Daehyeok Kim

I am a fifth-year PhD student in the Computer Science Department at Carnegie Mellon University, where I am fortunate to be co-advised by Professors Vyas Sekar and Srinivasan Seshan. I am also a senior researcher at Microsoft.

My research interests lie in the intersection of computer systems and networking with a recent focus on in-network computing. Recently, I have been designing new abstractions and runtime systems for heterogeneous programmable data plane devices such as network switches and NICs to unleash their full potentials. In the past, I worked on various topics in systems security and wireless networks.

Prior to CMU, I worked at KAIST for three years as a research scientist. Before then, I received a BS and MS degree in Computer Science and Engineering from POSTECH, South Korea.

I am a recipient of the 2019 Microsoft Research PhD Fellowship.

recent publications

SIGCOMM

RedPlane: Enabling Fault-Tolerant Stateful In-Switch Applications (to appear)

Daehyeok Kim, Jacob Nelson, Dan Ports, Vyas Sekar, and Srinivasan Seshan

In Proceedings of ACM SIGCOMM, 2021.
SIGCOMM

TEA: Enabling State-Intensive Network Functions on Programmable Switches

Daehyeok Kim, Zaoxing Liu, Yibo Zhu, Changhoon Kim, Jeongkeun Lee, Vyas Sekar, and Srinivasan Seshan

In Proceedings of ACM SIGCOMM, 2020.

Abs Paper Slides Talk video Talk + Q&A video

Programmable switches have been touted as an attractive alternative for deploying network functions (NFs) such as network address translators (NATs), load balancers, and firewalls. However, their limited memory capacity has been a major stumbling block that has stymied their adoption for supporting state-intensive NFs such as cloud-scale NATs and load balancers that maintain millions of flow-table entries. In this paper, we explore a new approach that leverages DRAM on servers available in typical NFV clusters. Our new system architecture, called TEA (Table Extension Architecture), provides a virtual table abstraction that allows NFs on programmable switches to look up large virtual tables built on external DRAM. Our approach enables switch ASICs to access external DRAM purely in the data plane without involving CPUs on servers. We address key design and implementation challenges in realizing this idea. We demonstrate its feasibility and practicality with our implementation on a Tofino-based programmable switch. Our evaluation shows that NFs built with TEA can look up table entries on external DRAM with low and predictable latency (1.8-2.2 μs) and the lookup throughput can be linearly scaled with additional servers (138 million lookups per seconds with 8 servers).
NSDI

FreeFlow: Software-based Virtual RDMA Networking for Containerized Clouds

Daehyeok Kim, Tianlong Yu, Hongqiang Harry Liu, Yibo Zhu, Jitu Padhye, Shachar Raindel, Chuanxiong Guo, Vyas Sekar, and Srinivasan Seshan

In Proceedings of 16th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2019.

Abs Paper Slides Talk + Q&A video

Many popular large-scale cloud applications are increasingly using containerization for high resource efficiency and lightweight isolation. In parallel, many data-intensive applications (e.g., data analytics and deep learning frameworks) are adopting or looking to adopt RDMA for high networking performance. Industry trends suggest that these two approaches are on an inevitable collision course. In this paper, we present FreeFlow, a software-based RDMA virtualization framework designed for containerized clouds. FreeFlow realizes virtual RDMA networking purely with a software-based approach using commodity RDMA NICs. Unlike existing RDMA virtualization solutions, FreeFlow fully satisfies the requirements from cloud environments, such as isolation for multi-tenancy, portability for container migrations, and controllability for control and data plane policies. FreeFlow is also transparent to applications and provides networking performance close to bare-metal RDMA with low CPU overhead. In our evaluations with TensorFlow and Spark, FreeFlow provides almost the same application performance as bare-metal RDMA.
SIGCOMM

HyperLoop: Group-Based NIC-Offloading to Accelerate Replicated Transactions in Multi-Tenant Storage Systems

Daehyeok Kim, Amirsaman Memaripour, Anirudh Badam, Yibo Zhu, Hongqiang Harry Liu, Jitu Padhye, Shachar Raindel, Steven Swanson, Vyas Sekar, and Srinivasan Seshan

In Proceedings of ACM SIGCOMM, 2018.

Abs Paper Slides Talk + Q&A video

Storage systems in data centers are an important component of large-scale online services. They typically perform replicated transactional operations for high data availability and integrity. Today, however, such operations suffer from high tail latency even with recent kernel bypass and storage optimizations, and thus affect the predictability of end-to-end performance of these services. We observe that the root cause of the problem is the involvement of the CPU, a precious commodity in multi-tenant settings, in the critical path of replicated transactions. In this paper, we present HyperLoop, a new framework that removes CPU from the critical path of replicated transactions in storage systems by offloading them to commodity RDMA NICs, with non-volatile memory as the storage medium. To achieve this, we develop new and general NIC offloading primitives that can perform memory operations on all nodes in a replication group while guaranteeing ACID properties without CPU involvement. We demonstrate that popular storage applications can be easily optimized using our primitives. Our evaluation results with microbenchmarks and application benchmarks show that HyperLoop can reduce 99th percentile latency ≈800x with close to 0% CPU consumption on replicas.