BUSINESS PROCESS DESIGN: TOWARDS SERVICE-BASED GREEN INFORMATION SYSTEMS Barbara Pernici, Danilo Ardagna, Cinzia Cappiello Politecnico di Milano [email protected]

BUSINESS PROCESS DESIGN: TOWARDS SERVICE-BASED GREEN INFORMATION SYSTEMSBarbara Pernici, Danilo Ardagna, Cinzia CappielloPolitecnico di [email protected]

Milano, 7 settembre 2008

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Prof. Barbara PerniciDEI

Outline

• Motivations• Process and Service QoS optimization• Flexible and Self-healing services• Towards green service management• Open research issues

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Sustainable IT

• Climate debate and Sustainable Growth • Power consumed by Information Technology (IT)

Power per rack 1kW in 2000, 8kW in 2006, 20kW in 2010• The impact of IT on energy budget is becoming more and more

significant Forecast up to 40% of IT budget in 2012

• Service centers alone consume 1.5% of the power produced in the US, and are projected to reach 4.5% within 5 years

efforts to reduce power consumed by service centers

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Green Information Systems

• design of Information Systems under an energy consumption perspective focusing on service and information management

• use of Information Systems focusing mainly on the reduction of the resources needed for

processing information and for information storage after its elaboration.

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Energy efficiency in IS

• Redundancy improves systems' QoS, but may introduce energy inefficiencies.

• advances in autonomic and self-healing service-based systems enable a potential reduction of system redundancy energy optimization related to data management is more and

more challenging.

SELF-HEALING

ADAPTIVITY

SERVICES

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Adaptivity approaches

• Dynamic service selection Varying context QoS optimization

• Self-healing services Unanticipated exceptions Changing operating conditions

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Quality global constraints:

cost <1000 train.reservation.cost<600

Invoke hotel.reservation

Invoke train.reservation

Preferred:

- ACMEHotels- ItalianHotels

Negotiate:

- lowest price - offer request

Invoke flight.reservation

not late late

Probability=0.8 Probability=0.2

Dynamic service selection

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Abstract process

op1

op2

op3

AS2

Abstract services

op1

op2

op3

AS1

Process

AS1.op1

AS1.op2

AS1.op3

AS2.op1

AS2.op2

AS2.op3

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Concrete process

op1

op2

op3

CS2

Concrete services Process Concretization

CS1.op1

CS1.op2

CS1.op3

CS2.op1

CS2.op2

CS2.op3

op1

op2

op3CS1

op3

AS1.op1

AS1.op2

AS1.op3

AS2.op1

AS2.op2

AS2.op3

Process

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t1 t2 tI

ws1

ws1,1 ws1,2 ws1,|OP(1)|

. . .

...

ws2

ws2,2 ws2,|OP(2)|...

wsJ

wsJ,1 wsJ,|OP(J)|...

. . .

wsJ,2ws2,1

A selection problem?

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Design time or run time problem?

When are service selected?

When is quality agreed?

Rebinding and renegotiation

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T1

T4

T2 T3

Flexible process Concrete services

Candidates for T1

Candidates for T2

Candidates for T3

Candidate for T4

substitute

Search criteria

Search criteria

Search criteria

Global process constraints

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Local optimization: run time selection of the best candidate service which supports the execution of the running high level activity

Global optimization: identification of the set of candidate services which satisfy the end user preferences for the whole application

Quality of Service (QoS) constraints at local and global level

WS Selection is an Optimization Problem

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An optimization problem?

Several approaches:

Local optimization (Cardoso) Linear programming (Benatallah, Ardagna) Genetic algorithms (Canfora)

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Complex services based on composition of other services May fail (functional / QoS)

Which are the responsible services (diagnosis)? How can we recover at run time (repair)?

?!??Wrong answer No answer

Late answer Bad quality answer

Self-healing services: the WS-Diamond approach

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The WS-Diamond repair cycles

ExecutionSupport

Diagnoser

Recovery Planner

fault

diagnosisplan

Logging & Monitoring

Model Compiler

1 run multiple runs

QoSManagement

QoSDiagnoserQoS Recovery

Planner

stats

fault

diagnosis

plan

logs

Self-HealabilityChecker

ExecutionSupport

Diagnoser

Recovery Planner

fault

diagnosisplan

Logging & Monitoring

Model Compiler

1 run multiple runs

QoSManagement

QoSDiagnoserQoS Recovery

Planner

stats

fault

diagnosis

plan

logs

Self-HealabilityChecker

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ServiceCenterInfrastructure

BusinessProcess

Virtual Machine Monitor

OS

App1

OS

App2

OS

Appn…

VM1 VM2 VMn

Storagetier

Servertier

t2

t1

t3

t4

End-users’perspective

Max of QoS for the end UserConstrained Optimization ProblemOptimization of process instances

Providers’perspective

Max SLA rev – Energy costQueuing Network Model and Non-linear Opt

Web service Componentsperformance parameters

Web service Components

workload

New performanceobjectives QoS

Re-negotiation

Linking business processes and IT infrastructure

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Virt. Machine Monitor

OS

App1

OS

App2

OS

Appn…

VM1 VM2 VMn

Storagetier

Servertier

ServiceCenterInfrastructure

BusinessProcess

t2

t1

t3

t4

System

Controller

Performance

PerformanceObjectives

Servers’ DVSLoad balancing

...

...

ProcessLayer

Max of QoS for the end UserConstrained Optimization ProblemOptimization of single process instanceData Dedup.: reduction of Business Obj. accesses

Infrastr.Layer

ControlLayer

Max SLA rev – Energy costQueuing Network Model and Non-linear Opt.Half an hour time scaleData Dedup.: Business obj. preservation

Trade-off Performance-EnergyIdentification and Control TheoryOne minute time scale

Web service ComponentsPerformance Parameters

Web service Components

Workload

Performance achievements

(% violations,...)

PerformanceGoals

New perf.objectives

QoSRe-negotiation

Controllers

ServiceWave’08

D. Ardagna, C. Cappiello, M. Lovera, B. Pernici, M. Tanelli

A third level: control

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Governance Layer

Technology Layer

Green IS strategies

Gre

en I

S C

ontr

olService management and

BPMData management

•Metrics•Guidelines•Energy and CO2 impact

•Policies for run-time system re-configuration•Run time energy monitoring•Energy use optimization

Green “purifiers”

Service technology Data technology

Green “purifiers”

Green “purifiers” Green “purifiers”

A proposal: Green IS framework and Green purifiers

“IS purifier” approach, as cleaning water for a sustainable environment

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Open research issues

PROBLEMS TO CONSIDER

• Interrelation between design and run time decisions (design for QoS optimization), complexity

• Semantic information about quality• Incomplete information and distributed decisions• Variable quality profiles• Multiple instances and multiple processes• Soft and hard constraints• Link with strategic goals and underlying infrastrucure; linking

decisions • Stability of solutions

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Thank you

Questions?

Documents

BUSINESS PROCESS DESIGN: TOWARDS SERVICE-BASED GREEN INFORMATION SYSTEMS Barbara Pernici, Danilo Ardagna, Cinzia Cappiello Politecnico di Milano [email protected]