Yiying Zhang


Datacenter Networking

Advance in datacenter networking in the past decade has driven a sea change in the way datacenters are organized and managed. We are exploring various datacenter networking issues from the systems perspective. Currently, we are interested in low-latency, RDMA-based network systems.


Network for Resource Disaggregation

The need to access remote resources and to access them fast demands a new network system for future disaggregated datacenters. We are building LegoNET, a new network system designed for adding disaggregated resources to existing datacenters in a non-disruptive way, while delivering low-latency, high-throughput performance and a flexible, easy-to-use interface.


RDMA Side-Channel Attack

RDMA is a technology that allows direct access from the network to a machine’s main memory without involving its CPU. While RDMA provides massive performance boosts and has thus been adopted by several major cloud providers, security concerns have so far been neglected.

The need for RDMA NICs to bypass CPU and directly access memory result in them storing various metadata like page table entries in their on-board SRAM. When the SRAM is full, RNICs swap metadata to main memory across the PCIe bus. We exploited the resulting timing difference to establish side channels and demonstrated that these side channels can leak access patterns of victim nodes to other nodes.

Pythia is a set of RDMA-based remote sidechannel attacks that allow an attacker on one machine to learn how victims on other machines access the server’s exported in-memory data. We reverse engineered the memory architecture of the most widely used RDMA NIC and use this knowledge to improve the efficiency of Pythia. We further extended Pythia to build side-channel attacks on Crail, a real RDMA-based key-value store application. Pythia is fast (57μs), accurate (97% accuracy), and can hide all its traces from the victim or the server.


Datacenter Approximate Tranmission Protocol

Many datacenter applications such as machine learning and streaming systems do not need the complete set of data to perform their computation. Current approximate applications in datacenters run on a reliable network layer like TCP and either sample data before sending or drop data after receiving to improve performance. These approaches are network oblivious and transmit (and retransmit) more data than necessary, affecting both application runtime and network bandwidth usage.

We propose to run approximate applications on a lossy network and to allow packet loss in a controlled manner. We designed a new network protocol called Approximate Transmission Protocol, or ATP, for datacenter approximate applications. ATP opportunistically exploits available network bandwidth as much as possible, while performing a loss-based rate control algorithm to avoid bandwidth waste and retransmission. It also ensures bandwidth fair sharing across flows and improves accurate applications’ performance by leaving more switch buffer space to accurate flows.


Kernel-Level Indirection Layer for RDMA

Recently, there is an increasing interest in building datacenter applications with RDMA because of its low-latency, high-throughput, and low-CPU-utilization benefits. However, RDMAis not readily suitable for datacenter applications. It lacks a flexible, high-level abstraction; its performance does not scale; and it does not provide resource sharing or flexible protection. Because of these issues, it is difficult to build RDMA-based applications and to exploit RDMA’s performance benefits.

To solve these issues, we built LITE, a Local Indirection TiEr for RDMA in the Linux kernel that virtualizes native RDMA into a flexible, high-level, easy-to-use abstraction and allows applications to safely share resources. Despite the widely-held belief that kernel bypassing is essential to RDMA’s low-latency performance, we show that using a kernel-level indirection can achieve both flexibility and lowlatency, scalable performance at the same time.


Related Publications

Pythia: Remote Oracles for the Masses
Shin-Yeh Tsai, Mathias Payer, Yiying Zhang
under submission

A Double-Edged Sword: Security Threats and Opportunities in One-Sided Network Communication
Shin-Yeh Tsai, Yiying Zhang
To appear at the 11th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud '19)

ATP: a Datacenter Approximate Transmission Protocol
Ke Liu, Shin-Yeh Tsai, Yiying Zhang
arxiv preprint

MemAlbum: an Object-Based Remote Software Transactional Memory System
Shin-Yeh Tsai, Yiying Zhang
the 2018 Workshop on Warehouse-scale Memory Systems (WAMS '18) (co-located with ASPLOS '18)

LITE Kernel RDMA Support for Datacenter Applications
Shin-Yeh Tsai, Yiying Zhang
Proceedings of the 26th ACM Symposium on Operating Systems Principles (SOSP '17)

Rockies: A Network System for Future Data Center Racks
Shin-Yeh Tsai, Linzhe Li, Yiying Zhang
WIP and Poster at the 14th USENIX Conference on File and Storage Technologies (FAST '16)