Intelligent, real-time migration of scientific computing applications on commercial cloud-based HPC platforms

Despite intending to reduce computing workload costs by migrating IT infrastructure and operations from physical servers to the cloud, businesses find that leasing the same amount of server space in the cloud can prove even more costly, and cloud cost management is a pain point. This is partly because businesses must overprovision to accommodate potential surges in server use and ensure that stateful applications, which cannot tolerate any downtime, are not compromised. Furthermore, the infiltration of graphic processing units (GPUs) into the cloud market has grown due to their capabilities as computational accelerators, subsequently increasing their demand within cloud storage. However, many cloud platforms are limited in their ability to support GPUs, and still charge considerable amounts of money for their storage and use. Exotanium is planning to develop novel technologies (X-Spot and X-Consolidate) that can consolidate idle workloads and over-sized software containers to take advantage of deeply discounted server space such as the Spot market. Additionally, this technology will develop the X-Spot platform to support GPUs, processors designed to render images and provide fast graphics processing. This support would enable large, highly compute-intensive applications to reliably run uninterrupted while taking advantage of the spot market's significant cost savings. Exotanium will address this through the following three objectives: 1) Demonstration of Exotanium supporting DOE workload in the public cloud and GovCloud of AWS, 2) Provide GPU Migration Support, and 3) Establishment of Hybrid Cloud Support. Exotanium can support DOE workloads in the public cloud at an unprecedented level of price, performance, and ease of use. Exotanium will support DOE HPC applications without requiring them to be changed at all. Successfully establishing Exotanium’s X-Spot platform to support GPUs could result in a potentially large number of highly compute-intensive DOE applications being run in the spot market of the AWS and Azure GovClouds at significant cost savings. Finally, demonstrating a successful hybrid cloud transparent live migration of workloads between on-premises private cloud to the public cloud could lead to significant cost savings without changing a line of code for the application, presenting a potential approach for modernization, migrating to the cloud.