Prediction-Based VM Provisioning and Admission Control for Multi-Tier Web Applications

A1 Journal article (refereed)


Internal Authors/Editors


Publication Details

List of Authors: Adnan Ashraf, Benjamin Byholm, Ivan Porres
Publisher: SpringerOpen
Publication year: 2016
Journal: Journal of Cloud Computing
Volume number: 5
Issue number: 15
Start page: 1
End page: 21
eISSN: 2192-113X


Abstract

We present a prediction-based, cost-efficient Virtual Machine (VM) provisioning and admission control approach for multi-tier web applications. The proposed approach provides automatic deployment and scaling of multiple web applications on a given Infrastructure as a Service (IaaS) cloud. It monitors and uses collected resource utilization metrics itself and does not require a performance model of the applications or the infrastructure dynamics. The approach uses the OSGi component model to share VM resources among deployed applications, reducing the total number of required VMs. The proposed approach comprises three sub-approaches: a reactive VM provisioning approach called ARVUE, a hybrid reactive-proactive VM provisioning approach called Cost-efficient Resource Allocation for Multiple web applications with Proactive scaling (CRAMP), and a session-based adaptive admission control approach called adaptive Admission Control for Virtualized Application Servers (ACVAS). Performance under varying load conditions is guaranteed by automatic adjustment and tuning of the CRAMP and ACVAS parameters. The proposed approach is demonstrated in discrete-event simulations and is evaluated in a series of experiments involving synthetic as well as realistic load patterns.


Keywords

admission control, Cloud computing, Cost-efficiency, Performance, Web application, Virtual machine provisioning

Last updated on 2019-07-12 at 02:42