Multi-level Elasticity Control of Cloud Services

Georgiana Copil, Daniel Moldovan, Hong-Linh Truong, Schahram Dustdar

Distributed Systems Group, Vienna University of Technology

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Overview

Motivation

Mapping Services Structures to Elasticity Metrics

Multi-level Control Runtime

Experiments

Conclusions and Future Work

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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 KingFisher [2] Cost-aware Provisioning Cloud Applications Auto-Scaling [3]

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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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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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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.CloudServiceLevelCons1: CONSTRAINT responseTime < 5 ms Cons2: CONSTRAINT responseTime < 10 ms WHEN nbOfUsers > 10000Str1: STRATEGY CASE fulfilled(Cons1) OR fulfilled(Cons2): minimize(cost)

#SYBL.ServiceUnitLevelStr2: STRATEGY CASE ioCost < 3 Euro : maximize( dataFreshness )

#SYBL.CodeRegionLevelCons4: 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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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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Mapping Services Structures to Elasticity Metrics

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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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Multi-level Control Runtime:Generating Elasticity Control Plans

Cloud Providers/Tools must support higher and richer APIs for elasticity controls

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Multi-level Control Runtime: Elasticity Control Prototype rSYBL

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Experiments - Setup

Test Infrastructure:– Local cloud running OpenStack– Ganglia and Hyperic SIGAR for monitoring– JClouds for controlling virtual machine instances.

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Experiments – Results [1/1]

Configuration

Controllers

DBNodes

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 topologylevel

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Experiments – Results [1/1]

Configuration

Controllers

DBNodes

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 topologylevel

Configuration

Controllers

DBNodes

Workload Total execution time

Cost

Config1 1 3 Workload 1 44 min 2.92

Config3 2 2 Workload 1 28.4 min 1.88

Config1 1 3 Workload 2 >3h+errors >12

Config3 2 2 Workload 2 102.75 min 6.88

Service unit level

Service topologylevel

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Experiments – Results [2/2]

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SYBL & MELA

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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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Thanks for your attention!

Georgiana Copil

e.copil@dsg.tuwien.ac.athttp://www.infosys.tuwien.ac.at/staff/ecopil/

Distributed Systems GroupVienna University of TechnologyAustria

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References

1. 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

2. 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

3. 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 2013doi: 10.1109/CCGrid.2013.73