1. BUSINESS PROCESS DESIGN: TOWARDS SERVICE-BASED GREEN INFORMATION SYSTEMS Barbara Pernici, Danilo Ardagna, Cinzia Cappiello Politecnico di Milano barbara.pernici@polimi.it Milano, 7 settembre 2008

2. Prof. Barbara Pernici DEI 2 Outline Motivations Process and Service QoS optimization Flexible and Self-healing services Towards green service management Open research issues

3. Prof. Barbara Pernici DEI 3 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

4. Prof. Barbara Pernici DEI 4 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.

5. Prof. Barbara Pernici DEI 5 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

6. Prof. Barbara Pernici DEI 6 Adaptivity approaches Dynamic service selection  Varying context  QoS optimization Self-healing services  Unanticipated exceptions  Changing operating conditions

7. Prof. Barbara Pernici DEI 7 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 latelate Probability=0.8 Probability=0.2 Dynamic service selection

8. Prof. Barbara Pernici DEI 8 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

9. Prof. Barbara Pernici DEI 9 Concrete process op1 op2 op3 CS2 Concrete servicesProcess Concretization CS1.op1 CS1.op2 CS1.op3 CS2.op1 CS2.op2 CS2.op3 op1 op2 op3 CS1 op3 AS1.op1 AS1.op2 AS1.op3 AS2.op1 AS2.op2 AS2.op3 Process

10. Prof. Barbara Pernici DEI 10 t1t1 t2t2 tItI ws 1 ws 1,1 ws 1,2 ws 1,|OP(1)|... ws 2 ws 2,2 ws 2,|OP(2)|... ws J ws J,1 ws J,|OP(J)|... ws J,2 ws 2,1 A selection problem?

11. Prof. Barbara Pernici DEI 11 Design time or run time problem? When are service selected? When is quality agreed? Rebinding and renegotiation

12. Prof. Barbara Pernici DEI 12 T1 T4 T2T3 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

13. Prof. Barbara Pernici DEI 13 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

14. Prof. Barbara Pernici DEI 14 An optimization problem? Several approaches:  Local optimization (Cardoso)  Linear programming (Benatallah, Ardagna)  Genetic algorithms (Canfora)

15. Prof. Barbara Pernici DEI 15 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

16. Prof. Barbara Pernici DEI 16 The WS-Diamond repair cycles

17. Prof. Barbara Pernici DEI 17 Service Center Infrastructure Business Process Virtual Machine Monitor OS App 1 OS App 2 OS App n … VM 1 VM 2 VM n Storage tier Server tier t2t2 t1t1 t3t3 t4t4 End-users’ perspective Max of QoS for the end User Constrained Optimization Problem Optimization of process instances Providers’ perspective Max SLA rev – Energy cost Queuing Network Model and Non- linear Opt Web service Components performance parameters Web service Components workload New performance objectives QoS Re-negotiation Linking business processes and IT infrastructure

18. Prof. Barbara Pernici DEI 18 Virt. Machine Monitor OS App 1 OS App 2 OS App n … VM 1 VM 2 VM n Storage tier Server tier Service Center Infrastructure Business Process t2t2 t1t1 t3t3 t4t4 System Controller Performance Objectives Servers’ DVS Load balancing... Process Layer Max of QoS for the end User Constrained Optimization Problem Optimization of single process instance Data Dedup.: reduction of Business Obj. accesses Infrastr. Layer Control Layer Max SLA rev – Energy cost Queuing Network Model and Non-linear Opt. Half an hour time scale Data Dedup.: Business obj. preservation Trade-off Performance-Energy Identification and Control Theory One minute time scale Web service Components Performance Parameters Web service Components Workload Performance achievements (% violations,...) Performance Goals New perf. objectives QoS Re-negotiation Controllers ServiceWave’08 D. Ardagna, C. Cappiello, M. Lovera, B. Pernici, M. Tanelli A third level: control

19. Prof. Barbara Pernici DEI 19 Governance Layer Technology Layer Green IS strategies Green IS Control Service management and BPM Data management Metrics Guidelines Energy and CO2 impact Policies for run-time system re-configuration Run time energy monitoring Energy use optimization Green “purifiers” Service technologyData technology Green “purifiers” A proposal: Green IS framework and Green purifiers “IS purifier” approach, as cleaning water for a sustainable environment

20. Prof. Barbara Pernici DEI 20 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

21. Prof. Barbara Pernici DEI 21 Thank you Questions?