Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

Huda Hallawi, Jörn Mehnen, Hongmei He

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.
Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalFuture Generation Computer Systems
Volume69
Early online date2 Nov 2016
DOIs
Publication statusPublished - 1 Apr 2017
Externally publishedYes

Fingerprint

Consolidation
Resource allocation
Genetic algorithms
Bins
Servers
Virtual machine

Keywords

  • cloud resources allocation
  • cloud resources provisioning
  • virtual machines consolidation
  • vector bin packing
  • genetic algorithm

Cite this

@article{7ba3e28a89b346b69954db4e3f83377b,
title = "Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation",
abstract = "This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4{\%}, 34{\%}, and 39{\%}, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.",
keywords = "cloud resources allocation, cloud resources provisioning, virtual machines consolidation, vector bin packing, genetic algorithm",
author = "Huda Hallawi and J{\"o}rn Mehnen and Hongmei He",
year = "2017",
month = "4",
day = "1",
doi = "10.1016/j.future.2016.10.025",
language = "English",
volume = "69",
pages = "1--10",
journal = "Future Generation Computer Systems",
issn = "0167-739X",

}

TY - JOUR

T1 - Multi-capacity combinatorial ordering GA in application to cloud resources allocation and efficient virtual machines consolidation

AU - Hallawi, Huda

AU - Mehnen, Jörn

AU - He, Hongmei

PY - 2017/4/1

Y1 - 2017/4/1

N2 - This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.

AB - This paper describes a novel approach making use of genetic algorithms to find optimal solutions for multi-dimensional vector bin packing problems with the goal to improve cloud resource allocation and Virtual Machines (VMs) consolidation. Two algorithms, namely Combinatorial Ordering First-Fit Genetic Algorithm (COFFGA) and Combinatorial Ordering Next Fit Genetic Algorithm (CONFGA) have been developed for that and combined. The proposed hybrid algorithm targets to minimise the total number of running servers and resources wastage per server. The solutions obtained by the new algorithms are compared with latest solutions from literature. The results show that the proposed algorithm COFFGA outperforms other previous multi-dimension vector bin packing heuristics such as Permutation Pack (PP), First Fit (FF) and First Fit Decreasing (FFD) by 4%, 34%, and 39%, respectively. It also achieved better performance than the existing genetic algorithm for multi-capacity resources virtual machine consolidation (RGGA) in terms of performance and robustness. A thorough explanation for the improved performance of the newly proposed algorithm is given.

KW - cloud resources allocation

KW - cloud resources provisioning

KW - virtual machines consolidation

KW - vector bin packing

KW - genetic algorithm

UR - http://www.sciencedirect.com/science/article/pii/S0167739X16304630

U2 - 10.1016/j.future.2016.10.025

DO - 10.1016/j.future.2016.10.025

M3 - Article

VL - 69

SP - 1

EP - 10

JO - Future Generation Computer Systems

JF - Future Generation Computer Systems

SN - 0167-739X

ER -