面向算力网络的智慧调度综述

李逸博; 李小平; 王爽; 蒋嶷川

doi:10.16383/j.aas.c230196

面向算力网络的智慧调度综述

doi: 10.16383/j.aas.c230196

李逸博^{1, 2,},
李小平^{1, 3,},
王爽^{1, 2,},
蒋嶷川^{1, 2,}

1.
东南大学计算机科学与工程学院南京 211189
2.
东南大学计算机网络和信息集成教育部重点实验室南京 211189
3.
广东工业大学计算机学院广州 510006

基金项目: 国家重点研发计划 (2022YFB3305500), 国家自然科学基金 (62273089)资助

详细信息

作者简介:
李逸博：东南大学计算机科学与工程学院硕士研究生. 2021年获得湘潭大学学士学位. 主要研究方向为分布式计算. E-mail: yiboli@seu.edu.cn

李小平：东南大学计算机科学与工程学院教授. 2002年获得哈尔滨工业大学博士学位. 主要研究方向为调度优化, 服务计算和智能制造. 本文通信作者. E-mail: xpli@seu.edu.cn

王爽：东南大学计算机科学与工程学院讲师. 2020年获得东南大学博士学位. 主要研究方向为调度优化, 云计算和真值发现. E-mail: shuangwang@seu.edu.cn

蒋嶷川：东南大学计算机科学与工程学院教授. 2005年获得复旦大学博士学位. 主要研究方向为分布式人工智能, 复杂智能系统. E-mail: yjiang@seu.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2023-04-10
- 录用日期: 2024-02-20
- 网络出版日期: 2024-05-13

Survey on Wise Scheduling in Computing Power Network

LI Yi-Bo^{1, 2
,},
LI Xiao-Ping^{1, 3
,},
WANG Shuang^{1, 2
,},
JIANG Yi-Chuan^{1, 2
,}

1.
School of Computer Science and Engineering, Southeast University, Nanjing 211189
2.
Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing 211189
3.
School of Computer Science and Technology, Guangdong University of Technology, Guangzhou 510006

Funds: Supported by National Key Research and Development Program of China (2022YFB3305500) and National Natural Science Foundation of China (62273089)

More Information

Author Bio:
LI Yi-Bo　Master student at the School of Computer Science and Engineering, Southeast University. He received his bachelor degree from Xiangtan University in 2021. His main research interest is distributed computing

LI Xiao-Ping　Professor at the School of Computer Science and Engineering, Southeast University. He received his Ph.D. degree from Harbin Institute of Technology in 2002. His research interest covers scheduling optimization, service computing, and intelligent manufacturing. Corresponding author of this paper

WANG Shuang　Lecturer at the School of Computer Science and Engineering, Southeast University. She received her Ph.D. degree from Southeast University in 2020. Her research interest covers scheduling optimization, cloud computing, and truth discovery

JIANG Yi-Chuan　Professor at the School of Computer Science and Engineering, Southeast University. He received his Ph.D. degree from Fudan University in 2005. His research interest covers distributed artificial intelligence and complex intelligent systems

摘要

摘要: 分布异构计算资源通过网络连接形成算力网络 (Computing power network, CPN), 其以“连”和“算”为核心. 针对广分布异构性导致可行解空间巨大、强不确定性导致可行解空间易变、高约束复杂性导致可行解孤岛繁多、多目标性导致冲突目标权衡优化难等挑战, 提出一个多层次算力网络体系框架, 包括参数化结构化业务管理、三阶段(计划、调度、执行)闭环调度模式、多模态资源管理三个功能. 提出支持快速、高效、鲁棒的“算法+知识+数据+算力”的算力网络智慧调度框架, 形式化分析可行解空间, 解析调度策略关键参数, 定性分析调度算法性能与效率的内在关系, 详细综述调度算法类型, 综述算力网络调度研究进展与发展方向. 对比已有相关综述研究, 展望算力网络调度未来理论和技术的难点与趋势.
- 算力网络 /
- 云计算 /
- 边缘计算 /
- 资源调度 /
- 知识
Abstract: Distributed heterogeneous computing resources are connected through the network to form a computing power network (CPN), in which “connection” and “computing” are the cores. There are several great challenges including the huge feasible solution space resulted from widely distributed heterogeneity, the variable feasible solution space caused by strong uncertainty, a large number of feasible solution islands resulted from highly constrained complexity, and the difficulty in optimizing multiple conflicting objectives. For these challenges, a multi-level computing power network framework is proposed in this paper, which contains three functions: Parameterized structural process management, three-phase (planning, scheduling, execution) closed-loop scheduling model, and multi-modal resource management. A computing power network intelligent scheduling framework with “algorithm +knowledge + data + computing power” is proposed to wisely schedule complex tasks to CPN resources effectively, efficiently and robustly. The feasible solution space is formally analyzed. The key parameters of scheduling strategy are introduced. The intrinsic relationship between effectiveness and efficiency of scheduling algorithms are qualitatively analyzed. Different types of scheduling algorithm are surveyed thoroughly. The state-of-the-art and future work are illustrated. The surveys on CPN scheduling algorithms are compared, followed by the theoretical and technical problems and tendency in CPN scheduling algorithms.
- Computing power network (CPN) /
- cloud computing /
- edge computing /
- resource scheduling /
- knowledge

