middleware systems research group msrg.org enabling bpm for clouds hans-arno jacobsen bell...

MIDDLEWARE SYSTEMSRESEARCH GROUP

MSRG.ORG

Enabling BPM for Clouds

Hans-Arno Jacobsen

Bell University Laboratory Chair

University of Torontohttp://www.padres.msrg.utoronto.ca

Summer School on Service Research, July 19th, 2010

“BPM in Cloud Architectures: Business Process Management with SLAs and Events”, joint work with Vinod Muthusamy (extended abstract)

http://eqosystem.msrg.org/

An eQoSystem for declarative distributed applications with SLAs

via Events & SLAs


MSRG.ORG

© 2010 Hans-Arno Jacobsen

c001 endc001 end

Service Summer, July 19th, 2010, KIT, Germany

> 0.7

elseelse

Business Process Example

Checkscore

Creditcheck

Creditcheck 2

Checkscore 2

Reject

Approve

Send toofficer

< 0.3

Store inDB

…

…

Loan Application Processing

< 0.5

service RTT < 100ms

uptime > 99.99 %

service RTT < 100ms

uptime > 99.99 %

Pid=c401Pid=c401 gid=c001gid=c001

gid=c001gid=c001

service time < 2sservice time < 2s

service cost < $0.02service cost < $0.02

Pid=c1Pid=c1Pid=c001Pid=c001

Pid=c3301Pid=c3301


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© 2010 Hans-Arno Jacobsen Service Summer, July 19th, 2010, KIT, Germany

Large-scale Business ProcessesVendorVendor

SaleSale

ManufactoryManufactory

FinanceFinance

Dispatch B

Out-stock B

Pick-up goods

Packaging

MarketingMarketing

Design

Out-stock B

Target price

Prototype

Out Take

Control

Assign

Confirm

Determinateplan

Check stock

Rawmaterials

Audit

Raw

Determinateplan

Executeplan

Processcontrol

Monitor

Process

Pay

Check

Signature

Print receipt

WarehouseWarehouse

Delivery

FedEx

Pick up

Monitoring

Statistic

Chart

StrategyStrategy DesignDesign MarketingMarketing OrderOrderManufactoryManufactory PaymentPayment

Requirementcollection

Feature selection

Goods selection

Confirmfeatures

Material

Make plan

Feedback

Check order

Fill order

Check dealer Check credit

Approval Approval

Validate

Affirm order

Sale prediction

SignContract

CCCadministrate

Goods delivery

Fill dispatch bill

Fill out-stock bill

Credit card

Case Study (Chinese Electronics Manufacturer):

• Department-level processes with 26 to 47 activities

• Global processes that compose departmental ones

• Thousands of concurrent instances

• Hundreds of collaborating partners

• Geographically distributed

• Administrative boundaries

Case Study (Chinese Electronics Manufacturer):

• Department-level processes with 26 to 47 activities

• Global processes that compose departmental ones

• Thousands of concurrent instances

• Hundreds of collaborating partners

• Geographically distributed

• Administrative boundaries


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What Support is Required ?

De-coupling and loose coupling

Fine-grained event filtering

In-network event processing

Composite event detection

Event correlation


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Agenda

Enabler

The PADRES approach

Event-driven BPM with PADRES

SLA-driven BPM with PADRES


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What Abstractions Enable BPM?



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What Abstractions Do Not Work? Databases

Great for managing historic data But what about future data (e.g., events)

Data streams Great for managing structured streams of tuples But what about un-structured, multi-typed, sporadic, un-

ordered events from many sources Rule-based expert systems

Great for inference and reasoning But what about managing large numbers of fined-grained

filters in distributed environments

Cum grani salis



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What Abstractions Enable BPM?

