Just last week [February 25th], the VMware Research team participated at two USENIX academic conferences, NSDI 20 & FAST 20, in the city of Santa Clara, California, U.S.
NSDI is the Symposium on Networked Systems Design and Implementation, focusing on the design principles, implementation, and practical evaluation of networked and distributed systems. Their goal is to bring together researchers from across the networking and systems community to foster a broad approach to addressing overlapping research challenges.
FAST is a conference on File and Storage Technologies and brings together storage-system researchers and practitioners to explore new directions in the design, implementation, evaluation, and deployment of storage systems. As part of their commitment to open access to research, the full Proceedings are free and open to the public on the technical sessions page.
Highlighted below are the two papers the VMware Research team submitted in the respective conferences.
NSDI 20 Conference
Title: Telekine: Secure Computing with Cloud GPUs
Authors: Tyler Hunt, Zhipeng Jia, Vance Miller, Ariel Szekely, and Yige Hu, The University of Texas at Austin; Christopher J. Rossbach, The University of Texas at Austin and VMware Research; Emmett Witchel, The University of Texas at Austin
Abstract: GPUs have become ubiquitous in the cloud due to the dramatic performance gains they enable in domains such as machine learning and computer vision. However, offloading GPU computation to the cloud requires placing enormous trust in providers and administrators. Recent proposals for GPU trusted execution environments (TEEs) are promising but fail to address very real side-channel concerns. To illustrate the severity of the problem, we demonstrate a novel attack that enables an attacker to correctly classify images from ImageNet by observing only the timing of GPU kernel execution, rather than the images themselves.
Telekine enables applications to use GPU acceleration in the cloud securely, based on a novel GPU stream abstraction that ensures execution and interaction through untrusted components are independent of any secret data. Given a GPU with support for a TEE, Telekine employs a novel variant of API remoting to partition application-level software into components to ensure secret-dependent behaviors occur only on trusted components. Telekine can securely train modern image recognition models on MXNet with 10%–22% performance penalty relative to an insecure baseline with a locally attached GPU. It runs graph algorithms using Galois on one and two GPUs with 18%–41% overhead. PDF PAPER
FAST 20 Conference
Title: How to Copy Files
Authors: Yang Zhan, The University of North Carolina at Chapel Hill and Huawei; Alexander Conway, Rutgers University; Yizheng Jiao and Nirjhar Mukherjee, The University of North Carolina at Chapel Hill; Ian Groombridge, Pace University; Michael A. Bender, Stony Brook University; Martin Farach-Colton, Rutgers University; William Jannen, Williams College; Rob Johnson, VMWare Research; Donald E. Porter, The University of North Carolina at Chapel Hill; Jun Yuan, Pace University
Abstract: Making logical copies, or clones, of files and directories is critical to many real-world applications and workflows, including backups, virtual machines, and containers. An ideal clone implementation meets the following performance goals: (1) creating the clone has low latency; (2) reads are fast in all versions (i.e., spatial locality is always maintained, even after modifications); (3) writes are fast in all versions; (4) the overall system is space efficient. Implementing a clone operation that realizes all four properties, which we call a nimble clone, is a long-standing open problem.
This paper describes nimble clones in BetrFS, an open-source, full-path-indexed, and write-optimized file system. The key observation behind our work is that standard copy-on-write heuristics can be too coarse to be space efficient, or too fine-grained to preserve locality. On the other hand, a write-optimized key-value store, as used in BetrFS or an LSM-tree, can decouple the logical application of updates from the granularity at which data is physically copied. In our write-optimized clone implementation, data sharing among clones is only broken when a clone has changed enough to warrant making a copy, a policy we call copy-on-abundant-write.
We demonstrate that the algorithmic work needed to batch and amortize the cost of BetrFS clone operations does not erode the performance advantages of baseline BetrFS; BetrFS performance even improves in a few cases. BetrFS cloning is efficient; for example, when using the clone operation for container creation, BetrFS outperforms a simple recursive copy by up to two orders-of-magnitude and outperforms file systems that have specialized LXC backends by 3-4×. PDF PAPER
Last year in 2019, VMware Researcher Radhika Niranjan Mysore received a Best Paper award at NSDI for her paper on “Understanding Lifecycle Management Complexity of Datacenter Topologies”.
Authors: Mingyang Zhang (USC), Radhika Niranjan Mysore (VMware Research), Sucha Supittayapornpong (USC), Ramesh Govindan (USC)
Abstract: Most recent datacenter topology designs have focused on performance properties such as latency and throughput. In this paper, we explore a new dimension, life cycle management, which attempts to capture operational costs of topologies. Specifically, we consider costs associated with deployment and expansion of topologies and explore how structural properties of two different topology families (Clos and expander graphs as exemplified by Xpander) affect these. We also develop a new topology that has the wiring simplicity of Clos and the expandability of expander graphs using the insights from our study. PDF PAPER