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图 1 不同算力网络概念分布

Fig. 1 Distribution of different computing power network concepts

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图 2 传统云边端示意图

Fig. 2 Schematic diagram of traditional cloud-edge-terminal architecture

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图 3 智慧调度为核心的算力网络体系架构

Fig. 3 Computing power network system architecture centered on intelligent scheduling

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图 4 需求结构模型

Fig. 4 Requirement structure model

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图 5 三阶段闭环调度模式

Fig. 5 Three-stage closed-loop scheduling model

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图 6 智慧调度架构

Fig. 6 Intelligent scheduling architecture

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图 7 算力网络调度优化模型

Fig. 7 Scheduling optimization model of computing power network

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图 8 调度算法时间与质量关系

Fig. 8 The relationship between time and quality of scheduling algorithm

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表 1 算力网络与云边端计算比较

Table 1 Comparison between computing power network and cloud-edge-terminal computing

	云边端计算	算力网络
优点	大规模计算资源, 大规模存储设施, 计算能力强, 可扩展计算资源静态、聚集、专业, 技术相对成熟	网、云、数、智、安、边、端、链深度融合, 计算性能好, 可扩展计算资源动态、分散、易用, 去中心化, 延迟低, 适合实时任务, 安全性和隐私性强
缺点	延迟较高, 不适合实时性高的任务, 安全性与隐私性差	技术不成熟
适用性	计算密集型、存储密集型任务	分布式一体化算网服务

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表 2 启发式和元启发式调度算法类型

Table 2 Types of heuristic scheduling algorithm and meta-heuristic scheduling algorithm

算法类型	分类	典型文献
启发式调度算法	构造型启发式	[106]−[107]
启发式调度算法	复合型启发式	[108]−[109]
元启发式调度算法	轨迹型元启发式	[110]−[111]
元启发式调度算法	种群型元启发式	[112]−[113]

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表 3 算力网络调度算法发表情况分析

Table 3 Analysis of the publication situation of computing power network scheduling algorithms

	2018 年	2019 年	2020 年	2021 年	2022 年
期刊论文数量	0	1	1	2	0
会议论文数量	1	0	0	2	5

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表 4 算力网络三个演化阶段的四个维度特点

Table 4 Four-dimensional characteristics of three evolutionary stages of computing power network

	资源	编排	运营	服务
泛在协同	网随算动	协同编排	协同运营	一站服务
融合统一	算网融合	智能编排	统一运营	融合服务
一体内生	算网一体	智慧内生	模式创新	一体服务

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表 5 算力网络综述对比

Table 5 Comparison of computing power network surveies

	文献[136]	文献[137]	本文
角度	5G通信	5G通信; 移动互联网	算力调度
关注重点	边缘节点计算资源限制; 业务需求与网络解耦; 静态服务器与移动客户端限制	泛在计算资源协同; 用户网络服务体验	广分布异构性导致可行解空间巨大; 强不确定性导致可行解空间易变; 高约束复杂性导致可行解孤岛繁多; 多目标性导致冲突目标权衡优化难
主要内容	提出基于分布式系统的计算网络融合架构; 协同共享多个边缘节点计算资源; 实现大量请求的处理和负载均衡; 考虑网络条件和可用计算资源的边缘节点交互; 提供基于网络的负载均衡服务分配方法	提出基于IETF的算力网络基本架构; 阐述算力网络的工作机制; 介绍计算任务调度等关键技术	提出多层次的算力网络体系架构; 提出算力网络管理机制; 提出算力网络智慧调度框架; 分析算力网络智慧调度问题性质
创新点	提出基于分布式系统的计算网络融合架构	介绍算力网络的基本架构与工作流程	提出参数化结构化业务管理模型; 提出算力网络调度优化模型; 提出算力网络智慧调度体系架构

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