Chin Guok

chin guok2
Chin Guok
Chief Technology Officer, Planning and Innovation Group Lead

Chin Guok has been with ESnet for more than 25 years and has led many innovative projects during that time. In 2006, Guok conceived and led the On-Demand Secure Circuits and Advance Reservation System (OSCARS) project, which won an R&D 100 award as well as the Department of Energy Secretary’s Honor award. More recently, Guok led the design for ESnet’s next-generation network, ESnet6. He was also the lead in delivering the market-leading ESnet High Touch project and the in-network cache deployment, along with being deeply engaged with the SENSE/Rucio collaboration and the ExaFEL project. Guok is also a sought-after speaker internationally and most recently gave a keynote address at the Korea Institute of Science and Technology Information (KISTI) anniversary event.

Guok’s research interests include high-performance networking and network protocols, dynamic network resource provisioning, network tuning issues, and hybrid network traffic engineering. Guok has an M.S. in Computer Science from the University of Arizona and a B.S. in Computer Science from the University of Pacific.

Journal Articles

W Bhimji, D Carder, E Dart, J Duarte, I Fisk, R Gardner, C Guok, B Jayatilaka, T Lehman, M Lin, C Maltzahn, S McKee, MS Neubauer, O Rind, O Shadura, NV Tran, P van Gemmeren, G Watts, BA Weaver, F Würthwein, “Snowmass 2021 Computational Frontier CompF4 Topical Group Report Storage and Processing Resource Access”, Computing and Software for Big Science, April 2023, 7,

The Snowmass 2021 CompF4 topical group’s scope is facilities R&D, where we consider “facilities” as the hardware and software infrastructure inside the data centers plus the networking between data centers, irrespective of who owns them, and what policies are applied for using them. In other words, it includes commercial clouds, federally funded High Performance Computing (HPC) systems for all of science, and systems funded explicitly for a given experimental or theoretical program. However, we explicitly consider any data centers that are integrated into data acquisition systems or trigger of the experiments out of scope here. Those systems tend to have requirements that are quite distinct from the data center functionality required for “offline” processing and storage.

Inder Monga, Chin Guok, John MacAuley, Alex Sim, Harvey Newman, Justas Balcas, Phil DeMar, Linda Winkler, Tom Lehman, Xi Yang, “Software-Defined Network for End-to-end Networked Science at the Exascale”, Future Generation Computer Systems, April 13, 2020,


Domain science applications and workflow processes are currently forced to view the network as an opaque infrastructure into which they inject data and hope that it emerges at the destination with an acceptable Quality of Experience. There is little ability for applications to interact with the network to exchange information, negotiate performance parameters, discover expected performance metrics, or receive status/troubleshooting information in real time. The work presented here is motivated by a vision for a new smart network and smart application ecosystem that will provide a more deterministic and interactive environment for domain science workflows. The Software-Defined Network for End-to-end Networked Science at Exascale (SENSE) system includes a model-based architecture, implementation, and deployment which enables automated end-to-end network service instantiation across administrative domains. An intent based interface allows applications to express their high-level service requirements, an intelligent orchestrator and resource control systems allow for custom tailoring of scalability and real-time responsiveness based on individual application and infrastructure operator requirements. This allows the science applications to manage the network as a first-class schedulable resource as is the current practice for instruments, compute, and storage systems. Deployment and experiments on production networks and testbeds have validated SENSE functions and performance. Emulation based testing verified the scalability needed to support research and education infrastructures. Key contributions of this work include an architecture definition, reference implementation, and deployment. This provides the basis for further innovation of smart network services to accelerate scientific discovery in the era of big data, cloud computing, machine learning and artificial intelligence.

Syed Asif Raza, Wenji Wu, Qiming Lu, Liang Zhang, Sajith Sasidharan, Phil DeMar, Chin Guok, John Macauley, Eric Pouyoul, Jin Kim, Seo-Young Noh, “AmoebaNet: An SDN-enabled network service for big data science”, Journal of Network and Computer Applications, Elsevier, October 1, 2018, 119:70-82,

M Kiran, E Pouyoul, A Mercian, B Tierney, C Guok, I Monga, “Enabling intent to configure scientific networks for high performance demands”, Future Generation Computer Systems, August 2, 2017,

Z. Yan, M. Veeraraghavan, C. Tracy, C. Guok, “On How to Provision Virtual Circuits for Network-Redirected Large-Sized, High-Rate Flows”, International Journal on Advances in Internet Technology, vol. 6, no. 3 & 4, 2013, November 1, 2013,

