A performance-aware dynamic scheduling algorithm for cloud-based IoT applications
A performance-aware dynamic scheduling algorithm for cloud-based IoT applications
复制标题
用于基于云的物联网应用的性能感知动态调度算法
DOI：
10.1016/j.comcom.2020.06.016
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发表时间：
2020-06
影响因子：
6
通讯作者：
Ya Guo
中科院分区：
文献类型：
--
作者：
Sanjeevi P.;Samraj Lawrence T.;Sathiyamoorthi V.;Manik;an R.;Qian Xia;Ya Guo
云计算已被用于支持物联网(IoT)数据的存储和处理。对物联网框架的需求越来越大，以提供处理时间更快、延迟更少的服务，以提供延迟敏感型实时应用，如智能家居中的灾难管理。物联网流程主要由调度技术组成，这使得它很难自适应、自配置以响应环境变化的性能。现有的物联网应用调度技术不是基于休眠模式的任务分配，这不可避免地会导致更大的功耗和更长的时延。同意传感器设备并对功能不同的传感器设备应用单独的排队变得越来越重要。提出了一种云中物联网设备动态管理框架(DMFIC)算法，用于评估和调度请求和传感器数据，通过预测向用户发送合适的通知的各种队列，以经济高效的方式协调海量数据和高时间分辨率。最后，通过一个智能家居应用实例验证了该框架的有效性。实验结果表明，与其他物联网服务相比，DMFIC算法的处理时间平均提高了5%，延迟平均降低了0.2%，能够有效地管理云中的传感器数据。
Cloud computing has been employed for supporting storage and handling of Internet of Things (IoT) data. There is an increasing demand for IoT framework to provide services with fast processing time and less delay to offer latency sensitivity real-time applications like disaster management in smart homes. IoT process is mostly comprised of scheduling techniques that makes it hard to self-adapt, self-configure to respond with performance aware on environment changes. Existing scheduling techniques of IoT applications are not based on allocating tasks through sleep modes, which unavoidably lead to more power consumption and longer time delays. Consenting sensor devices and applying separate queueing to a sensor device that varies differently in their capabilities are increasingly significant. In this work, a dynamic management framework for IoT devices in cloud (DMFIC) algorithm is proposed to evaluate and schedule requests and sensor data, which allow coordinating huge data with high time-based resolution in a cost-effectual manner through anticipating various queues for sending an appropriate notification to users. A smart home application was used to demonstrate the proposed framework. The experimental result shows that the DMFIC algorithm gives an average of 5% higher processing time and 0.2% less delay compared to other IoT services and can efficiently manage sensor data in cloud.
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影响因子：
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作者：
J. Petnik;J. Vanus
通讯作者：
J. Petnik;J. Vanus
DOI：
10.1016/j.future.2018.08.040
发表时间：
2019-02
期刊：
Future Gener. Comput. Syst.
影响因子：
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影响因子：
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DOI：
10.1016/j.future.2016.11.011
发表时间：
2018
期刊：
Future Gener. Comput. Syst.
影响因子：
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作者：
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