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Enabling cost-aware and adaptive elasticity of multi-tier cloud applications
Affiliation:1. Department of Mathematics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea;2. Department of Mathematics Education, Chungbuk National University, 1 Chungdae-ro, Seowon-gu, Cheongju, Chungbuk, 28644, South Korea;1. LRI, Université Paris-Sud, Bat. 650, rue Noetzlin, 91405 Orsay, France;2. DEIB, Politecnico di Milano, via Ponzio 34/5, 20133 Milano, Italy;3. IUF, Institut Universitaire de France, France;4. Dipartimento di Ingegneria Gestionale, dell’Informazione e della Produzione, Università degli Studi di Bergamo, Via Marconi 5, 24044 Dalmine (BG), Italy;1. University Politehnica of Bucharest, Romania;2. UPMC Sorbonne Universités, Paris, France;1. Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, 60439, USA;2. Leadership Computing Facility Division, Argonne National Laboratory, Argonne, IL, 60439, USA
Abstract:Elasticity (on-demand scaling) of applications is one of the most important features of cloud computing. This elasticity is the ability to adaptively scale resources up and down in order to meet varying application demands. To date, most existing scaling techniques can maintain applications’ Quality of Service (QoS) but do not adequately address issues relating to minimizing the costs of using the service. In this paper, we propose an elastic scaling approach that makes use of cost-aware criteria to detect and analyse the bottlenecks within multi-tier cloud-based applications. We present an adaptive scaling algorithm that reduces the costs incurred by users of cloud infrastructure services, allowing them to scale their applications only at bottleneck tiers, and present the design of an intelligent platform that automates the scaling process. Our approach is generic for a wide class of multi-tier applications, and we demonstrate its effectiveness against other approaches by studying the behaviour of an example e-commerce application using a standard workload benchmark.
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