Social Auto-Scaling

Smith, Peter, González-Vélez, Horacio and Caton, Simon (2018) Social Auto-Scaling. In: 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP) 2018. IEEE, pp. 186-195. ISBN 9781538649756

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Abstract

Today, auto-scaling solutions are still largely reactive and are based on the load measured on existing nodes nearing a threshold or traffic forecast information provided in advance of a scheduled event. Despite these advancements, events which cause a flash flood of web traffic do not always benefit from this approach to auto-scaling because the latency of provisioning new nodes is not sufficient to prevent resource saturation. This paper proposes a novel approach to preemptive compute scaling based on the buzz of specific social network hashtags. We argue that improved scaling latency and service availability can be achieved for web services that receive intermittent as well as unexpected traffic load patterns. By combining social network monitoring, with auto-scaling frameworks, this approach can reduce the operational expense impact of over- and under-provisioning as well as the business costs of the latter. We demonstrate our approach using the AWS application suite, and demonstrate how our approach expedites the auto-scaling strategy for our use case: public transportation web sites.

Item Type:	Book Section
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software T Technology > T Technology (General) > Information Technology > Computer software T Technology > T Technology (General) > Information Technology > Cloud computing
Divisions:	School of Computing > Staff Research and Publications
Depositing User:	Caoimhe Ní Mhaicín
Date Deposited:	18 Jun 2018 10:36
Last Modified:	18 Jun 2018 10:36
URI:	https://norma.ncirl.ie/id/eprint/3019

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