使用QSC-Net与基于MF-MBO的能源意识任务调度,确保量子弹性智能城市通信网络的安全.
Nalavala Ramanjaneya Reddy1, G Arul Dalton2, K Swathi3
1Department of CSE, RGM College of Engineering and Technology (Autonomous), Nandyal, A.P, 518501, India. nalavala.ramanji@gmail.com.
Scientific reports
|March 7, 2026
概括
一个新的多策略模糊增强的君主蝶优化 (MF-MBO) 改进了智能城市和边缘计算的任务管理. 与传统方法相比,这种可适应的优化框架提高了效率,负载平衡和能源消耗.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 现代任务管理对于智能城市和边缘云计算中的时间变化系统至关重要.
- 现有的优化技术如遗传算法,粒子群优化和君主蝶优化 (MBO) 在适应性,多目标性能和过早融合方面表现出局限性.
- 虚拟化基础设施在支持异质任务类型和服务质量要求方面存在挑战.
研究的目的:
- 引入一种新的混合调度框架,多策略模糊增强的君主蝶优化 (MF-MBO),旨在实现适应性和高效的任务管理.
- 在动态和异质计算环境中解决保守优化技术的局限性.
- 提高分布式系统的运营效率,可扩展性和稳定性.
主要方法:
- 开发了一个混合调度框架 (MF-MBO),集成了多目标排名的模糊主导地位.
- 集成的自适应量子灵感道,以防止停滞和局限的贪迁移,用于本地提炼和负载平衡.
- 动态平衡的探索和开发,以加快融合并确保分布式虚拟机之间的任务公平性.
主要成果:
- 在各种工作负载条件下,MF-MBO在基线算法 (MBO,GA,PSO) 上表现优越.
- 实现了显著的改进:17.4%的任务执行时间,22.8%的负载平衡效率,15.6%的能源消耗.
- 在动态环境中表现出更高的运营效率,可扩展性和稳定性.
结论:
- MF-MBO框架为智能城市服务,分布式边缘计算和物联网应用提供了一个实用和可解释的优化管道.
- 拟议的方法为复杂系统中的可适应优化提供了一种可重复的方法.
- 经验结果和基准被介绍,以促进未来的研究和验证.
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