It is our opinion that the afore-mentioned requirements can best be addressed by The content-based publish/subscribe paradigm Realized by content-based message routing

Events represent state transitions in the environment. Conveyed as publications to the pub/sub system

Event filtering and correlation is based on Subscriptions managed by the pub/sub system


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amount > $500K

amount > $500K

Input queue

Output queue

d2

Output queue

d3

subscription dest

Matching Engine

Routing Table

&

Class = Loan d2

Service RTT < 2s d3

B

B

B

B

B

Content-based Publish/Subscribe

Publisher Publisher

Subscriber Subscriber

Publications

Subscriptions

NotificationNotification

Service RTT <

2sService RTT <

2s

Service RTT <

150msService RTT <

150ms

Uptime > 99.99%

Uptime > 99.99%

class = Loan,

class = Loan,

Service = credit check

Service = credit check

status = approved,

status = approved,

1. Advertise1. Advertise

2. Subscribe2. Subscribe

9

Event-BasedEvent-Based

DecoupledDecoupled

FlexibleFlexible

ResponsiveResponsive

Content RoutingContent Routing

DeclarativeDeclarative


Content-based Routing3. Publish3. Publish


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Application Modeling

Advertisements (schema or types) [class=Loan], [action=*], [customerID=*], [amount<$100K], [region=East]

Publications & events (data) [class, Loan], [action, request], [customerID, 876594], [amount, $50K]

Subscriptions (query) [class=Loan], [amount>$500K], [region=*]

Application semantics is expressed via advertisements (data sources), publications(data sources), and subscriptions (data sinks)


A

S

P


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Benefits of Publish/Subscribe Simplifies IT development and maintenance by

decoupling enterprise components

Supports sophisticated interactions among components using expressive subscription languages

Supports fine-grained subscriptions for event management

Achieves scalability with in-network filtering and processing


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Many Applications are Event-based &Benefit from Publish/Subscribe

A

B C

D

E

F

RFID and sensor networksService oriented architectures

Workflows, business processes and job scheduling

Supply chain and logistics

Event-Based

Light

CallbackRFID: Razor SKU

Transform

Fault!

Temperature

Invoke Loan

Ordered

Delivered

In flightJob A done

Triggered


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Web start & open sourcepadres.msrg.org

Implemented in Java


Our PADRES ESB for Event-driven BPM

First generation of students, when I looked away Peng Alex David aRno Eli Serge

PADRES is Publish/subscribe Applied to Distributed Resource Scheduling

PAdres is Distributed REsource Scheduling

http://www.padres.msrg.utoronto.ca

Enterprise Services Bus(Events & Services Bus)

13

Acknowledgements (2004-present):


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Our PADRES ESB Stack & Vision

Server Farm

Computers

ComputersDatabase

Laptops

Computers

Workstation

Laptops

DatabaseServer

ServerSwitch

Server

Switch

Computing, Storage, and Networking Resources

Content-based Routing (Publish/Subscribe)

Content-based RouterClients (publisher/subscriber)

PADRES ESB

Business Process Execution

Deploy Control UpdateVisualize

Monitor ...

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Business Process Management and Business Activity Monitoring

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Event Management Framework

Application Events

Business Events

Complex Events

Network & System Events


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B

B

B

B

P

S

S

input queue

output queue dest2

output queue dest3

dest1

dest2

dest3

P

S

= publisher

= subscriber

subscription dest

Matching Engine

Routing Table

+

service time < 3s dest2

service time < 2s dest3

Publications

service time = 2.5sservice time = 1sservice time = 3s

PADRES ESB is an Overlay of P/S Brokers

output queue dest1


Try out and download at:http://www.padres.msrg.org


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Innovative PADRES Features

A

B C

D

E

F

CompositeEvents

HistoricAccess

Management

Robustness

LoadBalancing

Security

ACM Middleware’2004IEEE ICDCS’2005

IEEE ICDCS’2009ACM Middleware’2008

IEEE ICDCS’2010ACM Middleware’2006

ACM Middleware’2007

CSRG TR 2009ACM DEBS’2007

ACM DEBS’2007


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Event-driven BPM with PADRES




PADRES ESB



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B C

D

A

B C

D

E

F

Example: D executes, if B and C have completed (D depends on B and C).


