Cost-aware scalability of applications in public clouds
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The document discusses the cost-aware scalability of applications in public clouds, focusing on an elastic cloud platform designed for smart environments. It emphasizes the need for elastic capabilities in cloud-based applications to manage unpredictable loads while controlling operational costs. The authors propose a model for capturing cloud pricing schemes and a framework for monitoring elasticity to improve cost efficiency in scalable applications.
Cost-aware scalability of applications in public clouds
- 1. Daniel Moldovan Hong-Linh Truong, Schahram Dustdar Cost-aware scalability of applications in public clouds Distributed Systems Group (http://dsg.tuwien.ac.at/) Vienna University of Technology (http://www.tuwien.ac.at/) International Conference on Cloud Engineering, IC2E 2016, IEEE Berlin, Germany, 4-8 April, 2016
- 2. Motivating Scenario Elastic cloud platform for smart environments (1/3) Context Company offering services for maintenance of smart environments E.g., buildings or vehicle fleets Application Cloud-based application storing, retrieving and analyzing data collected from sensors Elasticity through Horizontal Scalability Unpredictable load Adapt to varying demand, and keep operating costs down for price competitiveness. 2 Design time view of Data-as-a-Service cloud system for IoT
- 3. Motivating Scenario Elastic cloud platform for smart environments (2/3) Horizontally Scalable Components Local Data Processing Event Processing Data Node 3 Data-as-a-Service elastic cloud system for IoT with elasticity capabilities To make an application elastic, elasticity capabilities need to be implemented and provided for application components.
- 4. Motivation Elastic cloud platform for smart environments (3/3) 4 Time Load Time Performance Cost Time Time Used cloud services count Driving factor for Scale Out Driving factor for Scale In An elastic application must be able to add/remove cloud services on demand. Scaling Out/Up is usually due to performance-related issues. Scale In/Down however is usually motivated by cost issues.
- 5. Motivation Cost Complexity: Configuration of used cloud services 5 Used cloud offered services Example: Data Node deployed on Amazon EC2 When deploying applications in public clouds, cost can be very complex. Even a single application component, on Amazon might use a VM service, a Storage service, Monitoring and Network services, all billed and paid differently.
- 6. 6 Motivation Cost Complexity: Cloud provider pricing scheme Example: Flexiant Cloud pricing scheme With few exceptions, cloud pricing schemes can also be complex, costs being reported over different metrics, with respect to certain usage or reservation time intervals.
- 7. 7 Evaluating Costs of Elastic Applications Approach
- 8. Managing Cloud Pricing Schemes
- 9. Evaluating Costs of Elastic Applications Modelling cloud pricing schemes 9 Flexiant Cloud pricing scheme We define a model for capturing pricing schemes of public cloud providers.
- 10. 10 Evaluating Costs of Elastic Applications Cloud pricing scheme fluent API We introduce a fluent API for describing pricing schemes of any complexity.
- 11. Managing Structure of Elastic Cloud Applications
- 12. 12 Evaluating Costs of Elastic Applications Managing application structure We introduce a fluent API for describing the structure of elastic applications in terms of application components and cloud services used by each component The resource and quality properties are needed because on some cloud providers you can allocate one cloud service with different extra options at different cost. For example on Amazon, you can allocate on VM type with or without EBS support, at different cost.
- 13. 13 Evaluating Costs of Elastic Applications Example: Elastic cloud platform for smart environments on Flexiant Visualization generated by our tool of one application tier, with the cloud services used by each application component.
- 14. Monitor Elastic Cloud Applications
- 15. Motivation Required monitoring information to compute cost 15 To compute costs for individual application components, the necessary monitoring information according to the billing metrics must be collected and structured.
