Using Simulation Models to Evolve Business Processes

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1 Using Simulation Models to Evolve Business Processes Marin Litoiu, Andrei Solomon York University with ideas from IBM Toronto Lab, CAS

2 Agenda Motivation and Goals Intro to BP Life Cycle BP Evolution Loop Case Study Conclusions

3 Motivation and Goals Business processes Coarse granular activities, distributed across a company Activities, data and control flow Use web services as underlying technology Optimize Key Performance Indicators (KPIs) Need to monitor WS execution and have a way to evolve them The promise of SOA- runtime agility We propose a feedback evolution loop based on simulation Processes are monitored on-line A simulation model is tuned at runtime Alternative deployments are proposed so that the KPIs are met

4 BPM Life Cycle Model. as-is or to-be business process is defined designed graphically modeled Simulated Assemble Deploy Assemble. (model) assembled (model) developed (model) configured (model) tested on the underlying system infrastructure. Simulation model Model Manage Deploy. deployed on an actual BPMS Manage. monitored and analyzed for the new Model phase adaptations. Model Design Simulate What-if scenarios Monitor Analyze(simulation) Predict(simulation) Execute

5 BPMN & BPEL Business Process Modeling Notation (BPMN) provides the graphical notation - Business Process Diagrams (BPD) gained much acceptance in the industry no tools can execute BPMN...but they can simulate it... Business Process Execution Language (BPEL) for Web Services standard executable language for business processes allows composition of web services can express conditional behavior, loops, declare variables, copy and assign values etc.

6 Business Measures Instance Metrics: Collection of measurements, resulting from each execution of the process Aggregated Metrics: Calculated across multiple instances using a function of interest (i.e. average, maximum, minimum, sum or count) KPIs: Example: Average Process Duration KPI KPI definition - specifies the method of calculation, given: current instances aggregated metrics predefined set of aggregation functions (i.e. average) time period for data collection (example: rolling 3 days = 3 days sliding window) specifies a desired target Are defined in Modeling phase

7 Proposed Manage Loop Business process: service components : web services - experience external disturbances Business process monitor: monitors records provides real time access to critical KPIs the business process for future analysis State estimator: output reflects an estimated system performance Accounts for un-modeled overhead Data links durations, queuing times Decision block: runs a change algorithm to decide upon a suitable correction Better deployments Tuning of the underlying IT A new L life cycle... State Estimator Simulation Model Particle Filter KPI targets Decision Control Change Business Process Monitor Disturbance Change Services Business Process

8 Configuration/Setup without particle filter d i Architecture without estimator: q i x i Monitoring: di - measured duration (known) qi queuing duration (unknown) xi service duration (unknown) Simulation Model Decision Control Change Business Process Estimator: finds xi from di Business Process Monitor Simulation: with state estimator uses xi without state estimator uses di instead of xi simulation does not match measurements high error translated into optimization process

9 Case Study (Credit Approval) Yes Process Approve d? No Disburse Reject Modeling and Simulation: IBM WebSphere Business Modeller Assembly and deployment IBM WID Monitoring: IBM WSP + IBM Monitor Estimation, decision making and integration: in house

10 State Estimation - Results Modeling Error - the absolute difference between the end to end simulated duration measured end to end response time(kpi) averaged over the number of window sequences End to End Dur ration (ms) 6 x smoothed monitored data simulation with particle filter simulation without particle filter Window Sequence Number Experiments modeling error: no estimation used ms particle filter used -.96 ms

11 Using the Simulation Model to Evolve the Process Yes 9% Disburs 33.3% Process Approved? 33.3% Process Clone 33.3% Process Clone 2 No 1% Reject Yes Disburse 5% Disburse Process Approve d? 5% Process 9% Yes 5% Disburse Approved? Clone No Reject 5% Process Clone 1% No Reject 5% Disburse 33.3% Process 9% Yes 5% Approved? Disburse Clone 33.3% Process Clone 1% No Reject 33.3% Process Clone 2

12 Scaling with Different Configurations End to End Dura ation (ms) 15 x no clones two process clones two process clone and one disburse clone one process clone one process and one disburse clone no clones to one process and one disburse clone no clones to two process clones no clones to two process and one disburse clone InterArrival Time (seconds)

13 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

14 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

15 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

16 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

17 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

18 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

19 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

20 BPM optimization application EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

21 BPM Deployment Optimization EndtoEnd d Duration (ms) 15 x configuration (a) one process clone configuration configuration (c) configuration (d) SLA threshold optimal solution number of clones added Time

22 Conclusions & Future work Conclusions: feedback based evolution architecture impact on business process optimization focused on Manage loop centered on: monitoring estimation simulation Further work and future challenges include: validation of the architecture on larger scale business (with other types of estimators) modeled IT resources in our implementation future : human resources (more challenging) automating the optimization of IT resources allocation prediction of future states and KPIs: linear regression autoregressive models techniques - to predict trends from historical data using the correlation between metrics implement an optimization algorithm: reallocating resources automatically guides the business user

23 Thank you. Questions?

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