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arXiv · 1610.00125

Dithen: A Computation-as-a-Service Cloud Platform For Large-Scale Multimedia Processing

Joseph Doyle·Vasileios Giotsas·Mohammad Ashraful Anam·Yiannis Andreopoulos
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Abstract

We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel execution of large-scale multimedia tasks. Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to minimize the cloud infrastructure cost under deadline-abiding execution. Dithen combines three key properties: (i) the reactive assignment of individual multimedia tasks to available computing units according to availability and predetermined time-to-completion constraints; (ii) optimal resource estimation based on Kalman-filter estimates; (iii) the use of additive increase multiplicative decrease (AIMD) algorithms (famous for being the resource management in the transport control protocol) for the control of the number of units servicing workloads. The deployment of Dithen over Amazon EC2 spot instances is shown to be capable of processing more than 80,000 video transcoding, face detection and image processing tasks (equivalent to the processing of more than 116 GB of compressed data) for less than $1 in billing cost from EC2. Moreover, the proposed AIMD-based control mechanism, in conjunction with the Kalman estimates, is shown to provide for more than 27% reduction in EC2 spot instance cost against methods based on reactive resource estimation. Finally, Dithen is shown to offer a 38% to 500% reduction of the billing cost against the current state-of-the-art in CaaS platforms on Amazon EC2 (Amazon Lambda and Amazon Autoscale). A baseline version of Dithen is currently available at http://www.dithen.com under the "AutoScale" option.

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Joseph Doyle, Vasileios Giotsas, Mohammad Ashraful Anam, Yiannis Andreopoulos. 2016-10-01. Dithen: A Computation-as-a-Service Cloud Platform For Large-Scale Multimedia Processing. https://doi.org/10.1109/tcc.2016.2617363

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