BUSINESS PROCESS DESIGN: TOWARDS SERVICE-BASED GREEN INFORMATION SYSTEMS Barbara Pernici, Danilo...
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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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
Adaptivity approaches
• Dynamic service selection Varying context QoS optimization
• Self-healing services Unanticipated exceptions Changing operating conditions
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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
Design time or run time problem?
When are service selected?
When is quality agreed?
Rebinding and renegotiation
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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
An optimization problem?
Several approaches:
Local optimization (Cardoso) Linear programming (Benatallah, Ardagna) Genetic algorithms (Canfora)
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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
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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Prof. Barbara PerniciDEI
Thank you
Questions?