Doyle, J;
Giotsas, V;
Anam, MA;
Andreopoulos, I;
(2016)
Cloud Instance Management and Resource Prediction For Computation-as-a-Service Platforms.
In:
Proceedings of the IEEE International Conference on Cloud Engineering (IC2E) 2016.
IEEE: Berlin, Germany.
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Abstract
Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of individual processing tasks to available cloud instances (compute units) according to availability and predetermined time-to-completion (TTC) constraints; (ii) accurate resource prediction; (iii) efficient control of the number of cloud instances servicing workloads, in order to optimize between completing workloads in a timely fashion and reducing resource utilization costs. In this paper, we propose three approaches that satisfy these properties (respectively): (i) a service rate allocation mechanism based on proportional fairness and TTC constraints; (ii) Kalman-filter estimates for resource prediction; and (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 compute units servicing workloads. The integration of our three proposals into a single CaaS platform is shown to provide for more than 27% reduction in Amazon EC2 spot instance cost against methods based on reactive resource prediction and 38% to 60% reduction of the billing cost against the current state-of-the-art in CaaS platforms (Amazon Lambda and Autoscale).
| Type: | Proceedings paper |
|---|---|
| Title: | Cloud Instance Management and Resource Prediction For Computation-as-a-Service Platforms |
| Event: | IEEE International Conference on Cloud Engineering (IC2E) 2016 |
| Location: | Berlin |
| Dates: | 04 April 2016 - 08 April 2016 |
| ISBN-13: | 978-1-5090-1961-8 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/IC2E.2016.36 |
| Publisher version: | http://dx.doi.org/10.1109/IC2E.2016.36 |
| Language: | English |
| Additional information: | Copyright © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| Keywords: | computation-as-a-service, big data, multimedia computing, Amazon EC2, spot instances |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Electronic and Electrical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1478277 |
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