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Journal of Network and Systems Management

, Volume 27, Issue 3, pp 688–729 | Cite as

A Bio-inspired Datacenter Selection Scheduler for Federated Clouds and Its Application to Frost Prediction

  • Elina PaciniEmail author
  • Lucas Iacono
  • Cristian Mateos
  • Carlos García Garino
Article
  • 151 Downloads

Abstract

Frost is an agro-meteorological event which causes both damage in crops and important economic losses, therefore frost prediction applications (FPA) are very important to help farmers to mitigate possible damages. FPA involves the execution of many CPU-intensive jobs. This work focuses on efficiently running FPAs in paid federated Clouds, where custom virtual machines (VM) are launched in appropriate resources belonging to different providers. The goal of this work is to minimize both the makespan and monetary cost. We follow a federated Cloud model where scheduling is performed at three levels. First, at the broker level, a datacenter is selected taking into account certain criteria established by the user, such as lower costs or lower latencies. Second, at the infrastructure level, a specialized scheduler is responsible for mapping VMs to datacenter hosts. Finally, at the VM level, jobs are assigned for execution into the preallocated VMs. Our proposal mainly contributes to implementing bio-inspired strategies at two levels. Specifically, two broker-level schedulers based on Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO), which aim to select the datacenters taking into account the network latencies, monetary cost and the availability of computational resources in datacenters, are implemented. Then, VMs are allocated in the physical machines of that datacenter by another intra-datacenter scheduler also based on ACO and PSO. Performed experiments show that our bio-inspired scheduler succeed in reducing both the makespan and the monetary cost with average gains of around 50% compared to genetic algorithms.

Keywords

Scientific computing Frost prediction applications Cloud computing Scheduling Ant colony optimization Particle Swarm optimization Genetic algorithms 

Notes

Acknowledgements

We acknowledge the financial support provided by ANPCyT through grants PICT-2012-2731, PICT-2014-1430 and PICT-2015-1435, and UNCuyo project project 06/B308. We want to thank the anonymous reviewers for their valuable comments and suggestions that helped to improve the quality of this paper.

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Authors and Affiliations

  1. 1.ITIC and Facultad de IngenieríaUNCuyo UniversityMendozaArgentina
  2. 2.CONICETBuenos AiresArgentina
  3. 3.ISISTAN-CONICETUNICEN UniversityTandilArgentina

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