Controlling Cloud Services Elasticity in Heterogeneous Clouds - UCC 2014 - CloudControl6
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The document discusses controlling cloud services elasticity in heterogeneous cloud environments, focusing on research challenges and approaches to manage relationships among different cloud providers. It emphasizes the need for a tailored control model that accounts for various application dependencies and explores enforcement strategies through experimental results. The conclusion suggests leveraging cloud heterogeneity to enhance application control and decision-making processes for resource management.
Controlling Cloud Services Elasticity in Heterogeneous Clouds - UCC 2014 - CloudControl6
- 1. Controlling Cloud Services Elasticity in Heterogeneous Clouds Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar Vienna University of Technology 7th IEEE/ACM International Conference on Utility and Cloud Computing 6th Cloud Control Workshop
- 2. Overview Background Motivation Approach Results Conclusions UCC 2014, 8-11 December, London 2
- 3. Background Application model: structural and runtime information Control Application Monitoring UCC 2014, 8-11 December, London 3 Control processes Metrics monitoring information Elasticity requirements Application description Aggregated information Elasticity behavior analysis
- 4. Motivation Should we control it uniformly or leverage Single application: the controller has an end-to-end view of the application UCC 2014, 8-11 December, London 4 cloud heterogeneity? How can we take into consideration relationships among components deployed on different cloud providers?
- 5. Research problems and approach (Q) Should we control it uniformly or leverage cloud heterogeneity? – Heterogeneous control – understand a variety of control primitives and protocols from a variety of providers (Q) How can we take into consideration relationships among components from different cloud providers? – Model relationship types, and consider them in the control process UCC 2014, 8-11 December, London 5
- 6. Enforcement on heterogeneous clouds Extend rSYBL to support heterogeneous cloud control – Supporting multiple enforcement plugins simultaneously – Parallelization of actions on multiple enforcement plugins UCC 2014, 8-11 December, London 6
- 7. Application model for heterogeneous clouds Extending the model for heterogeneous cloud application control UCC 2014, 8-11 December, London 7
- 8. Application model for heterogeneous clouds Extending the infrastructure system description for heterogeneous cloud application control – Supporting the description of complex systems - various virtual resources from multiple cloud providers UCC 2014, 8-11 December, London 8
- 9. Application model for heterogeneous clouds Elasticity relationships description for understanding control dependencies – Single way load dependency • a change in the antecedent load causes a similar change in consequent load – Two way load dependency • a change in one side causes a similar change in the other – Instantiation dependency • for the instantiation of the consequent the antecedent should to exist – Data dependency • the specified data should to be transferred among the two – Polynomial relationship • a polynomial function describes the connection between the two components UCC 2014, 8-11 December, London 9
- 10. Decision on heterogeneous clouds Elasticity Requirements Elasticity Relationships Metrics monitoring information Application level control Decision process Evaluate control actions Simulate relationships UCC 2014, 8-11 December, London 10 effects Affected Components components to re-evaluate Cloud resources control
- 11. Experiments - Settings CONSTRAINT avgBufferSize<50 CONSTRAINT responseTime<50 ms bufferSize requests M2MDaaS: STRATEGY CASE avgBufferSize<5 : minimize(cost) UCC 2014, 8-11 December, London 11 STRATEGY CASE responseTime<40ms AND throughput<20ops/s : scalein Load relationship
- 12. Experiments – Results Action enforced on local processing due to elasticity requirement Buffer size expected to increase Compensation action enforced UCC 2014, 8-11 December, London 12
- 13. Experiments – Results Application cost evolving to accommodate varying load on two providers UCC 2014, 8-11 December, London 13
- 14. Conclusions and Future Work Profiting from clouds heterogeneity – We can fix a variety of issues using the same tools – Controlling applications as a whole when running over multiple clouds strengthens the decision capacity Understanding application distribution – Understanding relationships which may appear across distributed components Ongoing work – Determining elasticity relationships – Improving the analysis/decision process UCC 2014, 8-11 December, London 14
- 15. Thank you! Georgiana Copil e.copil@dsg.tuwien.ac.at http://dsg.tuwien.ac.at/staff/ecopil/ Prototypes available http://tuwiendsg.github.io Distributed Systems Group Vienna University of Technology Austria UCC 2014, 8-11 December, London 15
- 16. Backup slide – sensitivity analysis UCC 2014, 8-11 December, London 16