Computing

, Volume 98, Issue 5, pp 495–522 | Cite as

Multi-objective Swarm Intelligence schedulers for online scientific Clouds

  • Elina Pacini
  • Cristian Mateos
  • Carlos García Garino
Article
First Online:
Received:
Accepted:

Abstract

Cloud Computing is a promising paradigm for parallel computing. However, as Cloud-based services become more dynamic, resource provisioning in Clouds becomes more challenging. The paradigm, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. In a Cloud, an appropriate number of Virtual Machines (VM) is created and allocated in physical resources for executing jobs. This work focuses on the Infrastructure as a Service (IaaS) model where custom VMs are launched in appropriate hosts available in a Cloud to execute scientific experiments coming from multiple users. Finding optimal solutions to allocate VMs to physical resources is an NP-complete problem, and therefore many heuristics have been developed. In this work, we describe and evaluate two Cloud schedulers based on Swarm Intelligence (SI) techniques, particularly Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). The main performance metrics to study are the number of serviced users by the Cloud and the total number of created VMs in online (non-batch) scheduling scenarios. We also perform a sensitivity analysis by varying the specific-parameter values of each algorithm to evaluate the impact on the performance of the two objective metrics. The intra-Cloud network traffic is also measured. Simulated experiments performed using CloudSim and job data from real scientific problems show that the use of SI-based techniques succeeds in balancing the studied metrics compared to Genetic Algorithms.

Keywords

Cloud Computing VM Scheduling Ant Colony Optimization  Particle Swarm Optimization Genetic Algorithms 

Mathematics Subject Classification

68M14 68M20 68T20 
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Notes

Acknowledgments

We thank the anonymous reviewer for his/her constructive comments to improve the paper. We also thank Dr. David Monge for their insightful comments on scientific workflows. We acknowledge the financial support provided by ANPCyT through grants PAE-PICT 2007-02311, PAE-PICT 2007-02312, PICT-2012-0045, and UNCuyo University project 06/B253. The first author acknowledges her Ph.D. fellowships granted by the PRH-UNCuyo Project and the National Scientific and Technological Research Council (CONICET).

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Copyright information

© Springer-Verlag Wien 2014

Authors and Affiliations

  • Elina Pacini
    • 1
  • Cristian Mateos
    • 2
  • Carlos García Garino
    • 3
  1. 1.ITIC-UNCuyo UniversityMendozaArgentina
  2. 2.ISISTAN Research Institute-UNICEN UniversityTandilArgentina
  3. 3.ITIC and Facultad de Ingeniería-UNCuyo UniversityMendozaArgentina

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