Four papers with Michigan authors at SIGCOMM 2021

ACM SIGCOMM's annual conference is the leading conference in data communications and networking in the world.
Data center rack

Four papers co-authored by CSE faculty at the University of Michigan were accepted to appear at ACM’s Special Interest Group on Data Communications 2021 conference, the leading conference in data communications and networking in the world. The nine U-M researchers proposed a new way to order messages in a data center for better data consistency, performed a performance and power measurement study of commercial 5G networks, designed a way to transmit Bluetooth packets over commercial WiFi, and implemented an easier way to handle packet scheduling.

Learn more about the projects:

1Pipe: scalable total order communication in data center networks

Bojie Li (Huawei Technologies); Gefei Zuo (University of Michigan); Wei Bai (Microsoft Research); Lintao Zhang (BaseBit Technologies)

Abstract: This paper proposes 1Pipe, a novel communication abstraction that enables different receivers to process messages from senders in a consistent total order. More precisely, 1Pipe provides both unicast and scattering (i.e., a group of messages to different destinations) in a causally and totally ordered manner. 1Pipe provides a best effort service that delivers each message at most once, as well as a reliable service that guarantees delivery and provides restricted atomic delivery for each scattering. 1Pipe can simplify and accelerate many distributed applications, e.g., transactional key-value stores, log replication, and distributed data structures.

We propose a scalable and efficient method to implement 1Pipe inside data centers. To achieve total order delivery in a scalable manner, 1Pipe separates the bookkeeping of order information from message forwarding, and distributes the work to each switch and host. 1Pipe aggregates order information using in-network computation at switches. This forms the “control plane” of the system. On the “data plane”, 1Pipe forwards messages in the network as usual and reorders them at the receiver based on the order information.

Evaluation on a 32-server testbed shows that 1Pipe achieves scalable throughput (80M messages per second per host) and low latency (10𝜇s) with little CPU and network overhead. 1Pipe achieves linearly scalable throughput and low latency in transactional key-value store, TPC-C, remote data structures, and replication that outperforms traditional designs by 2∼20x.

A variegated look at 5G in the wild: performance, power, and QoE implications

Arvind Narayanan (University of Minnesota – Twin Cities); Xumiao Zhang, Ruiyang Zhu (University of Michigan); Ahmad Hassan (University of Minnesota – Twin Cities); Shuowei Jin, Xiao Zhu (University of Michigan); Xiaoxuan Zhang, Denis Rybkin, Zhengxuan Yang (University of Minnesota – Twin Cities); Z. Morley Mao (University of Michigan); Feng Qian, Zhi-Li Zhang (University of Minnesota – Twin Cities)

Abstract: Motivated by the rapid deployment of 5G, we carry out an in-depth measurement study of the performance, power consumption, and application quality-of-experience (QoE) of commercial 5G networks in the wild. We examine different 5G carriers, deployment schemes (Non-Standalone, NSA vs. Standalone, SA), radio bands (mmWave and sub 6-GHz), protocol configurations (_e.g._ Radio Resource Control state transitions), mobility patterns (stationary, walking, driving), client devices (_i.e._ User Equipment), and upper-layer applications (file download, video streaming, and web browsing). Our findings reveal key characteristics of commercial 5G in terms of throughput, latency, handover behaviors, radio state transitions, and radio power consumption under the above diverse scenarios, with detailed comparisons to 4G/LTE networks. Furthermore, our study provides key insights into how upper-layer applications should best utilize 5G by balancing the critical tradeoff between performance and energy consumption, as well as by taking into account the availability of both network and computation resources. We have released the datasets and tools of our study at

BlueFi: bluetooth over WiFi

Hsun-Wei Cho, Kang G. Shin (The University of Michigan)

Abstract: Bluetooth and WiFi are the two dominant technologies enabling the communication of mobile and IoT devices. Built with specific design goals and principles, they are vastly different, each using its own hardware and software. Thus, they are not interoperable and require different hardware.

One may, therefore, ask a simple, yet seemingly impossible question: “Can we transmit Bluetooth packets on commercial off-the-shelf (COTS) WiFi hardware?” We answer this question positively by designing, implementing and demonstrating a novel system called BlueFi. It can readily run on existing, widely-deployed WiFi devices without modifying NIC firmware/hardware. BlueFi works by reversing the signal processing of WiFi hardware and finds special 802.11n packets that are decodable by unmodified Bluetooth devices. With BlueFi, every 802.11n device can be used simultaneously as a Bluetooth device, which instantly increases the coverage of Bluetooth, thanks to the omnipresence of WiFi devices. BlueFi is particularly useful for WiFi-only devices or environments.

We implement and evaluate BlueFi on devices with widely-adopted WiFi chips. We also construct two prevalent end-to-end apps — Bluetooth beacon and audio — to showcase the practical use of BlueFi. The former allows ordinary APs to send location beacons; the latter enables WiFi chips to stream Bluetooth audio in real time.

Programmable packet scheduling with a single queue

Zhuolong Yu, Chuheng Hu, Jingfeng Wu (Johns Hopkins University); Xiao Sun (Stony Brook University); Vladimir Braverman (Johns Hopkins University); Mosharaf Chowdhury (University of Michigan); Zhenhua Liu (Stony Brook University); Xin Jin (Peking University)

Abstract: Programmable packet scheduling enables scheduling algorithms to be programmed into the data plane without changing the hardware. Existing proposals either have no hardware implementations for switch ASICs or require multiple strict-priority queues.
We present Admission-In First-Out (AIFO) queues, a new solution for programmable packet scheduling that uses only a single first-in first-out queue. AIFO is motivated by the confluence of two recent trends: shallow buffers in switches and fast-converging congestion control in end hosts, that together leads to a simple observation: the decisive factor in a flow’s completion time (FCT) in modern datacenter networks is often which packets are enqueued or dropped, not the ordering they leave the switch. The core idea of AIFO is to maintain a sliding window to track the ranks of recent packets and compute the relative rank of an arriving packet in the window for admission control. Theoretically, we prove that AIFO provides bounded performance to Push-In First-Out (PIFO). Empirically, we fully implement AIFO and evaluate AIFO with a range of real workloads, demonstrating AIFO closely approximates PIFO. Importantly, unlike PIFO, AIFO can run at line rate on existing hardware and use minimal switch resources—as few as a single queue.