Modeling Business Processes

Dependency in processes and more complex process patterns require event correlation

Event correlation is enabled by the detection of composite events

Composite events are expressed via composite subscriptions

Composite subscription consists of atomic subscriptions Subscription language features for BPM modeling

E.g., AND, OR, and variables ($x)

A

B C

D

E

F

Exception


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Composite Subscription Examples

Expresses a structural property of a process

[class = Activity Status], [cmd. = Archived],[Process ID = $X ] AND[class = Activity Status], [cmd. = Signed Off],[Process ID = $X ]

Expresses a performance property of a process

[cmd. = Credit check request], [Process ID = $X ] AND[status = Approved], [Process ID = $X ]


Checkscore

Creditcheck

Process

Approve

Singed Off

…Loan Process

Reject

Archive

> $50K


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Business Process Management Transformation of

process into pub/sub language

Deployment of transformed process

Execution of process by triggering instances

Monitor process & instance execution

Manage, i.e., control, version,…

A

B C

D

trigger

E

F

trigger multiple instances concurrently

Exception & compensation


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Receive

PADRES ESB1

3 4

5

BPELReceive

Assign

Flow

Invoke Wait

Reply

Wait Reply

Invoke

2

Assign

WS Gateway Agent

6

END

WS client

Web Service

Web Service

http/soappub/sub



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Sub4:

[class,eq,ACTIVITY_STATUS],

[process,eq,’Process5’],

[activityName,eq,’activity2’],

[IID,eq,$X],

[status,eq,’SUCCESS’]

&&

[class,eq,LINK_STATUS],



[IID,eq,$X],

[status,isPresent,any]

Sub4:




[IID,eq,$X],


&&

[class,eq,LINK_STATUS],



[IID,eq,$X],

[status,isPresent,any]

BPEL Transformation Example

Process 5Process 5activity1activity1

flow1flow1

activity2activity2 activity5activity5



activity8activity8

Sub1:




[IID,isPresent,any],


Sub1:






Pub1:

[class, ACTIVITY_STATUS],

[process,’Process5’],

[activityName,’flow1’],

[IID,’g001’],

[status,’STARTED’]

Pub1:



[activityName,’flow1’],

[IID,’g001’],

[status,’STARTED’]

Sub2:



[activityName,eq,’flow1’],


[status,eq,’STARTED’]

Sub2:



[activityName,eq,’flow1’],


[status,eq,’STARTED’]

Pub2:



[activityName,’actitiy2’],

[IID,’g001’],

[status,’SUCCESS’]

Pub2:




[IID,’g001’],


Pub3:

[class,LINK_STATUS],



[IID,’g001’],

[status,’POSITIVE’]

Pub3:

[class,LINK_STATUS],



[IID,’g001’],

[status,’POSITIVE’]

Pub4:




[IID,’g001’],


Pub4:




[IID,’g001’],


Sub5:






&&





[status,eq,”SUCCESS”]

Sub5:






&&





[status,eq,”SUCCESS”]

Pub5:




[IID,’g001’],


Pub5:




[IID,’g001’],



Cf. our ACM Trans Web’2010for full BPEL mapping

Sub1 (flow agent):

[class = ACTIVITY_STATUS],

[process = ’Process5’],

[activityName = ’activity1’],

[IID isPresent any],

[status = ’SUCCESS’]

Sub1 (flow agent):

[class = ACTIVITY_STATUS],



[IID isPresent any],

[status = ’SUCCESS’]

Sub4 (actvity6 agent):

( [class = ACTIVITY_STATUS],



[IID = $X],

[status = ’SUCCESS’] )

AND

( [class = LINK_STATUS],



[IID = $X],

[status isPresent any] )


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Evaluation: Changing Request Rate

P/S

P/S

P/S

P/S

P/S

PADRES ESBPADRES ESB

Clustered

Centralized

Distributed


20 servers


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SLA-driven BPM




PADRES ESB



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http://eqosystem.msrg.org/

An eQoSystem for declarative distributed applications with SLAs


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Currently, business goals must be manually considered at every stage of the business

process development cycle

N

Y

Far?Get

destinationValidaterequest

Find flight

Find train

cost < $0.02

service time < 3s

Only trusted partners



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Layer SLA Metric Example

Business Process

Cost Cost of search service < $0.02 per use

Fidelity, quality, utility Map resolution > 300x300

Trust, reputation Only use trusted payment services

Deployment & Operations

Service time Execution time < 3s

Throughput Throughput > 100/min

Availability Uptime > 99.9%

Load balance Server utilization delta < 10%

Network Latency Service RTT < 100ms

Bandwidth Max bandwidth < 3Mbps

Jitter Delay jitter < 10ms

Service Level Agreements (SLAs)SLAs are contracts between service consumers and providers

that specify the expected behavior of each party and the penalties of violating the contract.