Neal Charbonneau, Vinod M. Vokkarane, Chin Guok, Inder Monga, “Advance Reservation Frameworks in Hybrid IP-WDM Networks”, IEEE Communications Magazine, May 9, 2011, 59, Issu:132-139,

Tom Lehman, Xi Yang, Nasir Ghani, Feng Gu, Chin Guok, Inder Monga, and Brian Tierney, “Multilayer Networks: An Architecture Framework”, IEEE Communications Magazine, May 9, 2011,

Inder Monga, Chin Guok, William E. Johnston, and Brian Tierney, “Hybrid Networks: Lessons Learned and Future Challenges Based on ESnet4 Experience”, IEEE Communications Magazine, May 1, 2011,

Chin P. Guok, Jason R. Lee, Karlo Berket, “Improving The Bulk Data Transfer Experience”, International Journal of Internet Protocol Technology 2008 - Vol. 3, No.1 pp. 46 - 53, January 1, 2008,

“Measurements On Hybrid Dedicated Bandwidth Connections”, INFOCOM 2007, IEEE (TCHSN/ONTC), May 1, 2007,


Conference Papers

Zhongfen Deng, Kesheng Wu, Alex Sim, Inder Monga, Chin Guok, et al, “Analyzing Transatlantic Network Traffic over Scientific Data Caches”, 6th ACM International Workshop on ​System and Network Telemetry and Analysis, July 31, 2023,

Large scientific collaborations often share huge volumes of data around the world. Consequently a significant amount of network bandwidth is needed for data replication and data access. Users in the same region may possibly share resources as well as data, especially when they are working on related topics with similar datasets. In this work, we study the network traffic patterns and resource utilization for scientific data caches connecting European networks to the US. We explore the efficiency of resource utilization, especially for network traffic which consists mostly of transatlantic data transfers, and the potential for having more caching node deployments. Our study shows that these data caches reduced network traffic volume by 97% during the study period. This demonstrates that such caching nodes are effective in reducing wide-area network traffic.

Alex Sim, Ezra Kissel, Damian Hazen, Chin Guok, “Experiences in deploying in-network data caches”, 26th International Conference on Computing in High Energy & Nuclear Physics, May 11, 2023,

Caitlin Sim, Kesheng Wu, Alex Sim, Inder Monga, Chin Guok, et al, “Predicting Resource Utilization Trends with Southern California Petabyte Scale Cache”, 26th International Conference on Computing in High Energy & Nuclear Physics, May 8, 2023,

Large community of high-energy physicists share their data all around world making it necessary to ship a large number of files over wide-area networks. Regional disk caches such as the Southern California Petabyte Scale Cache have been deployed to reduce the data access latency. We observe that about 94% of the requested data volume were served from this cache, with-out remote transfers, between Sep. 2022 and July 2023. In this paper, we show the predictability of the resource utilization by exploring the trends of recent cache usage. The time series based prediction is made with a machine learning approach and the prediction errors are small relative to the variation in the input data. This work would help understanding the characteristics of the resource utilization and plan for additional deployments of caches in the future.

Caitlin Sim, Kesheng Wu, Alex Sim, Inder Monga, Chin Guok, et al, “Predicting Resource Usage Trends with Southern California Petabyte Scale Cache”, 26th International Conference on Computing in High Energy & Nuclear Physics, May 8, 2023,

Caitlin Sim, Kesheng Wu, Alex Sim, Inder Monga, Chin Guok, “Effectiveness and predictability of in-network storage cache for Scientific Workflows””, IEEE International Conference on Computing, Networking and Communication, February 20, 2023,

Large scientific collaborations often have multiple scientists accessing the same set of files while doing different analyses, which create repeated accesses to the large amounts of shared data located far away. These data accesses have long latency due to distance and occupy the limited bandwidth available over the wide-area network. To reduce the wide-area network traffic and the data access latency, regional data storage caches have been installed as a new networking service. To study the effectiveness of such a cache system in scientific applications, we examine the Southern California Petabyte Scale Cache for a high-energy physics experiment. By examining about 3TB of operational logs, we show that this cache removed 67.6% of file requests from the wide-area network and reduced the traffic volume on wide-area network by 12.3TB (or 35.4%) an average day. The reduction in the traffic volume (35.4%) is less than the reduction in file counts (67.6%) because the larger files are less likely to be reused. Due to this difference in data access patterns, the cache system has implemented a policy to avoid evicting smaller files when processing larger files. We also build a machine learning model to study the predictability of the cache behavior. Tests show that this model is able to accurately predict the cache accesses, cache misses, and network throughput, making the model useful for future studies on resource provisioning and planning.


