Cost-Efficient Scheduling Of Elastic Processes In Hybrid Clouds

Presentation of our research paper "Cost-Efficient Scheduling Of Elastic Processes In Hybrid Clouds" at the IEEE Cloud 2015 conference in New York, USA.

http://dx.doi.org/10.1109/CLOUD.2015.13

Transcript

1. Cost-Efficient Scheduling Of Elastic Processes In Hybrid Clouds Philipp Hoenisch, Christoph Hochreiner, Dieter Schuller, Stefan Schulte, Jan Mendling, Schahram Dustdar
2. Outline ‣Introduction ‣Motivation ‣Approach ‣Evaluation ‣Conclusion 2
3. Introduction
4. Business Process Management Execute a sequence of software services to provide a value added service to customers [wes07]. 4 Evaluation Process Mining Business Activity Monitoring Enactment Operation Monitoring Maintenance Configuration System Selection Implementation Test and Deployment Design Business Process Identification and Modeling Analysis Validation Simulation Verification Administration and Stakeholders
5. Business Process Management Execute a sequence of software services to provide a value added service to customers [wes07]. 4 Evaluation Process Mining Business Activity Monitoring Enactment Operation Monitoring Maintenance Configuration System Selection Implementation Test and Deployment Design Business Process Identification and Modeling Analysis Validation Simulation Verification Administration and Stakeholders
6. Elastic Processes [dus11] Enabled by elasticity aspects from Cloud Computing. 5 Resource Elasticity Quality Elasticity Cost Elasticity
7. Motivation
8. VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Motivational Scenario 7 Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Service 1 Service 2
9. Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Motivational Scenario 8 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 Service 1 Service 2 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
10. Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Motivational Scenario 8 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 Service 1 Service 2 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
11. Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Motivational Scenario 8 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 Service 1 Service 2 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
12. Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Motivational Scenario 8 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 Service 1 Service 2 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
13. Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Motivational Scenario 8 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 Service 1 Service 2 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
14. Service 1 Service 2 Service 1 Service 2 Service 1 Service 2 Motivational Scenario 8 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 Service 1 Service 2 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
15. Motivational Scenario 9 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
16. Motivational Scenario 9 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
17. Motivational Scenario 9 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
18. Motivational Scenario 9 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
19. Motivational Scenario 9 Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 VM 1 VM 2 VM 3 VM 4 VM 5 VM 6 VM N Private Cloud Public Cloud Service 1 Service 2 Service 3 Service 4 Service 5 Service 6
20. Challenges ‣Minimize the total process execution costs ‣ Collocate process steps with intense data interaction ‣ Achieve efficient resource usage across multiple clouds ‣ Avoid SLA violations ‣Manage a large number of concurrent processes ‣Prioritize private clouds for service execution 10
21. Approach
22. Service Instance Placement Problem (SIPP) [hoe15] Mixed Integer Linear Programming solution based on CPLEX 12
23. Service Instance Placement Problem (SIPP) [hoe15] Mixed Integer Linear Programming solution based on CPLEX 12 ‣Minimize the total process execution costs ‣ Collocate process steps with intense data interaction ‣ Achieve efficient resource usage across multiple clouds ‣ Avoid SLA violations ‣Manage a large number of concurrent processes ‣Prioritize private clouds for service execution
24. Service Instance Placement Problem (SIPP) [hoe15] Mixed Integer Linear Programming solution based on CPLEX 12 ‣Minimize the total process execution costs ‣ Collocate process steps with intense data interaction ‣ Achieve efficient resource usage across multiple clouds ‣ Avoid SLA violations ‣Manage a large number of concurrent processes ‣Prioritize private clouds for service execution within one cloud across multiple clouds