SLAs specify business goals declaratively.


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Runtime Uses of SLAs

A B C D

p

q

Web service Execution engine

2

1b service time < 1s

M

ESB broker topology

A,B

D

1a

C

service time < 2s

Dynamic service discoveryDiscover services with capabilities that satisfy goals.

MonitoringOnly monitor the business events related to goals.Feed back measurements to support runtime adaptations.

Distributed executionFine-grained allocation of process to available resources.Move portions of process to strategic locations.

ESB adaptationReconfigure the ESB topology to satisfy goals.

M Monitor ESB node (PADRES broker)

Process



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Agenda

Distributed process execution Architecture Components

Execution & optimization algorithms Design issues Evaluation



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Process Execution ArchitecturesCentralized

One execution engine May not scale Central point of failure

Agent-based Distributed execution engine In-network processing

Lower bandwidth and latency Fine-grained use of resources

Clustered Replicated execution engines Centralized control and data

High bandwidth and latency Still may not scale & administrative

limitations

ABCD

ABCD

ABCD

D

C

A,B

Problem: How to deploy activities in a distributed manner to satisfy SLAs?



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Large-scale Business ProcessesVendorVendor

SaleSale

ManufactureManufacture

FinanceFinance

Dispatch B

Out-stock B

Pick-up goods

Packaging

MarketingMarketing

Design

Out-stock B

Target price

Prototype

Out Take

Control

Assign

Confirm

Determinateplan

Check stock

Rawmaterials

Audit

Raw

Determinateplan

Executeplan

Processcontrol

Monitor

Process

Pay

Check

Signature

Print receipt

WarehouseWarehouse

Delivery

FedEx

Pick up

Monitoring

Statistic

Chart

StrategyStrategy DesignDesign MarketingMarketing OrderOrderManufactureManufacture PaymentPayment

Requirementcollection

Feature selection

Goods selection

Confirmfeatures

Material

Make plan

Feedback

Check order

Fill order

Check dealer Check credit

Approval Approval

Validate

Affirm order

Sale prediction

SignContract

CCCadministrate

Goods delivery

Fill dispatch bill

Fill out-stock bill

Credit card



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31

Distributed Process Execution:Architecture & Components



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Execution Engine

Atomic Redeployer (IEEE ICDCS’2009)

Candidate Engine Discovery

(ACM DEBS’2009)

PADRES messaging layer

Activity Manager

Activities(ACM TWEB’2010)

Activity Profiler

Engine Profiler

Late

ncy

Ba

ndw

idth

En

gine

re

sour

ce

RedeploymentManager (CASCON)

SLAs, cost models

Estimators

Ranking algorithms

SLA Management Stack

Instance states

Input, output queues



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Activity Profiler Profiles execution of local activities Maintains profiles for various metric types

Message hops, disk I/O, energy usage, etc.Profile for

message hops metric

Activity Pred/Succ

Msg rate

T P 40/min

T S 10/min

1 4 7

Execution engineBroker

2

3

5

6

8

9

P

T

S

P T S

Process

ESB broker topology Profile for

execution time metric

Activity Exec time

T 0.3 s

Profile for memory usage

metric

Activity Mem

T 30 MB

Activity


Example:


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Activity Profiler Summary


APk

activity profiler for metric type Mk

APk

activity profiler for metric type Mk

activity lifecycle events

ai sk(ai)


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Engine Profiler (e.g., distance) Discover paths

e2→e5 , e5→e7

Probe paths e5→e4 (for candidate C)

Compute paths e2→e4 , e4→e7

Cases e4 in e2→e5

e4 in e5→e7

otherwise

1 4 7


2

3

5

6

8

9

P

S C

P T S

Probed paths

Source Target Distance Probe @

5 4 1 1:34pm

Discovered paths

Source Target Distance Disc @

2 5 3 1:48pm

5 7 2 1:49pm

Computed paths

Source Target Distance Comp @

2 4 2 2:02pm

4 7 1 2:04pm

Computes and caches information about candidate engines

Cf. DEBS’2009 for our resource discovery algorithms to identify candidates

T

Process



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Engine Profiler Summary


EPk

engine profiler for metric type Mk

EPk

engine profiler for metric type Mk

set of candidate engines

ej pk(ej)


