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Multi-level Elasticity Control of Cloud
Services
Georgiana Copil, Daniel Moldovan,
Hong-Linh Truong, Schahram Dustdar

Dis...
Overview
 Motivation

 Mapping Services Structures to Elasticity Metrics
 Multi-level Control Runtime
 Experiments
 C...
Motivation
 Traditional approach to cloud service control
– Consider specific types of cloud services
– Assume optimizati...
Our Approach
 Use multi-level elasticity requirements, for knowing how
to control the cloud service
 Place modeling the ...
Our Approach
 Use multi-level elasticity requirements, for knowing how
to control the cloud service
 Place modeling the ...
High Level Description of Elasticity
Requirements
 SYBL (Simple Yet Beautiful
Language) for specifying
elasticity require...
Our Approach
 Use multi-level elasticity requirements, for knowing how
to control the cloud service
 Place modeling the ...
Mapping Services Structures to Elasticity
Metrics

ICSOC, 11 December 2013

8
Our Approach
 Use multi-level elasticity requirements, for knowing how
to control the cloud service
 Place modeling the ...
Multi-level Control Runtime:
Generating Elasticity Control Plans

Cloud Providers/Tools must
support higher and richer
API...
Multi-level Control Runtime:
Elasticity Control Prototype rSYBL

ICSOC, 11 December 2013

11
Experiments - Setup
 Test Infrastructure:
– Local cloud running OpenStack
– Ganglia and Hyperic SIGAR for monitoring
– JC...
Experiments – Results [1/1]
Configuration

Controllers

DB
Nodes

Total execution
time

Cost

Config1

1

3

578.4 s

0.48...
Experiments – Results [1/1]
Configuration

Controllers

DB
Nodes

Total execution
time

Cost

Config1

1

3

578.4 s

0.48...
Experiments – Results [2/2]

ICSOC, 11 December 2013

15
SYBL & MELA

ICSOC, 11 December 2013

16
Conclusion and Future Work
 Using SYBL, cloud providers could sell elasticity as a
service to cloud consumers
 SYBL and ...
Thanks for your attention!
Georgiana Copil

e.copil@dsg.tuwien.ac.at
http://www.infosys.tuwien.ac.at/staff/ecopil/
Distrib...
References
1.

2.

3.

Dimitrios Tsoumakos, Ioannis Konstantinou, Christina Boumpouka, Spyros Sioutas, Nectarios
Koziris, ...
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Multi-level Elasticity Control of Cloud Services -- ICSOC 2013

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Presentation given at ICSOC 2013

Abstract: Fine-grained elasticity control of cloud services has to deal with multiple elasticity perspectives (quality, cost, and resources). We propose a cloud services elasticity control mechanism that considers the service structure for controlling the cloud service elasticity at multiple levels, by firstly defining an abstract composition model for cloud services and enabling multi-level elasticity control. Secondly, we define mechanisms for solving conflicting elasticity requirements and generating action plans for elasticity control. Using the defined concepts and mechanisms we develop a runtime system supporting multiple levels of elasticity control and validate the resulted prototype through experiments.

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Multi-level Elasticity Control of Cloud Services -- ICSOC 2013

