challenges in establishment of open cloud markets

Challenges in Establishment of Open Cloud Markets

Ivona Brandic

[email protected]

Vienna University of Technology, Austria

HPC Advisory Council Switzerland Conference 2012

March 13-15, Lugano

Goods, Standardization, Markets

Challenging scenarios in electronic marketplaces

specification of non-standardized goods

dynamic evolution of user requirements

high diversity of user requirements

Infrastructure

STANDARDIZED GOODS

VIRTUAL GOODS

Applications

2

Characteristics of Electronic Markets: Market Liquidity

Credits: Marcel Risch, Jörn Altmann, Ivona Brandic

3

Today’s Cloud landscape

4

low market liquidity, stability and sustainability

large variety in services fragmented dynamic participation

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Adding autonomic capabilities solves these problems!

5

1. Definition of virtual products 2. Self-aware markets

Infrastructure

STANDARDIZED GOODS

VIRTUAL GOODS

Applications

Challenges

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Market

place

?

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(1) Definition of virtual products

(2) Self-aware markets Rules Size

Connectivity

….

Cloud

Product 1

Cloud

Product 2

Cloud

Product x

Goals

Characteristics

Liquidity

… From Autonomic Computing to Green ICT and self-aware Markets

Knowledge

Sensor Actuator

monitoring

analysis planning

execution

QoS Metric

Protocol

QoS Metric

Protocol

Evaluation

Service

Compositions

Mapping Strategies

Negotiation using

VieSLAF framework

Autonomic Manager

QoS Example

Definition of Virtual Products: The SLA mapping approach

Market Directory

Private template

Service consumer

Private template

Service provider

Public SLA template

MMMMM

Market Directory

Private template

Service consumer

Step 1: Assign service to a template

Private template

Service provider

Public SLA template

MMMMM

Market Directory

Private template

Service consumer


Private template

Service provider

Public SLA template

MMMMM

MM

Step 2: Define mappings

Market Directory

Step 3: Lookup

Private template

Service consumer


Private template

Service provider

Public SLA template

MMMMM

MM


Market Directory

Step 3: Lookup

Private template

Service consumer


Private template

Service provider

Public SLA template

MMMMM

MM


MM


Market Directory

Step 3: Lookup

Private template

Service consumer


Private template

Service provider

Public SLA template

SLA TemplateAdaptation Component

MMMMM

MM


MM

Step 4: Define mappingsStep 5: Adaptpublic templates

Market Directory

Step 3: Lookup

Private template

Service consumer


Private template

Service provider

Public SLA template


MMMMM

SLA MappingModification Component

MM


MM

Step 4: Define mappingsStep 5: Adaptpublic templates

Step 6: ModifySLA mappings

Market Directory

Step 3: Lookup

Private template

Service consumer


Private template

Service provider

Public SLA template


MMMMM

SLA MappingModification Component

MM


MM


Step 7: Assign new templates to usersStep 7: Assign new templates to users

Step 5: Adaptpublic templates

Step 6: ModifySLA mappings

8

Changing properties or

adding/deleting parameters

Service requirements

Standardized goods

Credits: Ivan Breskovic

<xsl:template match="/ns:SLA/

ns:ServiceDefinition/

ns:WSDLSOAPOperation/

ns:Metric[@name='calculatePrice']/

ns:Function">

<ns:Function xsi:type="Times

resultType="float">

<ns:Operand>

<xsl:copy>

<xsl:copy-of

select="@*|node()"/>

</xsl:copy>

</ns:Operand>

<ns:Operand>

<ns:FloatScalar>

0.68096718

</ns:FloatScalar>

</ns:Operand>

</ns:Function>

</xsl:template>

Local

WSLA

template

Rule

From local

to remote

Rule

From local

to remote

Rule

from local

to remote XSLT

transformation

Remote

WSLA

template

XSLT

transformation

Rule

From local

to remote

