在移动云下一代网络 (NGN) 中智能资源配置,与上下文数据和机器学习进行编排,以优化微服务应用程序的成本
Mahmood Ul Hassan1, Amin A Al-Awady1, Abid Ali2,3
1Department of Computer Skills, Deanship of Preparatory Year, Najran University, Najran 66241, Saudi Arabia.
Sensors (Basel, Switzerland)
|February 10, 2024
概括
本研究介绍了移动云计算 (MCC) 的一个高效的上下文意识的服务卸载方法. 拟议的MCVS调度算法优化了任务卸载,降低了移动应用程序的成本和能源消耗.
科学领域:
- 计算机科学 计算机科学
- 分布式计算 (Distributed Computing) 是一种分布式计算.
- 人工智能的人工智能
背景情况:
- 移动云计算 (MCC) 通过提供资源,使智能移动应用程序成为可能.
- 在MCC中的任务安排对于上下文意识,资源密集型应用程序至关重要.
- 现有的任务卸载方法与NP难题作斗争,缺乏以实例为中心的估计,限制了服务可靠性.
研究的目的:
- 为MCC.设计一个高效的,具有上下文意识的服务卸载方法.
- 解决传统任务卸载在解决NP-hard调度问题的局限性.
- 改善MCC中的服务可靠性和资源估计.
主要方法:
- 使用以实例为中心的测量开发了一种高效的上下文意识的服务卸载方法.
- 采用了经过修订的机器学习模型,用于卸载决策的任务适应.
- 提出了MCVS调度算法,以预测微服务使用率,考虑设备时间,成本,网络,位置和CPU功率.
主要成果:
- MCVS算法在卸载,CPU使用和执行时间方面表现出效率.
- 在80%的服务到达率下,实现了4.5%的服务卸载率和0.18%的CPU使用率.
- 与现有方法相比,在MCC中展示了微服务卸载的更低的成本和更低的能耗.
结论:
- 拟议的MCVS调度算法有效地优化了MCC中的微服务卸载.
- 这种方法可以节省大量的成本和能源.
- 该方法证明了卓越的培训和任务卸载效率.
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