  1. 1. Multi-level Elasticity Control of Cloud Services Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar Distributed Systems Group, Vienna University of Technology
  2. 2. Overview  Motivation  Mapping Services Structures to Elasticity Metrics  Multi-level Control Runtime  Experiments  Conclusions and Future Work ICSOC, 11 December 2013 2
  3. 3. Motivation  Traditional approach to cloud service control – Consider specific types of cloud services – Assume optimization strategies on behalf of the user – Do not consider cloud service structure Tiramola [1] Control of NoSQL Clusters ICSOC, 11 December 2013 KingFisher [2] Cost-aware Provisioning 3 Cloud Applications Auto-Scaling [3]
  4. 4. Our Approach  Use multi-level elasticity requirements, for knowing how to control the cloud service  Place modeling the cloud service and its environment at the center of the approach  Generate plans of abstract actions for elasticity control ICSOC, 11 December 2013 4
  5. 5. Our Approach  Use multi-level elasticity requirements, for knowing how to control the cloud service  Place modeling the cloud service and its environment at the center of the approach  Generate plans of abstract actions for elasticity control ICSOC, 11 December 2013 5
  6. 6. High Level Description of Elasticity Requirements  SYBL (Simple Yet Beautiful Language) for specifying elasticity requirements  SYBL-supported requirement levels – – – – – Cloud Service Level Service Topology Level Service Unit Level Relationship Level Programming/Code Level #SYBL.CloudServiceLevel Cons1: CONSTRAINT responseTime < 5 ms Cons2: CONSTRAINT responseTime < 10 ms WHEN nbOfUsers > 10000 Str1: STRATEGY CASE fulfilled(Cons1) OR fulfilled(Cons2): minimize(cost) #SYBL.ServiceUnitLevel Str2: STRATEGY CASE ioCost < 3 Euro : maximize( dataFreshness ) #SYBL.CodeRegionLevel Cons4: CONSTRAINT dataAccuracy>90% AND cost<4 Euro [Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar, "SYBL: an Extensible Language for Controlling Elasticity in Cloud Applications", 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), May 14-16, 2013, Delft, Netherlands] ICSOC, 11 December 2013 6
  7. 7. Our Approach  Use multi-level elasticity requirements, for knowing how to control the cloud service  Place modeling the cloud service and its environment at the center of the approach  Generate plans of abstract actions for elasticity control ICSOC, 11 December 2013 7
  8. 8. Mapping Services Structures to Elasticity Metrics ICSOC, 11 December 2013 8
  9. 9. Our Approach  Use multi-level elasticity requirements, for knowing how to control the cloud service  Place modeling the cloud service and its environment at the center of the approach  Generate plans of abstract actions for elasticity control ICSOC, 11 December 2013 9
  10. 10. Multi-level Control Runtime: Generating Elasticity Control Plans Cloud Providers/Tools must support higher and richer APIs for elasticity controls ICSOC, 11 December 2013 10
  11. 11. Multi-level Control Runtime: Elasticity Control Prototype rSYBL ICSOC, 11 December 2013 11
  12. 12. Experiments - Setup  Test Infrastructure: – Local cloud running OpenStack – Ganglia and Hyperic SIGAR for monitoring – JClouds for controlling virtual machine instances. ICSOC, 11 December 2013 12
  13. 13. Experiments – Results [1/1] Configuration Controllers DB Nodes Total execution time Cost Config1 1 3 578.4 s 0.48 Config2 1 6 472.1 s 0.91 Config3 2 2 382.4 s 0.42 Config4 3 7 372.2 s 0.72 ICSOC, 11 December 2013 13 Service unit level Service topology level
  14. 14. Experiments – Results [1/1] Configuration Controllers DB Nodes Total execution time Cost Config1 1 3 578.4 s 0.48 Config2 1 6 472.1 s 0.91 Config3 2 2 382.4 s 0.42 Config4 3 7 372.2 s 0.72 Service unit level Service topology level Configuration Controllers DB Nodes Workload Total Cost execution time Config1 1 3 Workload 1 44 min 2.92 Service unit level Config3 2 2 Workload 1 28.4 min 1.88 Service topology level Config1 1 3 Workload 2 >3h+errors >12 Config3 2 2 Workload 2 102.75 min 6.88 ICSOC, 11 December 2013 14
  15. 15. Experiments – Results [2/2] ICSOC, 11 December 2013 15
  16. 16. SYBL & MELA ICSOC, 11 December 2013 16
  17. 17. Conclusion and Future Work  Using SYBL, cloud providers could sell elasticity as a service to cloud consumers  SYBL and its runtime rSYBL enable multi-level elasticity control of cloud services  Future work – Elasticity behavior analysis – New/improved algorithms for the decision process  Visit SYBL webpage – http://www.infosys.tuwien.ac.at/research/viecom/SYBL – Tomorrow demo session: SYBL+MELA Demo ICSOC, 11 December 2013 17
  18. 18. Thanks for your attention! Georgiana Copil e.copil@dsg.tuwien.ac.at http://www.infosys.tuwien.ac.at/staff/ecopil/ Distributed Systems Group Vienna University of Technology Austria ICSOC, 11 December 2013 18
  19. 19. References 1. 2. 3. Dimitrios Tsoumakos, Ioannis Konstantinou, Christina Boumpouka, Spyros Sioutas, Nectarios Koziris, "Automated, Elastic Resource Provisioning for NoSQL Clusters Using TIRAMOLA," CCGRID, pp.34-41, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013 Upendra Sharma; Shenoy, P.; Sahu, S.; Shaikh, A., "A Cost-Aware Elasticity Provisioning System for the Cloud," Distributed Computing Systems (ICDCS), 2011 31st International Conference on , vol., no., pp.559,570, 20-24 June 2011, doi: 10.1109/ICDCS.2011.59 Jing Jiang; Jie Lu; Guangquan Zhang; Guodong Long, "Optimal Cloud Resource Auto-Scaling for Web Applications," Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on , vol., no., pp.58,65, 13-16 May 2013 doi: 10.1109/CCGrid.2013.73 ICSOC, 11 December 2013 19

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