Wenji Wu, Liang Zhang, Qiming Lu, Phil DeMar, Robert Illingworth, Joe Mambretti, Se-Young Yu, Jim Hao Chen, Inder Monga, Xi Yang, Tom Lehman, Chin Guok, John MacAuley, “ROBIN (RuciO/BIgData Express/SENSE) A Next-Generation High-Performance Data Service Platform”, 2020 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), IEEE/ACM, December 31, 2020,

Qiming Lu, Liang Zhang, Sajith Sasidharan, Wenji Wu, Phil DeMar, Chin Guok, John Macauley, Inder Monga, Se-Young Yu, Jim Hao Chen, Joe Mambretti, Jin Kim, Seo-Young Noh, Xi Yang, Tom Lehman, Gary Liu, “BigData Express: Toward Schedulable, Predictable, and High-Performance Data Transfer”, 2018 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), IEEE/ACM, February 24, 2019,

Inder Monga, Chin Guok, John Macauley, Alex Sim, Harvey Newman, Justas Balcas, Phil DeMar, Linda Winkler, Xi Yang, Tom Lehman, “SDN for End-to-end Networked Science at the Exascale (SENSE)”, INDIS Workshop SC18, November 11, 2018,

The Software-defined network for End-to-end Networked Science at Exascale (SENSE) research project is building smart network services to accelerate scientific discovery in the era of ‘big data’ driven by Exascale, cloud computing, machine learning and AI. The project’s architecture, models, and demonstrated prototype define the mechanisms needed to dynamically build end-to-end virtual guaranteed networks across administrative domains, with no manual intervention. In addition, a highly intuitive ‘intent’ based interface, as defined by the project, allows applications to express their high-level service requirements, and an intelligent, scalable model-based software orchestrator converts that intent into appropriate network services, configured across multiple types of devices. The significance of these capabilities is the ability for science applications to manage the network as a firstclass schedulable resource akin to instruments, compute, and storage, to enable well defined and highly tuned complex workflows that require close coupling of resources spread across a vast geographic footprint such as those used in science domains like high-energy physics and basic energy sciences.

Qiming Lu, Liang Zhang, Sajith Sasidharan, Wenji Wu, Phil DeMar, Chin Guok, John Macauley, Inder Monga, Se-young Yu, Jim Hao Chen, others, “Bigdata express: Toward schedulable, predictable, and high-performance data transfer”, 2018 IEEE/ACM Innovating the Network for Data-Intensive Science (INDIS), January 1, 2018, 75--84,

M Kiran, E Pouyoul, A Mercian, B Tierney, C Guok, I Monga, “Enabling Intent to Configure Scientific Networks for High Performance Demands”, 3nd International Workshop on Innovating the Network for Data Intensive Science (INDIS) 2016, SC16., November 10, 2016,

Henrique Rodriguez, Inder Monga, Abhinava Sadasivarao , Sharfuddin Sayed, Chin Guok, Eric Pouyoul, Chris Liou,Tajana Rosing, “Traffic Optimization in Multi-Layered WANs using SDN”, 22nd Annual Symposium on High-Performance Interconnects, Best Student Paper Award, August 27, 2014,

Wide area networks (WAN) forward traffic through a mix of packet and optical data planes, composed by a variety of devices from different vendors. Multiple forwarding technologies and encapsulation methods are used for each data plane (e.g. IP, MPLS, ATM, SONET, Wavelength Switching). Despite standards defined, the control planes of these devices are usually not interoperable, and different technologies are used to manage each forwarding segment independently (e.g. OpenFlow, TL-1, GMPLS). The result is lack of coordination between layers and inefficient resource usage. In this paper we discuss the design and implementation of a system that uses unmodified OpenFlow to optimize network utilization across layers, enabling practical bandwidth virtualization. We discuss strategies for scalable traffic monitoring and to minimize losses on route updates across layers. We explore two use cases that benefit from multi-layer bandwidth on demand provisioning. A prototype of the system was built open using a traditional circuit reservation application and an unmodified SDN controller, and its evaluation was per-formed on a multi-vendor testbed.



Abhinava Sadasivarao, Sharfuddin Syed, Chris Liou, Ping Pan, Andrew Lake, Chin Guok, Inder Monga, “Open Transport Switch - A Software Defined Networking Architecture for Transport Networks”, August 17, 2013,


There have been a lot of proposals to unify the control and management of packet and circuit networks but none have been deployed widely. In this paper, we propose a sim- ple programmable architecture that abstracts a core transport node into a programmable virtual switch, that meshes well with the software-defined network paradigm while leverag- ing the OpenFlow protocol for control. A demonstration use-case of an OpenFlow-enabled optical virtual switch im- plementation managing a small optical transport network for big-data applications is described. With appropriate exten- sions to OpenFlow, we discuss how the programmability and flexibility SDN brings to packet-optical backbone networks will be substantial in solving some of the complex multi- vendor, multi-layer, multi-domain issues service providers face today. 


