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

30 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

Keywords

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

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