- 16. Background: Monitoring Elastic Cloud Applications MELA: Structuring monitoring information 16 … Data Node UnitData Node Unit Data Controller Unit Data Controller Unit Data End Topology Data End Topology … Event Processing Topology Event Processing Topology Elastic DaaS Elastic DaaS … Unit InstanceUnit Instance Unit InstanceUnit Instance m m Custom metric aggregation and structuring <rule> := operation "=>" metric <operation>:= operator "(" operand { "," operand } ")" <operator> := "+"|"-"|"*"|"/"|"AVG"|"MAX"|"MIN“ |"CONCAT"|"FIRST"|"LAST"|"SET" <operand> := metric | number | string Daniel Moldovan , Georgiana Copil, Hong-Linh Truong, Schahram Dustdar, "MELA: Monitoring and Analyzing Elasticity of Cloud Services ", 5'th International Conference on Cloud Computing Technology and Science (CloudCom). Bristol, UK, 2-5 December, 2013. Daniel Moldovan , Georgiana Copil, Hong-Linh Truong, Schahram Dustdar, "MELA: Monitoring and Analyzing Elasticity of Cloud Services ", 5'th International Conference on Cloud Computing Technology and Science (CloudCom). Bristol, UK, 2-5 December, 2013. We use our previous work to structure monitoring information and select what metrics to collect, as a means of enabling detailed costs analysis.
- 17. Background: Monitoring Elastic Cloud Applications Multi-level Monitoring Snapshot 17 With our approach we can structure, enrich and aggregate monitoring information, useful especially considering that elastic applications will have multiple instances of their components.
- 18. Evaluate Costs of Elastic Cloud Applications
- 19. 19 Evaluating Costs of Scalable Cloud Applications Experiments: Elastic cloud platform for smart environments on Flexiant
- 20. Evaluating Costs of Scalable Cloud Applications Experiments: Elastic cloud platform cost composition (1/2) 20 Combining monitoring data structuring and our cost model, we can compute a hierarchic cost decomposition.
- 21. Evaluating Costs of Scalable Cloud Applications Experiments: Elastic cloud platform cost composition (2/2) 21
- 22. Evaluate Cost Efficiency of Cloud Applications
- 23. Service instance j Costs Analysis and Cost-aware Control Cost efficiency of scalable applications: billing fragmentation 23 Cost efficiency if scaled IN (%) Time/Usage Service instance i 100 0 Billing Cycle (e.g., /hour, or /GB of IO) Scale IN: service instance deallocation Scale OUT: service instance allocation Public clouds usually bill rounding up certain usage units (E.g., 1 hour, 1 GB) When scaling in applications in public clouds, one must understand which application component instance is more cost efficient (i.e. used) to scale in, so we do not deallocate paidfor but unused resources.
- 24. Costs Analysis and Cost-aware Control Cost efficiency of scalable applications: cost efficiency formula 24
- 25. Costs Analysis and Cost-aware Control Experiments: Cost-aware scalability of Event Processing Unit on Flexiant 25
- 26. Costs Analysis and Cost-aware Control Experiments: Cost efficiency of Event Processing Unit VM on Flexiant 26
- 27. Costs Analysis and Cost-aware Control Experiments: Cost efficiency of Event Processing Unit VMs on Flexiant 27 Costefficiencyifdeallocated(%) Scale IN Scale IN Scale OUT Scale OUT
- 28. Cost-aware Control of Elastic Cloud Applications
- 29. Costs Analysis and Cost-aware Control Experiments: Cost-aware scalability of Event Processing Unit (1/2) Cost Efficiency Comparison of Scale In Strategies Scale in every 45 minutes 2 Cost agnostic strategies: scale in Last/First added 2 Cost-driven strategies: scale in based on Reservation Cycle/ Overall Cost Efficiency 29
- 30. Costs Analysis and Cost-aware Control Experiments: Cost-aware scalability of Event Processing Unit (2/2) Cost Efficiency Comparison of Scale In Strategies Random 1-3 scale-in/scale-out requests at random time intervals between 30-60 minutes Best cost-agnostic strategy: Deallocating last Added Best cost-aware strategy: Deallocating if Cost Efficiency > 80% 30 Number of event processing instances under cost-aware and cost-agnostic scalability
- 31. Costs Analysis and Cost-aware Control Conclusions Research question How can scalable applications running in public clouds be controlled in a cost efficient manner? Objective Improve cost efficiency of scalable applications running in public clouds Approach Concepts of Composite Cost and Cost efficiency of scalable applications Model for capturing cloud pricing schemes Algorithms for analyzing composite cost and cost efficiency of cloud applications Framework/Tools MELA: Monitoring and analyzing elasticity of cloud applications (http://tuwiendsg.github.io/MELA/) 31 Work partially supported by the European Commission in terms of the CELAR FP7 project (http://www.celarcloud.eu/ ) Distributed Systems Group (http://dsg.tuwien.ac.at/) Vienna University of Technology (http://www.tuwien.ac.at/)