Research on Cloud Resource Scheduling Based on Hierarchical Game Theory and Hybrid Parameterized Control

doi:10.11959/j.issn.1000-0801.DXKX260102

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Research on Cloud Resource Scheduling Based on Hierarchical Game Theory and Hybrid Parameterized Control

更新时间：2026-05-11

- Research on Cloud Resource Scheduling Based on Hierarchical Game Theory and Hybrid Parameterized Control
- Telecommunications Science (2026)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260102
  CLC： TP393
- Received：10 February 2026，
  
  Revised：2026-04-08，
  
  Accepted：11 May 2026，
- 稿件说明：
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Research on Cloud Resource Scheduling Based on Hierarchical Game Theory and Hybrid Parameterized Control[J/OL]. Telecommunications Science, 2026.
DOI：

Research on Cloud Resource Scheduling Based on Hierarchical Game Theory and Hybrid Parameterized Control[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260102.

摘要

面对云计算环境的大规模并发性、高度动态性以及多目标冲突（如完工时间、能耗与资源利用率之间的权衡）的严峻挑战，传统的启发式算法难以适应负载潮汐，而单智能体深度强化学习（DRL）在处理大规模状态空间时往往陷入“维度灾难”。本文提出了一种创新HGT-MARL-CS-PSO框架，旨在通过分层博弈机制解决云资源调度的复杂性。首先，本文将调度问题形式化为一个两级斯塔克伯格博弈模型：高层管理智能体作为领导者，根据全局负载特征制定宏观战略目标；低层执行智能体作为跟随者，在战略约束下进行非合作纳什博弈，以平衡局部资源竞争与全局协作。其次，为了克服离散动作空间爆炸的问题，本文设计了一种混合参数化控制机制，智能体并不直接输出任务映射，而是输出连续的权重参数向量，动态驱动底层的布谷鸟搜索-粒子群调度器进行微观寻优。理论分析证明了该框架在斯塔克伯格均衡点的收敛性。Google Borg Trace真实数据集的大规模实验表明，HGT-MARL-CS-PSO在完工时间、能耗效率及资源利用率等关键指标上显著优于现有的DRL基线及主流元启发式算法，展现了卓越的泛化能力与鲁棒性。

Abstract

Cloud resource scheduling in large-scale data centers faces severe challenges characterized by high concurrency

dynamic workload volatility

and conflicting optimization objectives (e.g.

tradeoffs between makespan

energy consumption

and resource utilization). Traditional heuristics often lack adaptability

while single-agent Deep Reinforcement Learning (DRL) approaches suffer from the "curse of dimensionality" when scaling to large state spaces. To address these limitations

this paper proposes HGT-MARL-CS-PSO

a novel framework that orchestrates scheduling through a hierarchical game-theoretic approach. First

we formulate the scheduling problem as a Two-Level Stackelberg Game: a high-level Manager Agent (M-Agent) acts as the Leader

defining global strategic goals based on macro-workload patterns

while low-level Executor Agents (E-Agents) act as Followers

engaging in a non-cooperative Nash game to balance local resource competition with global cooperation. Second

to mitigate the explosion of discrete action spaces

we introduce a Hybrid Parameterized Control Mechanism. Instead of generating task mappings directly

the RL agents output continuous weight parameter vectors to dynamically configure a subordinate Cuckoo Search-Particle Swarm Optimization (CS-PSO) scheduler for precise micro-execution. Theoretical analysis validates the convergence of the proposed framework towards a Stackelberg Equilibrium. Extensive experiments using real-world Google Borg Traces demonstrate that HGT-MARL-CS-PSO significantly outperforms state-of-the-art DRL baselines and meta-heuristics in terms of makespan reduction

energy efficiency

and resource utilization

proving its superior generalization and robustness in complex environments.

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Keywords

references

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