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Redeployment Manager Estimator: Computes an estimate of the metric

cost ck(ai,ej) of hosting an activity ai at engine ej

Cost model: Computes an estimate of the cost c(ai,ej) of hosting activity ai on engine ej

Check deployment: Determines what to do with an activity ai Determine best engine e Compute benefit: c(ai) – c(ai,e) Compute resident time at current engine If resident long enough

If benefit is large enough move ai to e Otherwise, apply pressure to other activities



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Redeployment Manager Summary Compute the cost of deploying local activities

ai at candidate engines ej

GivenF(ai): Cost functionP(ai): PredecessorsS(ai): Successors

MeasurementsC(ai): Cost at local engineE(P(ai)): Location of predecessorsE(S(ai)): Location of successors

ComputeC(ai, ej) for every ai, ej :

Estimated cost of deploying activity ai at candidate engine ej

ComplexityMemory: O( |ai| |ej| )

Computation: O( |ai| | ej | |Navg(ai)| )Cost Model



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Redeployment Manager Summary


Ek

estimator for metric type Mk

Ek

estimator for metric type Mk

ai

ej

ck(ai,ej)

APkAPk EPk

EPk

CM

cost model

CM

cost modelai

ej

f

c(ai,ej)

EkEk

EkEk

Check Deployment

Check Deployment

ai

ejredeployredeploy

{ej}pressurepressure

RM

Redeployment Manager

RM

Redeployment Manager

{ai}{ (ai,ej) }

redeployredeploy

{ (ai,{ej})}pressurepressure


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Atomic Redeployment

Traditional pub/sub client movement protocols are expensive and do not offer transactional properties

Transactional movement Formalized movement properties similar to ACID properties Efficient and guaranteed routing reconfiguration

For example, guarantee that no messages are lost, if an activity is re-deployed

See IEEE ICDCS’2009



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Execution Engine

Atomic Redeployer (IEEE ICDCS’2009)

Candidate Engine Discovery

(ACM DEBS’2009)

PADRES messaging layer

Activity Manager

Activities(ACM TWEB’2010)

Activity Profiler

Engine Profiler

Late

ncy

Ba

ndw

idth

En

gine

re

sour

ce

RedeploymentManager (CASCON)

SLAs, cost models

Estimators

Ranking algorithms

SLA Management Stack Summary

Instance states

Input, output queues



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1. Distribution cost Cdist = f(Cd3, Cd1, Cd2)Cd1 Message rateCd2 Message sizeCd3 Latency

2. Engine cost Ceng = f(Ce1, Ce2,Ce3)Ce1 Load (number of instances)Ce2 Resources (CPU, memory, etc.)Ce3 Activity complexity

3. Service cost Cserv = f(Cs1, Cs2, Cs3)Cs1 Latency of external serviceCs2 Execution time of external serviceCs3 Marshalling/unmarshalling

Cost(activity) = f(wiCi)Cost(process) = ∑cost(activity)

The cost of a process is based on metrics

These metrics can be weighted to achieve different objectives

Optimize timewd1 = wd3 = we3 = wserv = 1, other wi = 0

Optimize network overheadwd2 = wd3 = 1, other wi = 0

Various optimization criteria can be specified

Threshold criteria: ∑wiCi > x E.g., Report SLA violations within 3 s.

Minimized criteria: min( ∑wiCi ) E.g., Minimize distribution overhead

Cost Model Components



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Examples of SLAs & Cost Functions Minimize message hops

f() = msg_hops_rate = msg_rate * engine_distance

Minimize bandwidth cost f() = msg_rate * link_cost $$$

Limit CPU & network energy usage f() = 0.3 * cpu_energy + 0.7 * link_energy < X

f() = 0.3 * (invocation rate * engine_unit_energy) + 0.7 * (msg_rate * link_unit_energy) < X



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Evaluation and design issues



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Red activities are pinned to

brokers

Process Hotspot – Illustration


Process

ESB broker topology

SLAMinimize traffic

MetricMessage hops

D E F

p = 90% q = 10 %

B

CA I

G

H


1 4 7

2

3

5

6

8

9

D F E

AB

C

GI

H


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Process Hotspot – Results Traffic with redeployment is 47% of the static case

Post-redeployment traffic is 10% Redeployment triggered in about 30 sec

47

10% of static



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Varying Hotspot – Illustration

