2017 Poster Sessions : Network Telemetry through Tomography

Student Name : Yilong Geng, Shiyu Liu, Zi Yin
Advisor : Balaji Prabhakar
Research Areas: Information Systems
Abstract:
The state-of-art data center networks are becoming more and more complex: around 100k servers and 40Gbps links, which makes the network telemetry an increasingly challenging task. Meanwhile, the network telemetry, that is inferring what is happening inside the network, is necessary for learning and control to avoid mini-accidents like congested links, packet drops and packet retransmissions etc. Therefore, we developed the Deep Network Tomography technology, which uses end-to-end measurements to reconstruct highly detailed information of the network, including queue sizes, link utilizations, and even where every single packet is, at a very fine grained time scale with high fidelity. Based on that, a pipeline of sensing, inference, learning and control (SILC) for cloud infrastructure is being built.


Bios:
Shiyu Liu is a 2nd year Ph.D. student in Electrical Engineering Department, Stanford University. Shiyu’s current research topic is inference and learning in Data Center Networks, primarily focusing on high fidelity reconstruction algorithms. Before that he worked on wireless networks, including projects on congestion control in Wi-Fi and intelligent client device sensing with Zigbee. He received his undergraduate degree in Electronic Engineering from Tsinghua University in 2015.

Zi Yin is a 4th year Ph.D. student, Electrical Engineering, Stanford University. Zi’s current research is on applying Machine Learning techniques to problems in Cloud and Data Centers. In the past, he worked on deep learning and its application in natural language processing, in particular sequence-to-sequence models for query relevance scoring. He has abundant experience in statistics and machine learning theory. Zi received his bachelor degrees in Mathematics and Information Engineering from Chinese University of Hong Kong.

Yilong Geng is a 5th year Ph.D. student, Electrical Engineering, Stanford University. Yilong’s research interest is in data center networking, from new hardware architectures to use of machine learning techniques. Currently, he’s designing algorithms and a scalable system for high fidelity network reconstruction and clock synchronization, all based on data from edge measurements. Prior to that, he worked on several NetFPGA projects, including Open Source Network Tester (OSNT) and a new NIC architecture for scalable end-host rate limiting. He received his undergraduate degree in Electronic Engineering from Tsinghua University.