摘要
容器技术作为轻量级虚拟化方案,已成为多云网络架构的核心支撑技术,但跨云通信的高昂成本仍是关键挑战。创新点在于将多云网络资源调度问题建模为二次分配问题(QAP),并提出一种改进的离散海洋捕食算法(DMPA)。DMPA通过以下策略显著提升性能:基于均匀分布与伪反向学习的混合种群初始化策略,避免初始解陷入局部最优;引入自适应收敛因子动态调整搜索范围,平衡全局探索与局部开发;采用可变交换间隔的2-exchange突变策略,增强种群多样性;结合禁忌搜索优化精英解,避免重复搜索。实验表明,DMPA在30个QAP实例中22个达到已知最优解,平均偏差率低于3%;在多云网络真实数据集下,优化效果明显,通信成本有所降低。相比MPA、HPSO等算法,DMPA在优化精度与稳定性上均表现突出,为多云网络成本优化提供了高效解决方案。
Container technology,as a lightweight virtualization solution,remains a core supporting technology for multi-cloud network architectures.However,the high cost of cross-cloud communication remains a critical challenge.The key innovation of models of the resource scheduling problem in multi-cloud networks as a quadratic assignment problem(QAP)and an improved discrete marine predators algorithm(DMPA)was proposed.The DMPA was significantly enhanced performance through the following strategies:a hybrid population initialization strategy based on uniform distribution and pseudo-reverse learning,which avoided the initial solution from falling into local optima;an adaptive convergence factor was introduced to dynamically adjust the search range,balancing global exploration and local exploitation;a 2-exchange mutation strategy with variable exchange intervals was employed to enhance population diversity;the integration of tabu search to optimize elite solutions and avoid repetitive searches.Experiments show that DMPA achieves known optimal solutions in 22 out of 30 QAP instances,with an average deviation rate of less than 3%.Under real-world multi-cloud network datasets,the optimization effect is significant,and communication costs are reduced.Compared to algorithms such as MPA and HPSO,DMPA demonstrates outstanding performance in both optimization accuracy and stability,providing an efficient solution for cost optimization in multi-cloud networks.
作者
高明
沈艺程
刘铭
GAO Ming;SHEN Yicheng;LIU Ming(College of Information and Electronic Engineering,Zhejiang Gongshang University,Hangzhou 310018,China;School of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《电信科学》
北大核心
2025年第8期86-100,共15页
Telecommunications Science
基金
国家自然科学基金资助项目(No.61871468)
浙江省新型网络标准与应用技术重点实验室基金资助项目(No.2013E10012)
浙江省基础公益研究计划项目(No.LGG20F010015)。
关键词
多云网络
云原生
二次分配
群智能优化算法
multi-cloud network
cloud native
secondary distribution
swarm intelligence optimization algorithm
