iSeSA: Towards Migrating HPC and AIWorkloads to Serverless Platform

Abstract

High Performance Computing applications typically involve compute intensive simulations which inherently may process large amounts of data at high speeds. The high compute requirements of these applications often exceed the on-premise cluster capacity to analyze the research data. Furthermore, capacity planning becomes very challenging for dynamically varying workloads due to their frequently changing resource demands. These challenges can be mitigated using highly scalable and cost-effective cloud services such as serverless platforms. In this work, we discuss a solution named intelligent Serverless Scalable Architecture framework (iSeSA) that can burst computation to the serverless platform on the public cloud when on-premise compute peak capacity has been reached by providing compute scalability and flexibility. We propose a hybrid cloud model that enables the use of a serverless platform on the public cloud in an automated, intelligent, and cost effective way by applying Machine Learning (ML) and Deep Learning techniques. The iSeSA framework takes a decision to execute the workload on-premise or on serverless platforms. It also manages surge requirements and achieves Service Level Agreement (SLA).