Multi-Objective Optimization of Dynamic Resource Scheduling in IoT Cloud Platform

Ran Li, Hailong Zhang, Enguo Zhu, Yi Ren
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Abstract

In the Internet-of-Things (IoT) cloud platform, optimizing resource scheduling is the main way to achieve the maximum benefit of the system. However, the current researches lack an effective solutions to manage the steady and the abnormal state changes of batch tasks as a whole. To solve the problem of cloud resource scheduling for batch tasks under different scenarios and achieve the maximum benefit of the power IoT cloud platform, this paper proposes a Multi-Objective Optimization Model (MOOM) for dynamic resource scheduling. Firstly, we analyze the task execution performance parameters under the steady state, and proposes a performance analysis model based on queuing theory. Based on the analysis model, we can calculate the approximate solution of task performance parameters under a certain configuration. Then, considering different operation scenarios of the power IoT, a dynamic scheduling mechanism for cloud resources is constructed based on the performance parameters, which can guide the cloud platform to determine the optimal resource scheduling scheme under a given scenario. In addition, MOOM also contains the optimization objective of cost minimization, and proposes a method to quantify the cost. Finally, extensive experimental evaluations demonstrate the efficiency and effectiveness of our proposed model.
物联网云平台中动态资源调度的多目标优化
在物联网云平台中,优化资源调度是实现系统效益最大化的主要途径。然而,目前的研究缺乏对批处理任务的稳态和异常状态变化进行整体管理的有效解决方案。为了解决不同场景下批量任务的云资源调度问题,实现电力物联网云平台效益最大化,本文提出了一种动态资源调度的多目标优化模型(MOOM)。首先对稳态下的任务执行性能参数进行了分析,提出了基于排队论的任务执行性能分析模型。基于分析模型,我们可以计算出任务性能参数在一定配置下的近似解。然后,考虑电力物联网的不同运行场景,构建基于性能参数的云资源动态调度机制,指导云平台在给定场景下确定最优的资源调度方案。此外,MOOM还包含了成本最小化的优化目标,并提出了一种量化成本的方法。最后,大量的实验评估证明了我们提出的模型的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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