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MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013

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Cloud computing has enabled a wide array of applications to be exposed as elastic cloud services. While the number of such services has rapidly increased, there is a lack of techniques for supporting cross-layered multi-level monitoring and analysis of elastic service behavior. In this paper we introduce novel concepts, namely elasticity space and elasticity pathway, for understanding elasticity of cloud services, and techniques for monitoring and evaluating them. We present MELA, a customizable framework that enables service providers and developers to analyze cross-layered, multi-level elasticity of cloud services, from the whole cloud service to service units, based on service structure dependencies. Besides support for real-time elasticity analysis of cloud service behavior, MELA provides several customizable features for extracting functions and patterns that characterize that behavior. To illustrate the usefulness of MELA, we conduct several experiments with a realistic data-as-a-service in an M2M cloud platform.
Prototype and Demos at http://tuwiendsg.github.io/MELA/
Paper DOI: http://dx.doi.org/10.1109/CloudCom.2013.18

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MELA: Monitoring and Analyzing Elasticity of Cloud Services -- CloudCom 2013

  1. 1. MELA: Monitoring and Analyzing Elasticity of Cloud Services Daniel Moldovan, Georgiana Copil, Hong-Linh Truong, Schahram Dustdar Distributed Systems Group (http://dsg.tuwien.ac.at/) Vienna University of Technology (http://www.tuwien.ac.at/) Work partially supported by the European Commission in terms of the CELAR FP7 project (http://www.celarcloud.eu/)
  2. 2. Motivation Elastic Cloud Service  Data-as-a-Service for Machine to Machine platforms  Load balancer distributes incoming requests to Event Processing instances  Distributed Data Store: Controller and Nodes Start with an initial lighter configuration 2
  3. 3. Motivation Elastic Cloud Service  Data-as-a-Service for Machine to Machine platforms  Load balancer distributes incoming requests to Event Processing instances  Distributed Data Store: Controller and Nodes Add service unit instance when load increases 2
  4. 4. Motivation Elastic Cloud Service  Data-as-a-Service for Machine to Machine platforms  Load balancer distributes incoming requests to Event Processing instances  Distributed Data Store: Controller and Nodes 2 Remove service unit instance when load decreases
  5. 5. Motivation Elastic Cloud Service  Data-as-a-Service for Machine to Machine platforms  Load balancer distributes incoming requests to Event Processing instances  Distributed Data Store: Controller and Nodes 2 Add service unit instance and data node instance when load increases too much
  6. 6. Motivation Insufficient Cloud Service Monitoring and Analysis Support  Service Level Monitoring  Response time  Number of clients  Other specific metrics Controlling the service’s elasticity User-Defined Requirements violation: - Cost per client too high Reasons: - Too much logging? Monitoring chatter? - Too expensive VMs? Which one can be downsized? - Not enough clients? Why?  System Level Monitoring  Ganglia, Nagios, etc.  CPU usage  Memory usage  Network transfer 3
  7. 7. Approach and Challenges  Structure Monitoring Data  How to map system data to service level?  How to derive higher level information? Monitoring Data Service Structure Impose service structure over collected monitoring data 4
  8. 8. Multi-Level Monitoring Snapshot Metrics composition and enrichment 5
  9. 9. Multi-Level Monitoring Snapshot 5
  10. 10. Multi-Level Monitoring Snapshot Enrich metric with COST information COST/VM * numberOfVMs 5
  11. 11. Multi-Level Monitoring Snapshot Propagate activeConnections from LoadBalancer service unit 5
  12. 12. Multi-Level Monitoring Snapshot 5
  13. 13. Multi-Level Monitoring Snapshot 5
  14. 14. Multi-Level Monitoring Snapshot Compute cost/client/h 5
  15. 15. Approach and Challenges  Evaluate Service’s Elasticity  How to characterize service elasticity?  How to derive service‘s behavior limits?  How to characterize and predict elasticity behavior? 6
  16. 16. Runtime Properties of Elastic Cloud Services  Background  Elastic process: cost, quality and resources elasticity  Extend concept to cloud services  Elasticity Space  Collection of monitoring snapshots  I.e. the space in which an elastic service moves  Elasticity Boundary  Elasticity Space boundaries in which service’s requirements are respected  Elasticity Pathway  Characterizes service evolution trough elasticity space 16 Elasticity Dimensions
  17. 17. Multi-Level Elasticity Space Event Processing Topology  Service requirement  COST<= 0.0034$/client/h  2.5$ monthly subscription for each service client (sensor) Elasticity Space “Clients/h” Dimension Elasticity Space Snapshot Elasticity Space “Response Time” Dimension 8
  18. 18. Multi-Level Elasticity Space Event Processing Topology  Service requirement  COST<= 0.0034$/client/h  2.5$ monthly subscription for each service client (sensor)  Determined Elasticity Space Boundaries  Clients/h > 148  300ms ≤ ResponseTime ≤ 1100 ms Elasticity Space “Clients/h” Dimension Elasticity Space “Response Time” Dimension 8
  19. 19. Multi-Level Elasticity Pathway  Service requirement  COST<= 0.0034$/client/h  2.5$ monthly subscription for each service client (sensor) 9
  20. 20. Multi-Level Elasticity Pathway  Service requirement  COST<= 0.0034$/client/h  2.5$ monthly subscription for each service client (sensor) Cloud Service Elasticity Pathway 9
  21. 21. Multi-Level Elasticity Pathway  Service requirement  COST<= 0.0034$/client/h  2.5$ monthly subscription for each service client (sensor) Cloud Service Elasticity Pathway 9 Event Processing service unit Elasticity Pathway
  22. 22. Conclusions  Concepts  Elasticity Space and Elasticity Boundary  Elasticity Pathway  Mechanisms  Constructing cross-layer monitoring snapshots  Determining elasticity space and boundary  Determining elasticity pathway  MELA  Customizable framework for monitoring and analyzing elasticity of cloud services MELA: Monitoring and Analyzing Elasticity of Cloud Services http://dsg.tuwien.ac.at/research/viecom/mela/ Distributed Systems Group(http://dsg.tuwien.ac.at/) Vienna University of Technology (http://www.tuwien.ac.at/) Work partially supported by the European Commission in terms of the CELAR FP7 project (http://www.celarcloud.eu/) 10

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