Resource-incentivized-based asynchronous task scheduling for cross-regional cloud-edge collaboration

CHENG Guanjie; WANG Ruihao; CHEN Yishan; ZHAO Zhouxing; ZHAO Xinkui; DING Zhiyang

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Resource-incentivized-based asynchronous task scheduling for cross-regional cloud-edge collaboration

更新时间：2026-05-11

- Resource-incentivized-based asynchronous task scheduling for cross-regional cloud-edge collaboration
- Telecommunications Science (2026)
- 作者机构：
  
  1.浙江大学软件学院，浙江宁波 315000
  2.江西理工大学信息工程学院，江西赣州 341000
  3.浙江省通信产业服务有限公司，浙江杭州 310000
  4.西北工业大学教育实验学院，陕西西安 710129
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62502446)
- DOI：
  CLC： TP393
- Received：04 January 2026，
  
  Revised：2026-02-23，
  
  Accepted：20 March 2026，
- 稿件说明：
移动端阅览
CHENG Guanjie, WANG Ruihao, CHEN Yishan, et al. Resource-incentivized-based asynchronous task scheduling for cross-regional cloud-edge collaboration[J/OL]. Telecommunications Science, 2026.
DOI：

CHENG Guanjie, WANG Ruihao, CHEN Yishan, et al. Resource-incentivized-based asynchronous task scheduling for cross-regional cloud-edge collaboration[J/OL]. Telecommunications Science, 2026. DOI：

摘要

针对跨区域云-边多数据中心系统（multi-data center system

MDCS）中节点异构、流量突发及网络时延不确定导致的协同难题，提出了一种基于资源激励型Stackelberg博弈（resource incentive Stackelberg game

RISG）的异步任务调度方法。首先，利用M/M/1排队理论刻画异构节点在高并发下的非线性拥塞效应，构建融合能耗、时延、传输开销及可靠性风险的综合成本模型，并推导出数据中心最优响应的闭式解。其次，设计了基于梯度的异步坐标下降（asynchronous coordinate descent

ACD）算法，支持全局调度器利用陈旧信息进行非阻塞式策略更新，克服了同步等待的低效性，并证明了算法的收敛性与均衡的存在唯一性。仿真结果表明，ACD算法收敛速度较传统异步梯度投影算法提升约26.9%；在系统重载场景下，平均响应时延较贪婪策略降低约45.17%；在强网络抖动环境下，社会总成本波动误差控制在8%以内，验证了该方法的高吞吐量与强鲁棒性。

Abstract

To address the collaborative challenges caused by node heterogeneity

traffic bursts

and network latency uncertainty in cross-regional cloud-edge multi-data center systems (MDCS)

this paper proposes an asynchronous task scheduling method based on a Resource Incentive Stackelberg Game (RISG). First

M/M/1 queuing theory is utilized to characterize the non-linear congestion effects of heterogeneous nodes under high concurrency

a comprehensive cost model integrating energy consumption

latency

transmission overhead

and reliability risk is constructed

and a closed-form solution for the optimal response of data centers is derived. Second

a gradient-based Asynchronous Coordinate Descent (ACD) algorithm is designed

which supports the global scheduler in performing non-blocking policy updates using stale information

overcoming the inefficiency of synchronous waiting

and the convergence of the algorithm as well as the existence and uniqueness of the equilibrium are proven. Simulation results show that the convergence speed of the ACD algorithm is improved by approximately 26.9% compared to the traditional asynchronous gradient projection algorithm; in heavy-load system scenarios

the average response latency is reduced by approximately 45.17% compared to greedy strategies; in environments with strong network jitter

the total social cost fluctuation error is controlled within 8%

verifying the high throughput and strong robustness of this method.

关键词

Keywords

references

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