SNIA Developer Conference September 15-17, 2025 | Santa Clara, CA
Vasily Tarasov
Principal Research Scientist
IBM
Vasily Tarasov is a Principal Research Scientist at IBM. His research and engineering interests revolve around data storage, focusing on high-performance storage systems for Hybrid Clouds and AI. At IBM Vasily led research projects on making IBM Spectrum Scale (GPFS) container-native, building a storage acceleration tier for AI workloads, and creating high-performance object store for data lakehouse. Most recently Vasily works on building and operating high-performance storage solutions for IBM's Vela supercomputer for AI. In the past, Vasily studied data deduplication, storage performance and workload analysis, user-space file systems, and storage for containers. Vasily holds Ph.D. degree in Computer Science from Stony Brook University, published dozens of papers in systems conferences, and regularly serves on program committees of top-tier storage conferences.
Related Sessions
Storage for AI in Public Clouds: Case Study of Vela in IBM Cloud
Over the last few years IBM has built several AI-HPC clusters to experiment with and train its Large Language Models (LLMs). One of the clusters – Cloud Vela – is especially notable because it explored less conventional approaches for building HPC clusters. Vela relies on public cloud infrastructure, runs in VMs, uses Ethernet, and relies on container orchestrator (Kubernetes) to manage workloads and resources. Training jobs submitted by data scientists produce two types of I/O traffic: 1) reading training data and 2) writing large periodic checkpoints. To satisfy the I/O requirements of Vela we architected a tiered storage system that runs a distributed file system (GPFS) on top of cloud object storage.
In my presentation I will start with an overview of Vela’s design, how data scientists interact with it, and what workloads they run. I will then dive in the details of the storage system architecture and implementation for Vela. I will describe the performance and semantical challenges of the native cloud storage options and why we resorted to using a distributed file system over object storage. I will cover the design of our data mover and the solution integration w/ Kubernetes that makes it easy for the end user to provision file system volumes backed by object buckets without administrator intervention. I will also talk about our challenges related to observability and cache sizing.