Spotting the Right Cloud Instances with Multiple AWS EC2 Fleets - Sodré, Daniel M.B.; Serrano, Lucas; Lima, Miguel de; Boeres, Cristina; Drummond, Lúcia M. A.; Rebello, Vinod E. F. (Anais do International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD); 2025: Anais do XXXVII International Symposium on Computer Architecture and High Performance Computing, 2025)
High Performance Computing (HPC) is increasingly transitioning to the cloud, although cost remains a significant barrier. While On-demand instances and Committed Use Discounts provide predictable pricing, the Spot market offers an appealing opportunity for substantial cost savings – though it does not guarantee resource availability. Effectively managing multiple instances for parallel HPC applications is essential. Services like AWS EC2 Fleet or Spot Fleet help with this, but they come with limitations, notably being constrained to a single region. Furthermore, simply selecting the lowest-priced instance often leads to suboptimal performance and, surprisingly, higher overall costs. To truly economize, a more sophisticated approach is required: one that involves profiling applications and instances to understand their intricate cost-performance trade-offs. The most cost-effective execution prioritizes instances that strike a better balance between the price per hour being charged and the actual performance they offer the application, even if its current Spot price is not the lowest available. This paper addresses these challenges by conducting a thorough analysis of existing EC2 (Spot) Fleet policies and introducing Fleet-MR, a novel multi-region instance selection framework. Fleet-MR aims to improve execution times or reduce costs, and its effectiveness is validated through experimental evaluations.