25. Service Instance Placement Problem (SIPP) [hoe15] Mixed Integer Linear Programming solution based on CPLEX 12 ‣Minimize the total process execution costs ‣ Collocate process steps with intense data interaction ‣ Achieve efficient resource usage across multiple clouds ‣ Avoid SLA violations ‣Manage a large number of concurrent processes ‣Prioritize private clouds for service execution within one cloud ✓ across multiple clouds
26. Service Instance Placement Problem (SIPP) [hoe15] Mixed Integer Linear Programming solution based on CPLEX 12 ‣Minimize the total process execution costs ‣ Collocate process steps with intense data interaction ‣ Achieve efficient resource usage across multiple clouds ‣ Avoid SLA violations ‣Manage a large number of concurrent processes ‣Prioritize private clouds for service execution ✓ within one cloud ✓ across multiple clouds
27. Service Instance Placement Problem (SIPP) [hoe15] Mixed Integer Linear Programming solution based on CPLEX 12 ‣Minimize the total process execution costs ‣ Collocate process steps with intense data interaction ‣ Achieve efficient resource usage across multiple clouds ‣ Avoid SLA violations ‣Manage a large number of concurrent processes ‣Prioritize private clouds for service execution ✓ ✓ within one cloud ✓ across multiple clouds
28. Extensions to SIPP ‣Extend the basic model to consider an arbitrary amount of Clouds ‣Consider data transfer costs ‣Allow for a prioritization of selected Clouds ‣Collocate process steps with intense data interaction in the same Cloud 13
29. Evaluation
30. Evaluation Prototype (ViePEP) [sch13] 15 Process Manager Resource Manager Reasoner Service 1 Service 2 Service 5 Service 3 Service 4 Service 6 ViePEP
31. Evaluation ‣10 process models out of SAP R/3 with different complexity ‣10 service models with different CPU usage, makespan and outgoing data transfer ‣2 SLA scenarios with different maximal durations ‣X-VM per service in an ad-hoc manner as baseline [fri14] 16
32. Process request pattern 17 # of Invocations 0 3 5 8 10 Minutes 1 5 10 15 20 Burst arrival Pyramid arrival
33. Evaluation Results 18 Cost 0 750 1500 2250 3000 Baseline Optimized Resource Leasing Cost Data Transfer Cost SLA Violation Cost Burst arrival
34. Evaluation Results 18 Cost 0 750 1500 2250 3000 Baseline Optimized Resource Leasing Cost Data Transfer Cost SLA Violation Cost Burst arrival - 92 % data transfer cost - 61 % total cost
35. Evaluation Results 18 Cost 0 750 1500 2250 3000 Baseline Optimized Resource Leasing Cost Data Transfer Cost SLA Violation Cost Burst arrival Cost 0 1250 2500 3750 5000 Baseline Optimized Pyramid arrival - 92 % data transfer cost - 61 % total cost
36. Evaluation Results 18 Cost 0 750 1500 2250 3000 Baseline Optimized Resource Leasing Cost Data Transfer Cost SLA Violation Cost Burst arrival Cost 0 1250 2500 3750 5000 Baseline Optimized Pyramid arrival - 85 % data transfer cost - 44 % total cost - 92 % data transfer cost - 61 % total cost
37. Conclusion
38. Contributions ‣Demonstrate that considering data transfer costs can decrease the total execution cost ‣Provide a model to optimize elastic process execution across more than one Cloud ‣Provide a model to collocate process steps with intense data communication within one Cloud ‣Provide a model to prioritize one cloud instance while maintaining a high Quality of Service 20
39. Future Work ‣Consider privacy restrictions for service executions ‣Support more complex system architectures and data communication pattern ‣Consider stateful services ‣Evaluate different resource pricing and SLAs 21
40. Q & A Christoph Hochreiner c.hochreiner@infosys.tuwien.ac.at
41. References [dus11] S. Dustdar, Y. Guo, B. Satzger and Truong, Hong-Linh. Principles of Elastic Processes. IEEE Internet Computing 15, 5 (2011). [fri14] S. G. Frincu and J. Gossa. On the Efficiency of Several VM Provisioning Strategies for Workflows with Multi-threaded Tasks on Clouds. IEEE Computing, vol. 96 (2014). [hoe15] P. Hoenisch, D. Schuller, S. Schulte, C. Hochreiner and S. Dustdar. Optimization of Complex Elastic Processes. IEEE Transactions on Services Computing (2015). [sch13] S. Schulte, P. Hoenisch, S. Venugopal and S. Dustdar. Introducing the Vienna Platform for Elastic Processes. ICSOC Workshops (2013). [wes07] M. Weske. Business process management: concepts, languages, architectures. Springer Heidelberg Dordrecht London New York (2007). 23