApp

ontological_typing

sensApp1

currentInst =32 minInst =31 maxInst =34 st1

tomcat1

currentInst =32 minInst =31 maxInst =34 tu1

ubuntu1

currentInst =32 minInst =31 maxInst =34 /cpuLoadAvg =394.5 ST

_Tomcat

TU

_Ubuntu

linguistic typing type

_CompInst

hostedCompInst * type

VMInst

cpuLoad:3Int sensApp12 st12

tomcat12

_tu12

ubuntu12

cpuLoad =390 C1 C2,% C3,"C4,% C5 C6 OpenStack* [loca.on:*NO]* haProxy1* sensApp1*[instances range: 1..4,

instances=2] tomcat1* ubuntu1* OpenStack* [loca.on:*NO]* haProxy1* sensApp11* tomcat11* ubuntu11* [CPU load = 99] sensApp12* tomcat12* ubuntu12* [CPU load = 90]

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CloudML abstract syntax:

multiple levels

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@3

@2

@1

@0

Language

definition

Component

types definition

Deployment

model:

implicit inst.

Deployment

model:

explicit inst.

Component

/currentInst@2: int = $ self.allInstances().size() $

*

hostedComp

VirtualMachine

cpuLoad@3: int

/cpuLoadAvg@2: double =

$ self.allInstances()-‐>collect(cpuLoad )-‐>sum()/self.currentInst $

SensApp: Component *

Tomcat: Component

*

Ubuntu: VirtualMachine

sensApp1:

SensApp [1..4]

/currentInst = 2

1..1

_tomcat1:

Tomcat [1..*]

/currentInst = 2

1..4

_{ubuntu1: Ubuntu [1..*]}

/cpuLoadAvg = 94.5

/currentInst = 2

sensApp11: sensApp1

tomcat11: tomcat1

ubuntu11: ubuntu1

cpuLoad = 99

sensApp12: sensApp1

tomcat12: tomcat1

ubuntu12: ubuntu1

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Criteria

• Size of language definition

• Complexity of OCL constraints

• Precision

• Extensibility

• Flexibility

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Size of language definition

@3

Language

definition:

Multi-level

Component

/currentInst@2: int = $ self.allInstances().size() $

*

hostedComp

VirtualMachine

cpuLoad@3: int

/cpuLoadAvg@2: double =

$ self.allInstances()-‐>collect(cpuLoad )-‐>sum()/self.currentInst $

Language

definition:

Two-level

CompType( hostedCompType * type CompTemp( currentInst: Int minInst: Int maxInst: Int hostedCompTemp * VMType( type VMTemp( /cpuLoadAvg: Double = … type _CompInst( hostedCompInst * type VMInst( cpuLoad: Int C1! C2,%C3,!C4,%C5% C6%

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CloudML two-level tooling

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CloudML multi-level tooling

22

MetaDepth

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MetaDepth

• Potency-based multi-level modelling

• Textual modelling

• Integration with Epsilon languages (adapted to

multi-level):

– 

Epsilon Object Language

– 

Epsilon Transformation Language

– 

Epsilon Generation Language

• Analysis of conflicts between integrity constraints at

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CloudML in MetaDepth

// Level 3: Language definition

Model CloudML@3 { Node Component {

hostedComp: Component[*];

/currentInst@2: int = $ self.allInstances().size() $; }

Node VirtualMachine: Component { cpuLoad@3: int;

/cpuLoadAvg@2: double = $ self.allInstances().collect ( c | c.cpuLoad ).sum() / self.currentInst $ ;

} }

// Level 2: Component types definition

CloudML ComponentTypes { Component Tomcat { tomcatHosts: SensApp[*]{hostedComp}; } Component SensApp {} VirtualMachine Ubuntu { ubuntuHosts: Tomcat[*]{hostedComp}; } }

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CloudML in MetaDepth

// Level 1: Deployment model with implicit instances

ComponentTypes DeploymentModel { SensApp sensApp1 [1..4] {} Tomcat tomcat1 [1..*] {

t1Hosts: sensApp1[1] {tomcatHosts}; }

Ubuntu ubuntu1 [1..*] {

u1Hosts: tomcat1[1..4] {ubuntuHosts}; }

}

// Level 0: Deployment model with explicit instances

DeploymentModel instances { sensApp1 sensApp11;

sensApp1 sensApp12;

tomcat1 tomcat11 { t1Hosts = [sensApp11]; } tomcat1 tomcat12 { t1Hosts = [sensApp12]; }

ubuntu1 ubuntu11 { cpuLoad = 99; u1Hosts = [tomcat11]; } ubuntu1 ubuntu12 { cpuLoad = 90; u1Hosts = [tomcat12]; } }

A comparison of two-level and multi-level modelling for cloud-based applications

s

Technology for a better society