Rule

From local

to remote

Rule from

remote

to local

+

+

XSLT

transformation

Rule

From local

to remote

Rule

From local

to remote

Rule from

remote

to local

XSLT

transformation

Local

WSLA

template

Rule

From local

to remote

Rule

From local

to remote

Rule

from local

to remote

+

+

Service

consumer

Service

provider

SLA Mapping Scenario

Step 1: Public SLA template management

Medical apps

Initial public

SLA template

Medical apps

Step 1:Group bytemplatestructures

Initial public

SLA template

Medical apps


Cardiology

Oncology

Surgery

Initial public

SLA template

Medical apps


Cardiology

Oncology

Surgery

Step 2: Group by SLO values

Initial public

SLA template

Medical apps


Cardiology

Oncology

Surgery


Initial public

SLA template

Medical apps


Cardiology

Oncology

Surgery

Step 3: Learn

Step 3: Learn


Initial public

SLA template

Medical apps


Cardiology

Oncology

Surgery

Step 3: LearnData-intensive surgical apps

Step 3: Learn


Simplesurgical apps

Initial public

SLA template

Medical apps


Cardiology

Oncology

Surgery

Step 3: LearnData-intensive surgical apps

Step 3: Learn


Simplesurgical apps

Initial public

SLA template

New public

SLA templates

10

Requirements: 1. function for finding distance between clustering items 2. methods for computing cluster centroids

Credits: Ivan Breskovic

Evolution of Public SLA Templates: Clustering Algorithms

Clustering

Finding a structure in a collection of unlabeled data

Goal: determine the intrinsic grouping in a set of unlabeled data

Algorithms

DBSCAN based on density distribution

of nodes

finds an appropriate number of clusters

two input values: distance e and set of points that are not farther away than min distance

k-Means assigns observations to

clusters with closest means

New means to centroids

Stops when there a no significant changes

11 Source:

http://home.dei.polimi.it/matteucc/Clustering/tutorial_html/

Computing distance between items

A. distance between template structures

B. distance between template objectives

12

dp (T1,T2 ) =

0 if propertiesof p are same in T1 and T2

1 if T1 orT2 does not contain p

1 if only one property of p differs in T1 and T2

2 if both properties of p differ in T1 and T2

ì

í

ïï

î

ïï

dH (X,Y ) = max{supxÎX

infyÎYd(x, y),sup

yÎY

infxÎXd(x, y)}

dH (VT1,VT2

) = max( x1 - x2 , y1 - y2 )

dV (T1,T2 ) =dH (VT1

,VT2) - min(dH ,V )

max(dH ,V ) - min(dH ,V )

x1 y1 x2 y2

VT1 VT2

Computing cluster centroids

13

Maximum method

selects the maximum candidate

Threshold method

selects the maximum candidate if it is used more than a given threshold

Significant-change method

selects the maximum candidate if the percentage difference between the candidate and the current value exceeds a threshold

name: Cost Charge Rate (Price)

# mappings: 15% 15% 40% (30%)

Name of an SLA parameter


14

Maximum method


Threshold method





# mappings: 15% 15% 40% (30%)



15

Maximum method


Threshold method





# mappings: 15% 15% 40% (30%)


Step 2: Autonomic mapping modification

Input

SLA templates: Tuser, Tinitial, Tnew

SLA mappings: Tuser ↔ Tinitial, Tinitial ↔ Tnew

Result

SLA mapping(s): Tuser ↔ Tnew

p p p

Tuser Tinitial Tnew

Tuser ↔ Tinitial Tinitial ↔ Tnew

Tuser ↔ Tnew

16

Input



Result



Availability Availability Availability

Tuser Tinitial Tnew

17

Input



Result



Availability Availability Av

Tuser Tinitial Tnew

Availability ↔ Av

Availability ↔ Av

18

Input



Result



Availability ServiceAv Av

Tuser Tinitial Tnew

ServiceAv ↔ Av

Availability ↔ ServiceAv ↔ Av

Availability ↔ ServiceAv

19

Clustering - Challenges

Case 1: SLA parameter deleted from the template

Is the new public SLA template good enough for the user?

Case 2: SLA parameter added to the template

How to recognize new parameter’s equivalent in user’s private SLA template?

