A Survey of Scheduling Mechanisms for Programmable Data Plane in Data Center

LIU Jing-ling; HE Zhong; WANG Zi-xi; FANG Hao; CUI Rui; HUANG Jia-wei

doi:10.12263/DZXB.20250334

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A Survey of Scheduling Mechanisms for Programmable Data Plane in Data Center

SURVEYS AND REVIEWS | 更新时间：2026-04-24

- A Survey of Scheduling Mechanisms for Programmable Data Plane in Data Center
- ACTA ELECTRONICA SINICA Vol. 53, Issue 12, Pages: 4740-4755(2025)
- 作者机构：
  
  中南大学计算机学院，湖南长沙 410083
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China(62302524;U24A20245;62132022);Natural Science Foundation of Hunan Province(2024JJ6531);Guizhou Provincial Science and Technology Projects(［2025］022);Science and Technology Innovation Program of Hunan Province(2024RC1005)
- DOI：10.12263/DZXB.20250334
  CLC： TP393;
- Received：28 April 2025，
  
  Accepted：03 December 2025，
  
  Published：25 December 2025
- 稿件说明：
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刘敬玲, 何种, 汪子汐, 等. 数据中心可编程数据平面的调度机制研究综述[J]. 电子学报, 2025, 53(12): 4740-4755.

LIU Jing-ling, HE Zhong, WANG Zi-xi, et al. A Survey of Scheduling Mechanisms for Programmable Data Plane in Data Center[J]. Acta Electronica Sinica, 2025, 53(12): 4740-4755.
刘敬玲, 何种, 汪子汐, 等. 数据中心可编程数据平面的调度机制研究综述[J]. 电子学报, 2025, 53(12): 4740-4755. DOI：10.12263/DZXB.20250334

LIU Jing-ling, HE Zhong, WANG Zi-xi, et al. A Survey of Scheduling Mechanisms for Programmable Data Plane in Data Center[J]. Acta Electronica Sinica, 2025, 53(12): 4740-4755. DOI：10.12263/DZXB.20250334

摘要

数据中心承载着云计算、大数据分析与人工智能等业务，不同流量对时延和吞吐量的需求各异.时延敏感的任务要求极低延迟，而大规模备份和分析流量则更关注高吞吐量，为此数据中心网络通常采用高效的调度机制以满足不同应用需求.传统调度机制依赖定制网络设备实现来满足特定的应用需求，其开发成本高且可扩展性差，导致难以在网络中快速部署新的调度算法.随着可编程交换机等新型网络设备的出现，数据平面可编程能力得到显著提升，网络设备从功能固化转向灵活配置，这为在数据平面设计高性能的调度机制奠定了基础.近期研究者们提出了大量新颖的可编程调度机制，这些机制极大地提升了网络性能.本文综述了近年数据中心可编程调度机制的设计思想，并将它们划分为通用表达、高并发流量、公平保障、租户隔离4类机制.其中，通用表达型机制提供灵活抽象支持多种策略；高并发流量型机制强调海量并发流下的线速处理；公平保障型机制关注在流或租户间实现带宽公平；租户隔离型机制面向多租户环境提供强隔离和分层资源分配.4类调度机制各有侧重，可互补应对复杂场景需求.随后，本文分别介绍相应代表性调度机制，并进一步比较了各类调度机制的优缺点，最后展望了基于可编程数据平面的调度机制的未来发展方向.

Abstract

Data centers host cloud computing

big data analytics

and artificial intelligence workloads

whose traffic exhibits highly heterogeneous latency and throughput requirements. Latency-sensitive tasks require extremely low delay

whereas large-scale backup and analytics flows are more concerned with high throughput; accordingly

data center networks usually adopt efficient scheduling mechanisms to meet these diverse application needs. Traditional packet scheduling mechanisms are implemented on customized network devices to meet specific application requirements

which leads to high development costs and poor scalability and makes it difficult to rapidly deploy new scheduling algorithms in the network. With the emergence of new network devices such as programmable switches

the programmability of the data plane has been significantly enhanced. Network devices have shifted from fixed functionalities to flexible configurations

laying the groundwork for designing high-performance scheduling mechanisms in the data plane. Recently

researchers have proposed numerous innovative programmable scheduling mechanisms

significantly improving network performance. This paper provides a comprehensive survey of recent programmable scheduling mechanisms for data center networking and classifies them into four categories: general-purpose abstraction mechanisms

mechanisms for high-concurrency traffic

mechanisms for fairness assurance

and mechanisms for tenant-level isolation. General-purpose abstraction mechanisms provide flexible abstractions that support a wide range of scheduling policies. Mechanisms for high-concurrency traffic emphasize line-rate processing under massive flow concurrency. Mechanisms for fairness assurance focus on providing fair bandwidth allocation among flows or tenants. Mechanisms for tenant-level isolation target multi-tenant environments by providing strong isolation and hierarchical resource allocation. These four types of mechanisms have different design emphases but can complement each other to cope with complex and diverse data center scenarios. Then

representative scheduling mechanisms for each category are discussed

followed by a comparative analysis of the advantages and disadvantages of various categories. Finally

the paper concludes with a discussion on the future development trends of scheduling mechanisms based on programmable data planes.

关键词

Keywords

references

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TU Hua-qing

LIAO Jun-hu

ZHU Jun

ZOU Tao

LI Chuan-huang

ZHANG Ru-yun

WU Jiang-xing

CUI Zi-xi

Related Institution

Zhejiang Gongshang University

Zhejiang Laboratory

Institute of Information Technology， PLA Strategic Support Force Information Engineering University

School of Computer and Information Technology，Beijing Jiaotong University

Key Laboratory of Big Data & Artificial Intelligence in Transportation， Ministry of Education

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