Title: Optimal cloud resource provisioning for auto-scaling enterprise applications

Authors: Satish Narayana Srirama; Alireza Ostovar

Addresses: Mobile & Cloud Lab, Institute of Computer Science, University of Tartu, Ulikooli 17 – 324, Tartu, Estonia ' Science and Engineering faculty, Information Systems School, Queensland University of Technology, 2 George St, Brisbane, QLD, Australia

Abstract: Auto-scaling enterprise/workflow systems on cloud needs to deal with both the scaling policy, which determines 'when to scale' and the resource provisioning policy, which determines 'how to scale'. This paper presents a novel resource provisioning policy that can find the most cost optimal setup of variety of instances of cloud that can fulfill incoming workload. All major factors involved in resource amount estimation such as processing power, periodic cost and configuration cost of each instance type, lifetime of each running instance and capacity of clouds are considered in the model. Benchmark experiments were conducted on Amazon cloud and were matched with Amazon AutoScale, using a real load trace and through two main control flow components of enterprise applications, AND and XOR. The experiments showed that the model is plausible for auto-scaling any web/services based enterprise workflow/application on the cloud, along with the effect of individual parameters on the optimal policy.

Keywords: cloud computing; auto-scaling; enterprise applications; resource provisioning; optimisation; control flows.

DOI: 10.1504/IJCC.2018.093769

International Journal of Cloud Computing, 2018 Vol.7 No.2, pp.129 - 162

Accepted: 12 Mar 2018
Published online: 19 Jul 2018 *

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