I received my MS and PhD in Computer Science at University of Southern California where I was advised by Prof. Ramesh Govindan and Prof. Barath Raghavan at Networked Systems Laboratory at University of Southern California. I received my BS degree (2014) and M.S. degree (2016) in Electrical Engineering at National Taiwan University. My current research interest is to automate protocol code generation on specifications, in which I target on analyzing English-written ambiguous RFCs and automatically converting all types of protocols into executable and efficient codes. My broader interest includes exploring diverse network infrastructures and networked systems. If you have any questions, please contact me at [last name]y[at]usc[dot]edu
Teaching Assistant at USC
Course: Advanced Operating System, Fall 2018, Instructor: Prof. Ramesh Govindan
Graduate Research Assistant (August 2016 - May 2023)
Department of Computer Science, University of Southern California.
Software Engineer Intern, Systems and Infrastructure (PhD)
Meta Inc. Remote, US · Menlo Park, CA
For decades, drafting Internet protocols has taken significant amounts of human supervision due to the fundamental ambiguity of natural language. Given such ambiguity, it is also not surprising that protocol implementations have long exhibited bugs. This pain and overhead can be significantly reduced with the help of natural language processing (NLP).We recently applied NLP to identify ambiguous or under-specified sentences in RFCs, and to generate protocol implementations automatically when the ambiguity is clarified. However this system is far from general or deployable. To further reduce the overhead and errors due to ambiguous sentences, and to improve the generality of this system, much work remains to be done. In this paper, we consider what it would take to produce a fully-general and useful system for easing the natural-language challenges in the RFC process.
For decades, Internet protocols have been specified using natural language. Given the ambiguity inherent in such text, it is not surprising that protocol implementations have long exhibited bugs. In this paper, we apply natural language processing (NLP) to effect semi-automated generation of protocol implementations from specification text. Our system, Sage, can uncover ambiguous or under-specified sentences in specifications; once these are clarified by the author of the protocol specification, Sage can generate protocol code automatically.Using Sage, we discover 5 instances of ambiguity and 6 instances of under-specification in the ICMP RFC; after fixing these, Sage is able to automatically generate code that interoperates perfectly with Linux implementations. We show that Sage generalizes to sections of BFD, IGMP, and NTP and identify additional conceptual components that Sage needs to support to generalize to complete, complex protocols like BGP and TCP.
Network Functions (NFs) perform on-path processing of network traffic. ISPs are deploying NF Virtualization (NFV) with software NFs run on commodity servers. ISPs aim to ensure that NF chains, directed acyclic graphs of NFs, do not violate Service Level Objectives (SLOs) promised by the ISP to its customers. To meet SLOs, NFV systems sometimes leverage on-path hardware (such as programmable switches and smart NICs) to accelerate NF execution.Lemur places and executes NF chains across heterogeneous hardware while meeting SLOs. Lemur’s novel placement algorithm yields an SLO-satisfying NF placement while weighing many constraints: hardware memory and processing stages, server cores, link capacity, NF profiles, and NF chain interactions. Lemur’s metacompiler automatically generates code and rules (in P4, Python, eBPF, C++, and OpenFlow) to stitch cross-platform NF chain execution while also optimizing resource usage. Our experiments show that Lemur is alone among competing strategies in meeting SLOs for canonical NF chains while maximizing marginal throughput (the traffic rate in excess of the service-level objective).
