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S. Qanbari, A. Farivarmoheb, P. Fazlali, S. Mahdi Zadeh, S. Dustdar:
"Telemetry for Elastic Data (TED): Middleware for MapReduce Job Metering and Rating";
Talk: 9th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2015), Helsinki, Finland; 2015-08-20 - 2015-08-22; in: "Proceedings of the 9th IEEE International Conference on Big Data Science and Engineering (IEEE BigDataSE 2015), Volume II", IEEE Computer Society, (2015), ISBN: 978-1-4673-7951-9; 104 - 111.

The consumption-based rating of MapReduce jobs is tightly coupled with metering the infrastructure resource usage it runs on. In this context, metering and controlling the job execution depends on the number and type of containers used to setup and run the Hadoop cluster as well as the duration of the job execution. Duration-basis metering like an hourly rate for every instance per hour usage, poses challenges of surcharge of jobs lasting less/more than an hour. Jobs lasting for 61 minutes will unfairly be charged for two hours. In response to these findings, the authors offer Job-basis telemetry mechanism rather than Duration-basis where the metering granularity is carried on MapReduce DAG bundles, jobs and tasks levels. This model is developed as an elastic data telemetry (TED) middleware to provide real-time resource utilization awareness over data-intensive applications. Clients will benefit of this model by enforcing their applications elasticity policies and achieve pricing transparency over their actual usage. This granular elasticity control is achieved by moving jobs among priority queues which fit cost and quality requirements. TED collects the emitted usage data stream, generates billable artifacts to form a tailored policy (scale up/in) to satisfy several desirable properties. This contributes to a supervised, finer-grained resource allocation due to application behavior.

http://dx.doi.org/10.1109/Trustcom.2015.568