Dithen: A Computation-as-a-Service Cloud Platform For Large-Scale Multimedia Processing
Joseph Doyle, Vasileios Giotsas, Mohammad Ashraful Anam, Yiannis, Andreopoulos

TL;DR
Dithen is a cloud platform designed for large-scale multimedia processing that optimizes resource allocation and reduces costs through reactive assignment, Kalman filtering, and AIMD algorithms, outperforming existing CaaS solutions.
Contribution
This paper introduces Dithen, a novel CaaS platform that combines reactive task assignment, Kalman-based resource estimation, and AIMD control to significantly lower multimedia processing costs.
Findings
Processed over 80,000 multimedia tasks for less than $1
Achieved 27% cost reduction with AIMD and Kalman filtering
Reduced billing costs by 38% to 500% compared to existing platforms
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
