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Jianfeng Tan

dblp:140/9465 · DBLP profile ↗
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6ranked-venue papers
0as first author
5since 2021 · last 2026
—ORCID · conflict

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 4 · 3 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021Theory of computation · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
3 papers
Cloud and datacenter computing · 88% Parallel and multicore computing · 12%
Software engineering, system software, and programming languages
2 papers
Operating systems · 87% Software testing · 13%
Computer networks
1 paper
Transport protocols and congestion control · 100%

Topics — the 12 heaviest of 12, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cloud and datacenter computing
virtualization
2.022026
DCS3: A Dual-Layer Co-Aware Scheduler With Stealing Balance and Synchronized Priority in Virtualization Environments · IEEE Trans. Computers 2026
SKernel: An Elastic and Efficient Secure Container System at Scale with a Split-Kernel Architecture · EuroSys 2026
Operating systems › system security › operating system security › protection mechanism › isolation
kernel isolation
1.012026
SKernel: An Elastic and Efficient Secure Container System at Scale with a Split-Kernel Architecture · EuroSys 2026
Cloud and datacenter computing › cluster resource management and scheduling › resource scheduling
cross-layer scheduling
1.012026
DCS3: A Dual-Layer Co-Aware Scheduler With Stealing Balance and Synchronized Priority in Virtualization Environments · IEEE Trans. Computers 2026
Parallel and multicore computing
load balancing
1.012026
DCS3: A Dual-Layer Co-Aware Scheduler With Stealing Balance and Synchronized Priority in Virtualization Environments · IEEE Trans. Computers 2026
Cloud and datacenter computing › cloud security
secure container runtime
1.012026
SKernel: An Elastic and Efficient Secure Container System at Scale with a Split-Kernel Architecture · EuroSys 2026
Cloud and datacenter computing › virtualization › virtual machine management
virtual machine scheduling
1.012026
DCS3: A Dual-Layer Co-Aware Scheduler With Stealing Balance and Synchronized Priority in Virtualization Environments · IEEE Trans. Computers 2026
Cloud and datacenter computing › serverless computing
cold start mitigation
0.912025
Fork in the Road: Reflections and Optimizations for Cold Start Latency in Production Serverless Systems · OSDI 2025
Cloud and datacenter computing
serverless computing
0.912025
Fork in the Road: Reflections and Optimizations for Cold Start Latency in Production Serverless Systems · OSDI 2025
Transport protocols and congestion control
TCP
0.512021
TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing · USENIX ATC 2021
Cloud and datacenter computing › autoscaling
container autoscaling
0.312026
SKernel: An Elastic and Efficient Secure Container System at Scale with a Split-Kernel Architecture · EuroSys 2026
Cloud and datacenter computing › cluster resource management and scheduling
cluster resource management
0.312025
Fork in the Road: Reflections and Optimizations for Cold Start Latency in Production Serverless Systems · OSDI 2025
Software testing
fuzzing
0.112021
TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing · USENIX ATC 2021

Methods — techniques the papers use, named apart from their topics

split-kernel architecture · 2.0hardware virtualization · 2.0work stealing · 1.0priority synchronization · 1.0fuzzing · 1.0
YearPublicationVenuePosition
2026 SKernel: An Elastic and Efficient Secure Container System at Scale with a Split-Kernel Architecture
abstract
Secure containers leverage hardware virtualization to isolate container sandboxes, enabling dedicated guest kernels to mitigate shared kernel attacks prevalent in traditional systems. However, existing approaches struggle with a fundamental trade-off: VM-based solutions (e.g., Kata) prioritize performance but lack elasticity and on-demand usage for volatile and bursty workloads, while lightweight methods (e.g., gVisor) rely on the host kernel for dynamic resource management at the cost of significant performance degradation due to guest-host dependencies.
Xiaohu Chai, Keyang Hu, Jianfeng Tan, Tiwei Bie, Guotao Tan, Anqi Shen, Dawei Shen, Xinyao Yang, Zhengyu He, Dong Du 0003, Yubin Xia, Kang Chen 0001, Yu Chen 0004
EuroSys3
2026 DCS3: A Dual-Layer Co-Aware Scheduler With Stealing Balance and Synchronized Priority in Virtualization Environments
abstract
Virtualization environments (e.g., containers and hypervisors) achieve isolation of multiple runtime entities but result in two mutually isolated guest and host layers. Such cross-ayer isolation could cause high latency and low throughput of the system. Previous aware scheduling and double scheduling fail to achieve bidirectional coordination between the guest and host layers. To address this challenge, we develop DCS3, a Dual-layer Co-aware Scheduler that combines stealing balance and synchronized priority. Stealing balancing migrates tasks between virtual CPU (vCPU) queues for load balance based on the workloads of physical CPUs (pCPUs). Synchronized priority dynamically adjusts the thread priorities running on the pCPUs according to the current vCPU workloads. The vCPUs and pC-PUs belong to the guest and host layers, respectively. Compared with aware scheduling, double scheduling, and DCS2 (i.e., DCS3 without synchronized priority), DCS3 has the following obvious advantages: 1) Requests Per Second (RPS) increases by up to 52%, 55%, and 2%, respectively; 2) request latency decreases by up to 72%, 71%, and 20%, respectively.
Chenglai Xiong, Guoqi Xie, Zhongjia Wang, Zhenli He, Shaowen Yao 0001, Jianfeng Tan, Tiwei Bie, Shoumeng Yan
IEEE Trans. Computers7
2025 Fork in the Road: Reflections and Optimizations for Cold Start Latency in Production Serverless Systems
Xiaohu Chai, Keyang Hu, Jianfeng Tan, Tiwei Bie, Anqi Shen, Dawei Shen, Qi Xing, Shun Song, Tongkai Yang, Zhengyu He, Dong Du 0003, Yubin Xia, Kang Chen 0001, Yu Chen 0004
OSDI4
2025 Adaptive nested Monte Carlo approach for multi-objective efficient global optimization
Shengguan Xu, Jianfeng Tan, Hongquan Chen, Yisheng Gao
J. Glob. Optim.2
2021 TCP-Fuzz: Detecting Memory and Semantic Bugs in TCP Stacks with Fuzzing
Yonghao Zou, Jia-Ju Bai, Jielong Zhou, Jianfeng Tan, Chenggang Qin, Shi-Min Hu 0001
USENIX ATC4
2013 PAB: Parallelism-Aware Buffer Management Scheme for Nand-Based SSDs
abstract
Recently, internal buffer module and multi-level parallel components have already become the standard elements of SSDs. The internal buffer module is always used as a write cache, reducing the erasures and thus improving overall performance. The multi-level parallelism is exploited to service requests in a concurrent or interleaving manner, which promotes the system throughput. These two aspects have been extensively discussed in the literature. However, current buffer algorithms cannot take full advantage of parallelism inside SSDs. In this paper, we propose a novel write buffer management scheme called Parallelism-Aware Buffer (PAB). In this scheme, the buffer is divided into two parts named as Work-Zone and Para-Zone respectively. Conventional buffer algorithms are employed in the Work-Zone, while the Para-Zone is responsible for reorganizing the requests evicted from Work-Zone according to the underlying parallelism. Simulation results show that with only a small size of Para-Zone, PAB can achieve 19.2% ~ 68.1% enhanced performance compared with LRU based on a page-mapping FTL, while this improvement scope becomes 5.6% ~ 35.6% compared with BPLRU based on the state-of-the-art block-mapping FTL known as FAST.
Xufeng Guo, Jianfeng Tan, Yu-Ping Wang 0001
MASCOTS2