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Towards a framework for monitoring cloud application platforms as sensor networks

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Abstract

With the continued growth in software environments on cloud application platforms, self-management at the Platform-as-a-Service (PaaS) level has become a pressing concern, and the run-time monitoring, analysis and detection of critical situations are all fundamental requirements if we are to achieve autonomic behaviour in complex PaaS environments. In this paper we focus on cloud application platforms offering their customers a range of generic built-in re-usable services. By identifying key characteristics of these complex dynamic systems, we compare cloud application platforms to distributed sensor networks, and investigate the viability of exploiting these similarities with a case study. We treat cloud data storage services as “virtual” sensors constantly emitting monitoring data, such as numbers of connections and storage space availability, which are then analysed by the central component of a monitoring framework so as to detect and react to SLA violations. We discuss the potential benefits, as well as some shortcomings, of adopting this approach.

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Notes

  1. Heroku, for example, provides upto 150 add-ons (http://addons.heroku.com).

  2. http://appengine.google.com/

  3. http://www.windowsazure.com/

  4. http://www.heroku.com/

  5. It should perhaps be noted that we are not attempting here to compete with existing approaches for run-time monitoring in clouds, but rather to offer complementary concepts, which can be reused when developing cloud monitoring mechanisms.

  6. Throughout this paper, we use the terms cloud application platform, cloud platform, service-based cloud, servicebased cloud platform, PaaS, aPaaS, etc. interchangeably to refer to the same concept.

  7. MAPE-K: Monitor, Analyse, Plan, Execute, Knowledge.

  8. CADA: Collect, Analyse, Detect, Act.

  9. We focus in this paper on run-time monitoring. Related activities can also include such techniques as post-mortem log analysis, data mining, and online or offline testing—the interested reader is referred to [15].

  10. Intrusive monitoring requires the monitored subject to be instrumented with probes to facilitate inspection of its characteristics. As with code instrumentation, it is essential that this is done with care, since the instrumentation can itself potentially affect the subject’s performance, providing a flawed picture of its inherent capabilities.

  11. http://gigaom.com/2012/05/04/heroku-boss-1-5m-apps-many-not-in-ruby/

  12. http://www.crunchbase.com/company/heroku

  13. http://www.opengeospatial.org/

  14. http://www.opengeospatial.org/projects/groups/sensorwebdwg

  15. https://www.cleardb.com/

  16. https://www.heroku.com/postgres

  17. http://www.mongohq.com/

  18. https://redislabs.com/redis-cloud

  19. https://www.memcachier.com/

  20. http://redislabs.com/memcached-cloud

  21. http://www.cloudfoundry.com/

  22. http://www.rabbitmq.com/

  23. http://www.appscale.com/

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Acknowledgments

The authors would like to thank Dimitrios Kourtesis for his valuable suggestions and comments on the contents of the paper.

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Authors and Affiliations

  1. South-East European Research Centre, CITY College, 24 Proxenou Koromila, 54622 , Thessaloniki, Greece

    Rustem Dautov & Iraklis Paraskakis

  2. Department of Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield , S1 4DP, United Kingdom

    Mike Stannett

Authors
  1. Rustem Dautov
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  2. Iraklis Paraskakis
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  3. Mike Stannett
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Corresponding author

Correspondence to Rustem Dautov.

Additional information

The research leading to these results has received funding from the European Union’s Seventh Framework Programme (FP7-PEOPLE-2010-ITN) under grant agreement n\(^\circ \) 264840 (http://www.relate-itn.eu/).

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Dautov, R., Paraskakis, I. & Stannett, M. Towards a framework for monitoring cloud application platforms as sensor networks. Cluster Comput 17, 1203–1213 (2014). https://doi.org/10.1007/s10586-014-0389-5

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