Enhanced heterogeneous cloud: transparent acceleration and elasticity
File(s)
Accepted version
Author(s)
Vandebon, Jessica
Coutinho, Jose GF
Luk, Wayne
Nurvitadhi, Eriko
Naik, Mishali
Type
Conference Paper
Abstract
This paper presents ORIAN, a fully-managed Platform-as-a-Service (PaaS) for deploying high-level applications onto large-scale heterogeneous cloud infrastructures. We aim to make specialised, accelerator resources in the cloud accessible to software developers by extending the traditional homogeneous PaaS execution model to support automatic runtime management of heterogeneous compute resources such as CPUs and FPGAs. In particular, we focus on two mechanisms: transparent acceleration, which automatically maps jobs to the most suitable resource configuration, and heterogeneous elasticity, which performs automatic vertical (type) and horizontal (quantity) scaling of provisioned resources to guarantee QoS (Quality of Service) objectives while minimising cost. We develop a prototype to validate our approach, targeting a hardware platform with combined computational capacity of 28 FPGAs and 36 CPU cores, and evaluate it using case studies in three application domains: machine learning, bioinformatics, and physics. Our transparent acceleration decisions achieve on average 96% of the maximum manually identified static configuration throughput for large workloads, while removing the burden of determining configuration from the user; an elastic ORIAN resource group provides a 2.3 times cost reduction compared to an over-provisioned group for non-uniform, peaked job sequences while guaranteeing QoS objectives; and our malleable architecture extends to support a new, more suitable resource type, automatically reducing the cost by half while maintaining throughput, and achieving a 23% throughput increase while fulfilling resource constraints.
Date Issued
2020-02-03
Date Acceptance
2019-12-01
Citation
2019 International Conference on Field-Programmable Technology (ICFPT), 2020
Publisher
IEEE
Journal / Book Title
2019 International Conference on Field-Programmable Technology (ICFPT)
Copyright Statement
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Identifier
https://ieeexplore.ieee.org/document/8977870
Source
2019 International Conference on Field-Programmable Technology (ICFPT)
Publication Status
Published
Start Date
2019-12-09
Finish Date
2019-12-13
Coverage Spatial
Tianjin, China
Date Publish Online
2020-02-03