Z. Yan, M. Veeraraghavan, C. Tracy, and C. Guok, “On how to Provision Quality of Service (QoS) for Large Dataset Transfers”, Proceedings of the Sixth International Conference on Communication Theory, Reliability, and Quality of Service, April 21, 2013,

Inder Monga, Eric Pouyoul, Chin Guok, “Software Defined Networking for big-data science (paper)”, SuperComputing 2012, November 11, 2012,


University campuses, Supercomputer centers and R&E networks are challenged to architect, build and support IT infrastructure to deal effectively with the data deluge facing most science disciplines. Hybrid network architecture, multi-domain bandwidth reservations, performance monitoring and GLIF Open Lightpath Exchanges (GOLE) are examples of network architectures that have been proposed, championed and implemented successfully to meet the needs of science. Most recently, Science DMZ, a campus design pattern that bypasses traditional performance hotspots in typical campus network implementation, has been gaining momentum. In this paper and corresponding demonstration, we build upon the SC11 SCinet Research Sandbox demonstrator with Software-Defined networking to explore new architectural approaches. A virtual switch network abstraction is explored, that when combined with software-defined networking concepts provides the science users a simple, adaptable network framework to meet their upcoming application requirements. 


Tom Lehman , Xi Yang, Chin P. Guok, Nageswara S. V. Rao, Andy Lake, John Vollbrecht, Nasir Ghani, “Control Plane Architecture and Design Considerations for Multi-Service, Multi-Layer, Multi-Domain Hybrid Networks”, INFOCOM 2007, IEEE (TCHSN/ONTC), May 1, 2007,

Chin Guok, David Robertson, Mary Thompson, Jason Lee, Brian Tierney and William Johnston, “Intra and Interdomain Circuit Provisioning Using the OSCARS Reservation System”, Third International Conference on Broadband Communications Networks, and Systems, IEEE/ICST, October 1, 2006,

Book Chapters

William Johnston, Evangelos Chaniotakis, Eli Dart, Chin Guok, Joe Metzger, Brian Tierney, “The Evolution of Research and Education Networks and their Essential Role in Modern Science”, Trends in High Performance & Large Scale Computing, ( November 1, 2008)

Published in: "Trends in High Performance & Large Scale Computing" Lucio Grandinetti and Gerhard Joubert, Editors


Chin Guok, ESnet's In-Network Caching Pilot, The Network Conference 2023 (TNC'23), June 5, 2023,

Ezra Kissel, Chin Guok, Alex Sim, Experiences in deploying in-network data caches, 26th International Conference on Computing in High Energy & Nuclear Physics (CHEP 2023), May 8, 2023,

Chin Guok, ESnet In-Network Caching Pilot, LHCOPN-LHCONE Meeting #50, 2023, April 18, 2023,

Inder Monga, Chin Guok, SDN for End-to-End Networking at Exascale, February 16, 2016,

Traditionally, WAN and campus networks and services have evolved independently from each other. For example, MPLS traffic engineered and VPN technologies have been targeted towards the WAN, while the LAN (or last mile) implementations have not incorporated that functionality. These restrictions have resulted in dissonance in services offered in the WAN vs. the LAN. While OSCARS/NSI virtual circuits are widely deployed in the WAN, they typically only run from site boundary to site boundary, and require painful phone calls, manual configuration, and resource allocation decisions for last mile extension. Such inconsistencies in campus infrastructures, all the way from the campus edge to the data-transfer hosts, often lead to unpredictable application performance. New architectures such as the Science DMZ have been successful in simplifying the variance, but the Science DMZ is not designed or able to solve the end-to-end orchestration problem. With the advent of SDN, the R&E community has an opportunity to genuinely orchestrate end-to-end services - and not just from a network perspective, but also from an end-host perspective. In addition, with SDN, the opportunity exists to create a broader set of custom intelligent services that are targeted towards specific science application use-cases. This proposal describes an advanced deployment of SDN equipment and creation of a comprehensive SDN software platform that will help with bring together the missing end-to-end story. 