20

Simulation testbed

Serviceconsumers

Market repositoryAccounting

serviceQuerying

serviceSLA mappingservice

Adaptation service

AA

Administration service

Service provider

Initial public SLA template

1. Assign service to the template 2. Fetch the SLA template

4. Apply clustering andlearning methods

6. Assign templatesto consumers

7. Measure overallnet utility

New public SLA templates

MMMMM

5. Modify SLAmappings

aa3. Create SLA mappings

Private SLA templates

AA

21

Utility and cost model

Utility 0 - parameter does not exist in one of the templates

1 - both parameter properties differ in templates

2 - only one property differs in templates

3 - templates do not differ with respect to the parameter

Cost 0 - user needs no new mappings

1 - user must create new mapping(s)

2 - user must create new adding/deleting wishes

5 - user must rectify autonomically created mappings

Overall net utility = overall utility – overall cost

22

Simulation results

Overall net utility

Overall cost

23

Adding autonomic capabilities solves these problems!

24

1. Definition of virtual products 2. Self-aware markets

Infrastructure

STANDARDIZED GOODS

VIRTUAL GOODS

Applications

Autonomic market platform

A marketplace is autonomic if it, once established, can exist and operate without human intervention

Self-awareness:

knowledge of the self and the self’s state, such that autonomic capabilities can be introduced

Self-Healing

Self-Protecting

Self-Optimizing

Self-Configuring

25

Market Adaptation

Market Platform Frontend

Market Implementation

Management App Cloud

Application Reqs &

Constraints

App

Service Directory Allocation/Pricing

Mechanism

Market Infrastructure

Middleware

Sensor

Mappings

M

K A

P

E

26 Credits: Ivan Breskovic

Monitoring methodology

Liquidity

Provider revenue

Transaction volume

Platform revenue

No. of allocations

Execution costs

No. of active participants

Immediacy

Bid-ask-spread

Market depth

Immediacy of matching

Average price bids

Average price requests

No. of allocated resources

Price of matching

Participation costs on market platform

Computation time No. of platform resources

Costs for platform resources

Active traders in last observation period

Metrics Goals

27

Simulation environment

28

Case study: buyers stop bidding

Simulation properties

200 buyers, each with 100 requests

100 sellers

Applied mechanism: continuous double auction

29

Infrastructure observations

Simulation properties

200 buyers, each with 100 requests

100 sellers

Used infrastructure

MacBook Pro

Dual Core 2.66 GHz

4 GB RAM

30

0

20000

40000

60000

80000

100000

120000

140000

160000

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82

Iteration

Total No. of bids Total No. of asks Total No. of allocations

Goals affected

Liquidity

Provider Revenue

Transaction Volume

Platform Revenue

No. Of Allocations

Execution Costs

No. Of Active Participants

Immediacy

Bid-ask-spread

Market Depth

Goals

0

500

1000

1500

2000

2500

3000

3500

4000

4500

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82

31

Applications: Scientific Computing

Data analysis in bioinformatics

Joint work with D. Kreil and P. Labaj, Chair of

Bioinformatics, BOKU Vienna.

Resulting Workflow

33 33

Million reads

Mapping to genome

Aligned reads Not-aligned reads

Splitting to sub-reads

Sub-reads

Set 1

Sub-reads

Set n

Sub-reads

Set 2

Searching for splice-junctions

Monitoring Agent

Monitoring Agent

Monitoring Agent

Reads aligned to the reference

Cloud Infrastructure

Monitoring

Framework

Knowledge

Management

FoSII Cloud Management

+ =

Green ICT

34

Green ICT

35

Green ICT

36

Green ICT

37

Green ICT

38

Joint work with Jean-Marc Pierson, Georges Da

Costa, Damien Borgetto, University Paul Sabatier,

Toulouse, France.

Conclusions and future work

Self-aware market platforms are crucial for the future development of utility computing

First step towards the vision: a monitoring methodology a series of realistic market goals

sets of extractable metrics from a market platform

mappings for each goal as a proxy for platform performance

Future work consider additional case studies

explore the remaining adaptation phases

connect and integrate with existing platforms

M

K A

P

E

39

Thank you for your attention!

Ivan Breskovic, Ivona Brandic, Vincent Emeakaroha, Michael Maurer

Vienna University of Technology, Austria

Christian Haas and Simon Caton Karlsruhe Institute of Technology, Germany

Jörn Altmann Seoul National University, Korea

challenges in establishment of open cloud markets

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