About

Eric Eide is an Research Associate Professor at the Kahlert School of Computing at the University of Utah. His research interests include High-Performance Computing with a focus on Scalable Systems, Systems such as Networking and Scalable Systems, Programming Languages with an emphasis on Formal Verification and Testing, and Security/Privacy particularly in Systems Security. The University of Utah has a notable history in networking, being one of the first four independent network nodes of the internet, with the first successful transmission occurring on October 29, 1969. Eide's work is situated within this context of pioneering computing research and infrastructure.

Research topics

• Computer Science
• Data Mining
• Machine Learning
• Telecommunications
• Operating system
• Artificial Intelligence
• Database
• Engineering
• Distributed computing
• Software engineering
• Computer network
• Reliability engineering

Selected publications

• Generating Conforming Programs with Xsmith

  2023-10-19 · 5 citations

  articleOpen accessSenior author

  Fuzz testing is an effective tool for finding bugs in software, including programming language compilers and interpreters. Advanced fuzz testers can find deep semantic bugs in language implementations through differential testing. However, input programs used for differential testing must not only be syntactically and semantically valid, but also be free from nondeterminism and undefined behaviors. Developing a fuzzer that produces such programs can require tens of thousands of lines of code and hundreds of person-hours. Despite this significant investment, fuzzers designed for differential testing of different languages include many of the same features and analyses in their implementations. To make the implementation of language fuzz testers for differential testing easier, we introduce Xsmith.

  PublisherOA PDFDOI

• An NSF REU Site Based on Trust and Reproducibility of Intelligent Computation: Experience Report

  2023-11-10

  articleOpen access

  This paper presents an overview of an NSF Research Experience for Undergraduate (REU) Site on Trust and Reproducibility of Intelligent Computation, delivered by faculty and graduate students in the Kahlert School of Computing at University of Utah. The chosen themes bring together several concerns for the future in producing computational results that can be trusted: secure, reproducible, based on sound algorithmic foundations, and developed in the context of ethical considerations. The research areas represented by student projects include machine learning, high-performance computing, algorithms and applications, computer security, data science, and human-centered computing. In the first four weeks of the program, the entire student cohort spent their mornings in lessons from experts in these crosscutting topics, and used one-of-a-kind research platforms operated by the University of Utah, namely NSF-funded CloudLab and POWDER facilities; reading assignments, quizzes, and hands-on exercises reinforced the lessons. In the subsequent five weeks, lectures were less frequent, as students branched into small groups to develop their research projects. The final week focused on a poster presentation and final report. Through describing our experiences, this program can serve as a model for preparing a future workforce to integrate machine learning into trustworthy and reproducible applications.

  PublisherOA PDFDOI

• LongTale: Toward Automatic Performance Anomaly Explanation in Microservices

  2022 · 10 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Artificial Intelligence

  Performance troubleshooting is notoriously difficult for distributed microservices-based applications. A typical root-cause diagnosis for performance anomaly by an analyst starts by narrowing down the scope of slow services, investigates into high-level performance metrics or available logs in the slow components, and finally drills down to an actual cause. This process can be long, tedious, and sometimes aimless due to the lack of domain knowledge and the sheer number of possible culprits. This paper introduces a new machine-learning-driven performance analysis system called LongTale that automates the troubleshooting process for latency-related performance anomalies to facilitate the root cause diagnosis and explanation. LongTale builds on existing application-layer tracing in two significant aspects. First, it stitches application-layer traces with corresponding system stack traces, which enables more informative root-cause analysis. Second, it utilizes a novel machine-learning-driven analysis that feeds on the combined data to automatically uncover the most likely contributing factor(s) for given performance slowdown. We demonstrate how LongTale can be utilized in different scenarios, including abnormal long-tail latency explanation and performance interference analysis.

  PublisherDOI

• Toward Findable, Accessible, Interoperable, and Reusable Cybersecurity Artifacts

  2022-08-04 · 4 citations

  articleOpen access

  Researchers in experimental cybersecurity are increasingly sharing the code, data, and other artifacts associated with their studies. This trend is encouraged and rewarded by conferences and journals through practices such as artifact evaluation and badging. While these trends in sharing artifacts are promising, the cybersecurity community is still far from an ecosystem in which artifacts are FAIR: findable, accessible, interoperable, and reusable. The lack of established standards and best practices for sharing and reuse results in artifacts that are often difficult to find and reuse; in addition, the lack of community standards results in artifacts that may be incomplete and low-quality. In this paper we describe our experience in creating an online community hub, called SEARCCH, to promote the sharing and reuse of artifacts for cybersecurity research. Based on our experience, we offer lessons learned: issues that must be addressed to further promote FAIR principles in experimental cybersecurity.

