%0 Conference Proceedings
%T A Genetic Algorithm for Power-Aware Virtual Machine Allocation in Private Cloud
%+ Ho Chi Minh City University of Technology [Vietnam National University, HCM] (HCMUT)
%A Quang-Hung, Nguyen
%A Nien, Pham, Dac
%A Nam, Nguyen, Hoai
%A Huynh Tuong, Nguyen
%A Thoai, Nam
%Z Part 1: Information and Communication Technology- Eurasia Conference (ICT-EurAsia)
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 1st International Conference on Information and Communication Technology (ICT-EurAsia)
%C Yogyakarta, Indonesia
%Y David Hutchison
%Y Takeo Kanade
%Y Madhu Sudan
%Y Demetri Terzopoulos
%Y Doug Tygar
%Y Moshe Y. Vardi
%Y Gerhard Weikum
%Y Khabib Mustofa
%Y Erich J. Neuhold
%Y A Min Tjoa
%Y Edgar Weippl
%Y Ilsun You
%Y Josef Kittler
%Y Jon M. Kleinberg
%Y Friedemann Mattern
%Y John C. Mitchell
%Y Moni Naor
%Y Oscar Nierstrasz
%Y C. Pandu Rangan
%Y Bernhard Steffen
%I Springer
%3 Information and Communicatiaon Technology
%V LNCS-7804
%P 183-191
%8 2013-03-25
%D 2013
%R 10.1007/978-3-642-36818-9_19
%Z Computer Science [cs]Conference papers
%X Energy efficiency has become an important measurement of scheduling algorithm for private cloud. The challenge is trade-off between minimizing of energy consumption and satisfying Quality of Service (QoS) (e.g. performance or resource availability on time for reservation request). We consider resource needs in context of a private cloud system to provide resources for applications in teaching and researching. In which users request computing resources for laboratory classes at start times and non-interrupted duration in some hours in prior. Many previous works are based on migrating techniques to move online virtual machines (VMs) from low utilization hosts and turn these hosts off to reduce energy consumption. However, the techniques for migration of VMs could not use in our case. In this paper, a genetic algorithm for power-aware in scheduling of resource allocation (GAPA) has been proposed to solve the static virtual machine allocation problem (SVMAP). Due to limited resources (i.e. memory) for executing simulation, we created a workload that contains a sample of one-day timetable of lab hours in our university. We evaluate the GAPA and a baseline scheduling algorithm (BFD), which sorts list of virtual machines in start time (i.e. earliest start time first) and using best-fit decreasing (i.e. least increased power consumption) algorithm, for solving the same SVMAP. As a result, the GAPA algorithm obtains total energy consumption is lower than the baseline algorithm on simulated experimentation.
%G English
%Z TC 5
%Z TC 8
%2 https://inria.hal.science/hal-01480228/document
%2 https://inria.hal.science/hal-01480228/file/978-3-642-36818-9_19_Chapter.pdf
%L hal-01480228
%U https://inria.hal.science/hal-01480228
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-TC8
%~ IFIP-ICT-EURASIA
%~ IFIP-LNCS-7804