Building accurate models to determine the current CPU utilization of a host within a virtual machine allocated on it

Briongos Herrerjo, Samira and Malagón Marzo, Pedro José and Risco Martín, José Luis and Moya Fernández, José Manuel (2017). Building accurate models to determine the current CPU utilization of a host within a virtual machine allocated on it. In: "Summer Simulation Multi-Conference (SummerSim '17)", 09/07/2017 - 12/07/2017, Bellevue, Washington. pp. 1-12.

Description

Title: Building accurate models to determine the current CPU utilization of a host within a virtual machine allocated on it
Author/s:
  • Briongos Herrerjo, Samira
  • Malagón Marzo, Pedro José
  • Risco Martín, José Luis
  • Moya Fernández, José Manuel
Item Type: Presentation at Congress or Conference (Article)
Event Title: Summer Simulation Multi-Conference (SummerSim '17)
Event Dates: 09/07/2017 - 12/07/2017
Event Location: Bellevue, Washington
Title of Book: Summer Simulation Multi-Conference (SummerSim '17)
Date: 2017
Subjects:
Freetext Keywords: Cloud Computing, side-channel, Genetic-algorithm, LASSO
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In cloud computing environments there are several virtual machines running in the same host. This fact opens the door for possible side channel-attacks. Prior to perform an attack it is mandatory to determine co- residency with the victim. An synchronized variation in the CPU activity in the host is a possible indicator of the presence of neighboring processes. However, cloud providers do not give information about the hosts CPU load, so we have to figure out a way of estimating it. We estimate the host CPU load considering its impact on the performance of a virtual machine (VM) running on it. In this work, we show that it is possible to calculate the CPU load of the host by executing a reference process, measuring the time it takes to execute, and using this information as an input to generate the CPU load models. We explore regression methods and regression methods tuned with genetic algorithms for the model generation. As a result, considering a CPU load value between 0 (no load) and 100 (maximum load), we obtain models which compute the host load with a mean squared error of around 5%, 10% and 30% (depending on the host architecture) when estimating the load every second.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainAYA2015-65973-C3-3-RUnspecifiedUnspecifiedGas en el interior y en el entorno de las galaxias. Preparación científica para SKA y contribución al diseño del flujo de datos - Procesado de datos en hardware
Government of SpainRTC-2014-2717-3UnspecifiedUnspecifiedOptimización energética de centros de datos de infraestructuras Cloud basadas en OpenStack
Government of SpainTIN-2015-65277-RUnspecifiedUnspecifiedUnspecified
Government of SpainRTC-2016-5434-8HIDRAUnspecifiedHolistic Intrusion Detection and Response Agent

More information

Item ID: 51043
DC Identifier: http://oa.upm.es/51043/
OAI Identifier: oai:oa.upm.es:51043
Official URL: https://dl.acm.org/citation.cfm?id=3140065.3140098
Deposited by: Memoria Investigacion
Deposited on: 01 Oct 2018 14:50
Last Modified: 01 Oct 2018 14:50
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