Abhinava Sadasivarao, Sharfuddin Syed, Ping Pan, Chris Liou, Andy Lake, Chin Guok, Inder Monga, Open Transport Switch: A Software Defined Networking Architecture for Transport Networks, Workshop, August 16, 2013,

Presentation at HotSDN Workshop as part of SIGCOMM 2013

M. Boddie, T. Entel, C. Guok, A. Lake, J. Plante, E. Pouyoul, B. H. Ramaprasad, B. Tierney, J. Triay, V. M. Vokkarane, On Extending ESnet's OSCARS with a Multi-Domain Anycast Service, IEEE ONDM 2012, December 2012,

I. Monga, E. Pouyoul, C. Guok, Software-Define Networking for Big-Data Science – Arthictectural Models from Campus to the WAN, SC12: IEEE HPC, November 2012,

C.Guok, E, Chaniotakis, A. Lake, OSCARS Production Deployment Experiences, GLIF NSI Operationalization Meeting, October 2012,

C. Guok, I. Monga, IDCP and NSI: Lessons Learned, Deployments and Gap Analysis, OGF 34, March 2012,

T. Lehman, C. Guok, Advanced Resource Computation for Hybrid Service and TOpology Networks (ARCHSTONE), DOE ASCR PI Meeting, March 2012,

Chin Guok, Evolution of OSCARS, Joint Techs, January 23, 2012,

On-demand Secure Circuits and Advance Reservation System (OSCARS) has evolved tremendously since its conception as a DOE funded project to ESnet back in 2004. Since then, it has grown from a research project to a collaborative open-source software project with production deployments in several R&E networks including ESnet and Internet2. In the latest release of OSCARS as version 0.6, the software was redesigned to flexibly accommodate both research and production needs. It is being used currently by several research projects to study path computation algorithms, and demonstrate multi-layer circuit management. Just recently, OSCARS 0.6 was leveraged to support production level bandwidth management in the ESnet ANI 100G prototype network, SCinet at SC11 in Seattle, and the Internet2 DYNES project. This presentation will highlight the evolution of OSCARS, activities surrounding OSCARS v0.6 and lessons learned, and share with the community the roadmap for future development that will be discussed within the open-source collaboration.

C. Guok, OSCARS, GENI Project Office Meeting, May 2011,

W.E. Johnston, C. Guok, J. Metzger, B. Tierney, Network Services for High Performance Distributed Computing and Data Management, The Second International Conference on Parallel, Distributed, Grid, and Cloud Computing for Engineering, Ajaccio - Corsica - France, April 12, 2011,

Chaitanya S. K. Vadrevu, Massimo Tornatore, Chin P. Guok, Inder Monga, A Heuristic for Combined Protection of IP Services and Wavelength Services in Optical WDM Networks, IEEE ANTS 2010, December 2010,

C. Guok, OSCARS Roadmap, OGF 28; DICE Control Plane WG, May 2010,

C. Guok, I. Monga, Composible Network Service Framework, ESCC, February 2010,

C. Guok, ESnet OSCARS, DOE Joint Engineering Taskforce, February 2009,

Chin Guok, David Robertson, Evangelos Chaniotakis, Mary Thompson, William Johnston, Brian Tierney, A User Driven Dynamic Circuit Network Implementation, IEEE DANMS 2008, November 2008,

C. Guok, Impact of ESnet OSCARS and Collaborative Projects, SC07, November 2007,

ESnet On-demand Secure Circuits and Advance Reservation System (OSCARS), Google invited talk; Advanced Networking for Distributed Petascale Science Workshop; IEEE GridNets; QUILT Fall Fiber Workshop, 2008, 2006,


Inder Monga, Chin Guok, Arjun Shankar, “Federated IRI Science Testbed (FIRST): A Concept Note”, DOE Office of Science, December 7, 2023, LBNL LBNL-2001553

The Department of Energy’s (DOE’s) vision for an Integrated Research Infrastructure (IRI) is to empower researchers to smoothly and securely meld the DOE’s world-class user facilities and research infrastructure in novel ways in order to radically accelerate discovery and innovation. Performant IRI arises through the continuous interoperability of research workflows with compute, storage, and networking infrastructure, fulfilling researchers’ quests to gain insight from observational and experimental data. Decades of successful research, pilot projects, and demonstrations point to the extraordinary promise of IRI but also indicate the intertwined technological, policy, and sociological hurdles it presents. Creating, developing, and stewarding the conditions for seamless interoperability of DOE research infrastructure, with clear value propositions to stakeholders to opt into an IRI ecosystem, will be the next big step.

The IRI testbed will tie together experimental and observational instruments, ASCR compute facilities for largescale analysis, and edge computing for data reduction and filtering using Energy Sciences Network (ESnet), the high performance network and DOE user facility. The testbed will provide pre-production capabilities that are beyond a demonstration of technology.

Governance, funding, and resource allocation are beyond the scope of this document: it seeks to provide a high-level view of potential benefits, focus areas, and the working groups whose formation would further define the testbed’s design, activities, and goals.