  PublisherOA PDFDOI

• Mobile and wireless research on the POWDER platform

  2021-06-22 · 1 citations

  article

  POWDER is a highly flexible, deeply programmable, and city-scale scientific instrument that enables cutting-edge research in wireless technologies. Researchers interact with the POWDER platform via the Internet to conduct their experiments, with zero penalty for remote access. In this two-part demonstration, the POWDER implementers show how to use the platform. First, they present the workflow that researchers follow to conduct experiments. Second, they highlight some of the hardware and software building blocks available through POWDER, including components related to over-the-air wireless and mobile networking, 5G, and massive MIMO.

  PublisherDOI

• Powder: Platform for Open Wireless Data-driven Experimental Research

  Computer Networks · 2021 · 54 citations

  • Computer Science
  • Computer Science
  • Telecommunications
  PublisherDOI

• Deepstitch

  2020-12-07

  articleSenior author

  While distributed application-layer tracing is widely used for performance diagnosis in microservices, its coarse granularity at the service level limits its applicability towards detecting more fine-grained system level issues. To address this problem, cross-layer stitching of tracing information has been proposed. However, all existing cross-layer stitching approaches either require modification of the kernel or need updates in the application-layer tracing library to propagate stitching information, both of which add further complex modifications to existing tracing tools. This paper introduces Deepstitch, a deep learning based approach to stitch cross-layer tracing information without requiring any changes to existing application layer tracing tools. Deepstitch leverages a global view of a distributed application composed of multiple services and learns the global system call sequences across all services involved. This knowledge is then used to stitch system call sequences with service-level traces obtained from a deployed application. Our proof of concept experiments show that the proposed approach successfully maps application-level interaction into the system call sequences and can identify thread-level interactions.

  PublisherDOI

• POWDER

  2020 · 101 citations

  • Computer Science
  • Computer Science
  • Computer network

  This paper provides an overview of the Platform for Open Wireless Data-driven Experimental Research (POWDER). POWDER is a city-scale, remotely accessible, end-to-end software defined platform to support mobile and wireless research. Compared to other mobile and wireless testbeds POWDER provides advances in scale, realism, diversity, flexibility, and access.

  PublisherDOI

• Fluorescence: Detecting Kernel-Resident Malware in Clouds

  Recent Advances in Intrusion Detection · 2019-01-01 · 2 citations

  articleSenior author
  Publisher

• The design and operation of cloudlab

  USENIX Annual Technical Conference · 2019-07-10 · 191 citations

  article

  Given the highly empirical nature of research in cloud computing, networked systems, and related fields, testbeds play an important role in the research ecosystem. In this paper, we cover one such facility, CloudLab, which supports systems research by providing raw access to programmable hardware, enabling research at large scales, and creating a shared platform for repeatable research. We present our experiences designing CloudLab and operating it for four years, serving nearly 4,000 users who have run over 79,000 experiments on 2,250 servers, switches, and other pieces of datacenter equipment. From this experience, we draw lessons organized around two themes. The first set comes from analysis of data regarding the use of CloudLab: how users interact with it, what they use it for, and the implications for facility design and operation. Our second set of lessons comes from looking at the ways that algorithms used under the hood, such as resource allocation, have important-- and sometimes unexpected--effects on user experience and behavior. These lessons can be of value to the designers and operators of IaaS facilities in general, systems testbeds in particular, and users who have a stake in understanding how these systems are built.

  Publisher

Recent grants

• CI-EN: Revitalizing Emulab for Modern Networking and Systems Research

  NSF · $2.2M · 2015–2020

Frequent coauthors

• John Regehr

  University of Utah

  17 shared

• Robert Ricci

  University of Utah

  15 shared

• David E. Johnson

  14 shared

• Mike Hibler

  University of Utah

  13 shared

• Jay Lepreau

  University of Utah

  12 shared

• Jacobus Van der Merwe

  University of Utah

  8 shared

• Leigh Stoller

  University of Utah

  7 shared

• Gary Wong

  University of Utah

  6 shared

Education

• Ph.D., Computer Science

  University of Utah

  1995

• M.S., Computer Science

  University of Utah

  1992

• B.S., Computer Science

  University of Utah

  1989