A Multi-Cloud Approach to Cost Optimization with AWS and Azure Fleet Services

  • Lucas Silveira Serrano UFF
  • Miguel de Lima UFF
  • Felipe A. Portella PETROBRAS
  • Lúcia M. A. Drummond PETROBRAS

Resumo


High Performance Computing (HPC) is increasingly moving to the cloud, where Spot instances offer significant cost savings compared to On-demand price models. However, selecting instances remains challenging due to a large variety of instance types and costs across different regions and providers. This work investigates strategies for allocating a set of Spot instances (a Spot fleet), simultaneously, in commercial clouds, focusing exclusively on raw pricing. AWS and Azure are first analyzed independently to highlight their particularities in pricing models and instance offerings. Building on this analysis, a multi-cloud solution (MC-Fleet) is proposed to support cost-efficient selection of a set of instances across providers. Extensive experiments demonstrate MC-Fleet effectiveness, achieving cost savings of 23% against a fully provisioned AWS single region us-east-1 and up to 47% over an incomplete provisioned sa-east-1.
Palavras-chave: Cloud computing, Costs, High performance computing, Conferences, Computational modeling, Buildings, Focusing, Pricing, Computer architecture, Optimization, Cloud Computing, HPC applications, Spot Instances, AWS EC2 Fleet, Azure Compute Fleet
Publicado
28/10/2025
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SERRANO, Lucas Silveira; LIMA, Miguel de; PORTELLA, Felipe A.; DRUMMOND, Lúcia M. A.. A Multi-Cloud Approach to Cost Optimization with AWS and Azure Fleet Services. In: WORKSHOP ON CLOUD COMPUTING (WCC) - INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE AND HIGH PERFORMANCE COMPUTING (SBAC-PAD), 37. , 2025, Bonito/MS. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2025 . p. 61-68.