About

George Varghese is a Distinguished Professor in the Department of Computer Science at UCLA Samueli School of Engineering. He holds the Jonathan B. Postel Chair in Networking Engineering and is recognized for his expertise in network verification, network algorithmics, cybersecurity, and the future internet. His research focuses on developing innovative solutions in these areas, contributing significantly to the advancement of networking technology. He earned his PhD from MIT in 1993 and has received numerous awards and recognitions, including membership in the American Academy of Arts and Sciences in 2022, election to the Internet Hall of Fame in 2021, and induction as a member of the National Academy of Engineering. He is also a Fellow of the ACM and the National Academy of Inventors, and has received the ACM SIGCOMM Lifetime Achievement Award and the Kobayashi Award. His work has had a substantial impact on the field of computer networking and cybersecurity.

Research topics

• Computer Science
• Distributed computing
• Computer network
• Artificial Intelligence
• Computer architecture
• Operating system
• Geology
• Programming language

Selected publications

• Software Managed Networks via Coarsening

  2025-11-17

  articleOpen accessSenior author

  We propose moving from Software Defined Networks (SDN) to Software Managed Networks (SMN) where all information for managing the life cycle of a network (from deployment to operations to upgrades), across all layers (from Layer 1 through 7) is stored in a central repository. Crucially, a SMN also has a generalized control plane that, unlike SDN, controls all aspects of the cloud including traffic management (e.g., capacity planning) and reliability (e.g., incident routing) at both short (minutes) and large (years) time scales. Just as SDN allows better routing, a SMN improves visibility and enables cross-layer optimizations for faster response to failures and better network planning and operations. Implemented naively, SMN for planetary sc6ale networks requires orders of magnitude larger and more heterogeneous data (e.g., alerts, logs) than SDN. We address this using coarsening — mapping complex data to a more compact abstract representation that has approximately the same effect, and is more scalable, maintainable, and learnable. We show examples including Coarse Bandwidth Logs for capacity planning and Coarse Dependency Graphs for incident routing. Coarse Dependency Graphs improve an incident routing metric from 45% to 78% while for a distributed approach like Scouts the same metric was 22%. We end by discussing how to realize SMN, and suggest cross-layer optimizations and coarsenings for other operational and planning problems in networks.

  PublisherOA PDFDOI

• Scaling IP Lookup to Large Databases using the CRAM Lens

  ArXiv.org · 2025-03-04

  preprintOpen accessSenior author

  Wide-area scaling trends require new approaches to Internet Protocol (IP) lookup, enabled by modern networking chips such as Intel Tofino, AMD Pensando, and Nvidia BlueField, which provide substantial ternary content-addressable memory (TCAM) and static random-access memory (SRAM). However, designing and evaluating scalable algorithms for these chips is challenging due to hardware-level constraints. To address this, we introduce the CRAM (CAM+RAM) lens, a framework that combines a formal model for evaluating algorithms on modern network processors with a set of optimization idioms. We demonstrate the effectiveness of CRAM by designing and evaluating three new IP lookup schemes: RESAIL, BSIC, and MashUp. RESAIL enables Tofino-2 to scale to 2.25 million IPv4 prefixes$\unicode{x2014}$likely sufficient for the next decade$\unicode{x2014}$while a pure TCAM approach supports only 250k prefixes, just 27% of the current global IPv4 routing table. Similarly, BSIC scales to 390k IPv6 prefixes on Tofino-2, supporting 3.2 times as many prefixes as a pure TCAM implementation. In contrast, existing state-of-the-art algorithms, SAIL for IPv4 and Hi-BST for IPv6, scale to considerably smaller sizes on Tofino-2.

  PublisherOA PDF

• Tackling Ambiguity in User Intent for LLM-based Network Configuration Synthesis

  2025-11-17

  articleOpen accessSenior author

  Beyond hallucinations, another problem in program synthesis using LLMs is ambiguity in user intent. We illustrate the ambiguity problem in a networking context for LLM-based incremental configuration synthesis of route maps and ACLs. Configuration stanzas frequently overlap in header space, making the relative priority of actions impossible for the LLM to infer without user interaction. Measurements in a large cloud identify complex ACLs with 100s of overlaps, showing ambiguity is a real problem. We propose a prototype system, Clarify, augmenting an LLM with a new module called a Disambiguator that helps elicit user intent. On a small synthetic workload, Clarify incrementally synthesizes routing policies and interactively disambiguates user intent to ensure correctness.

  PublisherDOI

• Model-Based Diagnosis: Automating End-to-End Diagnosis of Network Failures

  ArXiv.org · 2025-06-29

  preprintOpen access

  Fast diagnosis and repair of enterprise network failures is critically important since disruptions cause major business impacts. Prior works focused on diagnosis primitives or procedures limited to a subset of the problem, such as only data plane or only control plane faults. This paper proposes a new paradigm, model-based network diagnosis, that provides a systematic way to derive automated procedures for identifying the root cause of network failures, based on reports of end-to-end user-level symptoms. The diagnosis procedures are systematically derived from a model of packet forwarding and routing, covering hardware, firmware, and software faults in both the data plane and distributed control plane. These automated procedures replace and dramatically accelerate diagnosis by an experienced human operator. Model-based diagnosis is inspired by, leverages, and is complementary to recent work on network verification. We have built NetDx, a proof-of-concept implementation of model-based network diagnosis. We deployed NetDx on a new emulator of networks consisting of P4 switches with distributed routing software. We validated the robustness and coverage of NetDx with an automated fault injection campaign, in which 100% of faults were diagnosed correctly. Furthermore, on a data set of 33 faults from a large cloud provider that are within the domain targeted by NetDx, 30 are efficiently diagnosed in seconds instead of hours.

