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Exploring reliable edge-cloud computing for service latency optimization in sustainable cyber-physical systems
Software: Practice and Experience ( IF 3.5 ) Pub Date : 2021-01-12 , DOI: 10.1002/spe.2942
Kun Cao 1 , Tongquan Wei 2 , Mingsong Chen 3 , Keqin Li 4 , Jian Weng 1 , Wuzheng Tan 1
Affiliation  

In recent years, the advance in information technology has promoted a wide span of emerging cyber-physical systems (CPS) applications such as autonomous automobile systems, healthcare monitoring, and process control systems. For these CPS applications, service latency management is extraordinarily important for the sake of providing high quality-of-experience to terminal users. Edge-cloud computing, integrating both edge computing and cloud computing, is regarded as a promising computation paradigm to achieve low service latency for terminal users in CPS. However, existing latency-aware edge-cloud computing methods dedicated for CPS fail to jointly consider energy budgets and reliability requirements, which may greatly degrade the sustainability of CPS applications. In this article, we explore the problem of minimizing service latency of edge-cloud computing coupled CPS under the constraints of energy budgets and reliability requirements. We propose a two-stage approach composed of static and dynamic service latency optimization. At static stage, Monte-Carlo simulation with integer-linear-programming technique is adopted to find the optimal computation offloading mapping and task backup number. At dynamic stage, a backup-adaptive dynamic mechanism is developed to avoid redundant data transmissions and executions for achieving additional energy savings and service latency enhancement. Experimental results show that our solution is able to reduce system service latency by up to 18.3% compared with representative baseline solutions.

中文翻译:

探索可靠的边缘云计算以优化可持续网络物理系统中的服务延迟

近年来,信息技术的进步推动了大量新兴的网络物理系统 (CPS) 应用,例如自动驾驶汽车系统、医疗监控和过程控制系统。对于这些 CPS 应用程序,为了为终端用户提供高质量的体验,服务延迟管理显得格外重要。边缘云计算结合了边缘计算和云计算,被认为是一种很有前途的计算范式,可以在 CPS 中为终端用户实现低服务延迟。然而,现有的专用于 CPS 的延迟感知边缘云计算方法未能共同考虑能源预算和可靠性要求,这可能会大大降低 CPS 应用程序的可持续性。在本文中,我们探讨了在能量预算和可靠性要求的约束下最小化边缘云计算耦合 CPS 的服务延迟问题。我们提出了一种由静态和动态服务延迟优化组成的两阶段方法。在静态阶段,采用整数线性规划技术的蒙特卡罗模拟来寻找最优的计算卸载映射和任务备份数。在动态阶段,开发了一种备份自适应动态机制,以避免冗余数据传输和执行,以实现额外的节能和服务延迟增强。实验结果表明,与代表性基线解决方案相比,我们的解决方案能够将系统服务延迟减少高达 18.3%。我们提出了一种由静态和动态服务延迟优化组成的两阶段方法。在静态阶段,采用整数线性规划技术的蒙特卡罗模拟来寻找最优的计算卸载映射和任务备份数。在动态阶段,开发了一种备份自适应动态机制,以避免冗余数据传输和执行,以实现额外的节能和服务延迟增强。实验结果表明,与代表性基线解决方案相比,我们的解决方案能够将系统服务延迟减少高达 18.3%。我们提出了一种由静态和动态服务延迟优化组成的两阶段方法。在静态阶段,采用整数线性规划技术的蒙特卡罗模拟来寻找最优的计算卸载映射和任务备份数。在动态阶段,开发了一种备份自适应动态机制,以避免冗余数据传输和执行,以实现额外的节能和服务延迟增强。实验结果表明,与代表性基线解决方案相比,我们的解决方案能够将系统服务延迟减少高达 18.3%。在动态阶段,开发了一种备份自适应动态机制,以避免冗余数据传输和执行,以实现额外的节能和服务延迟增强。实验结果表明,与代表性基线解决方案相比,我们的解决方案能够将系统服务延迟减少高达 18.3%。在动态阶段,开发了一种备份自适应动态机制,以避免冗余数据传输和执行,以实现额外的节能和服务延迟增强。实验结果表明,与代表性基线解决方案相比,我们的解决方案能够将系统服务延迟减少高达 18.3%。
更新日期:2021-01-12
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