ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing

doi:10.1109/TCC.2025.3555231

	ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing
	Zou, Wenhao 1,2; Zhang, Zongshuai 1,2,3; Wang, Nina 1,2,3; Tian, Yu 1,2; Tian, Lin 1,2,3
	2025-04-01
发表期刊	IEEE TRANSACTIONS ON CLOUD COMPUTING
ISSN	2168-7161
卷号	13 期号:2 页码:641-654
摘要	With the increasing number of devices, the demand for data computation is growing rapidly. In edge-cloud collaborative computing, tasks can be scheduled to servers as interdependent subtasks, enhancing performance through parallel computing. A task is executed in an executor, which must first initialize the runtime environment in a process called task startup. However, most existing research neglects the reuse of executors, leading to considerable delays during task startup. To address this issue, we model the edge-cloud collaborative task scheduling scenario considering executor reuse, task startup, and dependency relationships. We then formulate the dependent task scheduling problem with task startup. To meet real-time demands in edge-cloud collaborative computing, we propose ReflexPilot, an online task scheduling architecture featuring executor management. Building on this architecture, we introduce OTSA-PPO, a task scheduling algorithm based on Proximal Policy Optimization (PPO), and EMA, an advanced executor allocation algorithm. Under constraints of computational and communication resources, ReflexPilot leverages OTSA-PPO for online scheduling of dependent tasks based on current states, while EMA pre-creates and reuses executors to reduce the average task completion time. Extensive simulations demonstrate that ReflexPilot significantly reduces the average task completion time by 31% to 71% compared with existing baselines.
关键词	Servers Optimal scheduling Collaboration Cloud computing Scheduling algorithms Containers Computer architecture Computational modeling Resource management Training Edge-cloud collaborative computing deep reinforcement learning dependent task scheduling startup latency
DOI	10.1109/TCC.2025.3555231
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[62120106007] ; Pilot for Major Scientific Research Facility of Jiangsu Province of China[BM2021800]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering ; Computer Science, Theory & Methods
WOS记录号	WOS:001504051800007
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/42350
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhang, Zongshuai
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Nanjing Inst InforSuperBahn, Nanjing 211100, Peoples R China
推荐引用方式 GB/T 7714	Zou, Wenhao,Zhang, Zongshuai,Wang, Nina,et al. ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing[J]. IEEE TRANSACTIONS ON CLOUD COMPUTING,2025,13(2):641-654.
APA	Zou, Wenhao,Zhang, Zongshuai,Wang, Nina,Tian, Yu,&Tian, Lin.(2025).ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing.IEEE TRANSACTIONS ON CLOUD COMPUTING,13(2),641-654.
MLA	Zou, Wenhao,et al."ReflexPilot: Startup-Aware Dependent Task Scheduling Based on Deep Reinforcement Learning for Edge-Cloud Collaborative Computing".IEEE TRANSACTIONS ON CLOUD COMPUTING 13.2(2025):641-654.