HFlow: A Dynamic and Elastic Multi-Layered I/O Forwarder

• Main Authors: Cernuda, Jaime, Devarajan, Hariharan, Logan, Luke, Bateman, Keith, Rajesh, Neeraj, Ye, Jie, Kougkas, Anthony, Sun, Xian-He
• Format: Conference Proceeding
• Language: English
• Subjects: Cluster computing; Conferences; data pipeline; Data streaming; data-intensive; Dynamic scheduling; dynamicity; elasticity; I/O; I/O forwarding; in-transit; multi-tenant; Real-time systems; Software; Task analysis
• ISSN: 2168-9253
• DOI: 10.1109/Cluster48925.2021.00064

Modern applications are highly data-intensive, leading to the well-known I/O bottleneck problem. Scientists have proposed the placement of fast intermediate storage resources which aim to mask the I/O penalties. To manage these resources, three core software abstractions are being used in leadership-class computing facilities: IO Forwarders, Burst Buffers, and Data Stagers. Yet, with the rise of multi-tenant deployment in HPC systems, these software abstractions are: managed and maintained in isolation, leading to inefficient interactions; allocated statically, leading to load imbalance; exclusively bifurcated between the intermediate storage, leading to under-utilization of resources, and, in many cases, do not support in-situ operations. To this end, we present HFlow, a new class of data forwarding system that leverages a real-time data movement paradigm. HFlow introduces a unified data movement abstraction (the ByteFlow) providing data-independent tasks that can be executed anywhere and thus, enabling dynamic resource provisioning. Moreover, the processing elements executing the ByteFlows are designed to be ephemeral and, hence, enable elastic management of intermediate storage resources. Our results show that applications running under HFlow display an increase in performance of 3x when compared with state-of-the-art software solutions.