Using SPITS to Optimize the Cost of High-Performance Geophysics Processing on the Cloud
Nicholas T. Okita, Tiago A. Coimbra, Charles Rodamilans, Martin Tygel, Edson Borin · Proceedings · 2018
Summary Public cloud providers, such as the Microsoft Azure and the Amazon Web Services (AWS), offer a wide variety of virtual machines, with different specifications, and prices, that are enabling users to run high-performance programs without buying specialized and expensive hardware. Moreover, some providers, such as AWS, allow the user to bid for lower cost virtual machines, called Spot Instances. Nonetheless, these machines may be terminated within a few minutes of warning at the provider discretion. In this work, we leveraged the SPITS programming model to implement a high-performance and fault-tolerant seismic processing application that is proper for execution on Spot instances and analyzed how different virtual machines from the AWS may affect the performance and the price of the computation. Our experimental results indicate that Spot instances have similar performance to regular instances but are roughly three times less expensive. Finally, we show that AWS groups virtual machines in Availability Zones and that selecting virtual machines from different zones may also affect the total execution cost.