D E F

p q


Process

ESB broker topology

B

CA I

G

H


1 4 7

2

3

5

6

8

9

D F E

AB

C

GI

H

Red activities are pinned to

brokers


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Varying Hotspot – Results Dynamic redeployment suffers temporarily

after process hotspot moves Traffic with redeployment is 42% of the static

case



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Summary on SLA-driven BPM Distributed execution engine has qualitative and quantitative

advantages Redeployment algorithm can optimize SLA in many cases Challenge 1: Local optima

Techniques to selectively expand knowledge can work Widen candidate radius Redeploy sets of activities

Challenge 2: Coordination Independent decisions can destabilize system Potential problem has not manifest in evaluations so far

Challenge 3: SLA granularity (more an engineering issue) Can’t specify SLA on portions of a process Can’t specify SLA on particular instances of a process

(e.g., VIP user)



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Benefits of Content-based Publish/Subscribe for BPM

Naturally enables centralized and distributed business process coordination

Coordination can span administrative domains and physically distributed resources

Supports process orchestration and choreography Monitoring & control is integral part of paradigm Agile on the fly process adaptation and versioning Correlation of application events with low-level

infrastructure events


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Summary: The PADRES Stack

Server Farm

Computers

ComputersDatabase

Laptops

Computers

Workstation

Laptops

DatabaseServer

ServerSwitch

Server

Switch

Computing, Storage, and Networking Resources



PADRES ESB



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Event Management Framework

Application Events

Business Events

Complex Events

Network and System Events

Download PADRES @ http://padres.msrg.org


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Outlook

Dynamic BPM for flexible service value chains

Ad hoc business processes

Business impact assessment

Business entity concept



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Conclusions

Many applications are inherently event-driven. Effective BPM requires capable event

processing abstractions. Content-based publish/subscribe is a powerful

event processing abstraction and paradigm. PADRES is based on the pub/sub paradigm. PADRES is an ESB targeted at event-based

BPM. PADRES enables real-time business analytics

and business activity monitoring.


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Acknowledgements: Current PADRES Team Chen Chen Alex Cheung Alton Chiu Amer Farroukh Patrick Lee Guoli Li

Bala Maniymaran Vinod Muthusamy Reza Sherafat Naweed Tajuddin Chunyang Ye Young Yoon


Countless alumni (see our web site)http://padres.msrg.org


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Questions & Discussion?

A D R E SP


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Selected Literature about PADRES All our papers are available from

http://msrg.org/tags/padres & hyperlinked below A Distributed Service Oriented Architecture for Business

Process Execution. ACM Trans. on the Web, 2010. Publisher Placement Algorithms in Content-based

Publish/Subscribe. IEEE ICDCS, 2010. Transactional Mobility in Distributed Content-Based

Publish/Subscribe Systems. IEEE ICDCS, 2009. Distributed Automatic Service Composition in Large-Scale

Systems. ACM DEBS, 2008. Adaptive Content-based Routing in General Overlay

Topologies. ACM Middleware, 2008. Historic Data Access in Publish/Subscribe. ACM DEBS, 2007. Papers on managing SLAs with PADRES

http://msrg.org/tags/sla



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References The PADRES ESB project home

http://padres.msrg.utoronto.ca An eQoSystem for declarative distributed applications with SLAs

http://research.msrg.utoronto.ca/Eqosystem/ The Micro-ToPSS event processing middleware for sensor networks (RFID)

http://microToPSS.msrg.utoronto.ca/ Mobile-ToPSS – publish/subscribe for mobile and location-based applications

http://research.msrg.utoronto.ca/Mobile/ ToPSS - the Toronto Publish/Subscribe System Family

http://www.ToPSS.biz (coming soon ) Quantifying events in software to increase modularity & customization in C-

based systems and software-based product lines http://www.AspeCtC.net (ACC - the AspeCt-oriented C compiler)

The Middleware Systems Research Group http://www.msrg.utoronto.ca

My web site http://www.eecg.toronto.edu/~jacobsen

@ the University of Toronto

DEBS Conferencehttp://www.debs.org

July 2011 @ IBM in New York, US


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middleware systems research group msrg.org enabling bpm for clouds hans-arno jacobsen bell...

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