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.

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. Overview
 Motivation
 Mapping Services Structures to Elasticity Metrics
 Multi-level Control Runtime
 Experiments
 Conclusions and Future Work
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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
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KingFisher [2] Cost-aware Provisioning
3
Cloud Applications Auto-Scaling [3]

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
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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
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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]
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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
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8. Mapping Services Structures to Elasticity
Metrics
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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
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10. Multi-level Control Runtime:
Generating Elasticity Control Plans
Cloud Providers/Tools must
support higher and richer
APIs for elasticity controls
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11. Multi-level Control Runtime:
Elasticity Control Prototype rSYBL
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12. Experiments - Setup
 Test Infrastructure:
– Local cloud running OpenStack
– Ganglia and Hyperic SIGAR for monitoring
– JClouds for controlling virtual machine instances.
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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
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Service
unit level
Service
topology
level

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
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15. Experiments – Results [2/2]
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16. SYBL & MELA
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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
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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
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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
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