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Cluster Computing

Erschienen in:

01.12.2014

Towards a framework for monitoring cloud application platforms as sensor networks

verfasst von: Rustem Dautov, Iraklis Paraskakis, Mike Stannett

Erschienen in: Cluster Computing | Ausgabe 4/2014

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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.

Vorheriger Artikel Scalable and effective peer-to-peer desktop grid system

Nächster Artikel Towards an integrated management system based on abstraction of heterogeneous virtual resources

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

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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.

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.

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

CADA: Collect, Analyse, Detect, Act.

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].

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.

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

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

http://www.opengeospatial.org/

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

https://www.cleardb.com/

https://www.heroku.com/postgres

http://www.mongohq.com/

https://redislabs.com/redis-cloud

https://www.memcachier.com/

http://redislabs.com/memcached-cloud

http://www.cloudfoundry.com/

http://www.rabbitmq.com/

http://www.appscale.com/

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Titel: Towards a framework for monitoring cloud application platforms as sensor networks
verfasst von: Rustem Dautov
Iraklis Paraskakis
Mike Stannett
Publikationsdatum: 01.12.2014
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe 4/2014
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-014-0389-5

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