  PublisherOA PDFDOI

• Contributors

  Elsevier eBooks · 2024-11-01

  book-chapterOpen access
  PublisherDOI

• If Layering is useful, why not Sublayering?

  2024-11-11 · 1 citations

  articleOpen accessSenior author

  The Internet's success arose from classical layering: protocols like TCP and Ethernet can be independently understood, changed, debugged, verified, and offloaded to hardware using a clean service interface between layers. To accrue the same benefits at a finer grain, we suggest sublayering, i.e., layering recursively within each layer. We show that the data link and routing layers have natural sublayers. However, while TCP intuitively decomposes into sub-functions (connection management, reliable delivery, congestion control) common state variables like sequence numbers and window sizes entangle these functions, making sublayering difficult. We propose an alternate sublayered TCP with equivalent functionality which enables easily changing congestion control and connection management. We also argue that sublayering can help create robust and verified Internet protocol implementations akin to seL4 for Operating Systems. To this end, we describe early experiments with a verified sublayered implementation of a simple bit-stuffing protocol using Coq, and a verified monolithic implementation of a lightweight TCP using Dafny. We end with a set of challenges for sublayered protocols.

  PublisherDOI

• Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages

  2024-01-01

  article
  PublisherDOI

• Synthetic Programming Elicitation for Text-to-Code in Very Low-Resource Programming and Formal Languages

  arXiv (Cornell University) · 2024-06-05

  preprintOpen access

  Recent advances in large language models (LLMs) for code applications have demonstrated remarkable zero-shot fluency and instruction following on challenging code related tasks ranging from test case generation to self-repair. Unsurprisingly, however, models struggle to compose syntactically valid programs in programming languages unrepresented in pre-training, referred to as very low-resource Programming Languages (VLPLs). VLPLs appear in crucial settings, including domain-specific languages for internal tools, tool-chains for legacy languages, and formal verification frameworks. Inspired by a technique called natural programming elicitation, we propose designing an intermediate language that LLMs "naturally" know how to use and which can be automatically compiled to a target VLPL. When LLMs generate code that lies outside of this intermediate language, we use compiler techniques to repair the code into programs in the intermediate language. Overall, we introduce \emph{synthetic programming elicitation and compilation} (SPEAC), an approach that enables LLMs to generate syntactically valid code even for VLPLs. We empirically evaluate the performance of SPEAC in a case study for the UCLID5 formal verification language and find that, compared to existing retrieval and fine-tuning baselines, SPEAC produces syntactically correct programs more frequently and without sacrificing semantic correctness.

  PublisherOA PDFDOI

• Extracting the Essential Simplicity of the Internet

  Communications of the ACM · 2023-01-20 · 5 citations

  articleOpen accessSenior author

  Looking past inessential complexities to explain the Internet's simple yet daring design.

  PublisherOA PDFDOI

• What do LLMs need to Synthesize Correct Router Configurations?

  2023 · 63 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Programming language

  We investigate whether Large Language Models (e.g., GPT-4) can synthesize correct router configurations with reduced manual effort. We find GPT-4 works very badly by itself, producing promising draft configurations but with egregious errors in topology, syntax, and semantics. Our strategy, that we call Verified Prompt Programming, is to combine GPT-4 with verifiers, and use localized feedback from the verifier to automatically correct errors. Verification requires a specification and actionable localized feedback to be effective. We show results for two use cases: translating from Cisco to Juniper configurations on a single router, and implementing a no-transit policy on multiple routers. While human input is still required, if we define the leverage as the number of automated prompts to the number of human prompts, our experiments show a leverage of 10X for Juniper translation, and 6X for implementing the no-transit policy, ending with verified configurations.

  PublisherOA PDFDOI

Recent grants

• New Directions in Accounting and Traffic Measurement

  NSF · $650k · 2002–2007

• CNS Core: Large: Collaborative Research: Network Design Automation

  NSF · $2.0M · 2019–2024

Frequent coauthors

• Subhash Suri

  University of California, Santa Barbara

  27 shared

• Cristian Estan

  Google (United States)

  21 shared

• Nick McKeown

  Aberystwyth University

  20 shared

• Florin Baboescu

  13 shared

• Girish Chandranmenon

  Nokia (United States)

  11 shared

• Michael Mitzenmacher

  Harvard University Press

  11 shared

• Amin Vahdat

  Google (United States)

  10 shared

• Jun Xu

  10 shared

Awards & honors

• Member, American Academy of Arts and Sciences (2022)
• Internet Hall of Fame Member (2021)
• NAE Member
• ACM Fellow
• ACM SIGCOMM Lifetime Achievement Award