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FPFTS: A joint fuzzy particle swarm optimization mobility-aware approach to fog task scheduling algorithm for Internet of Things devices
Software: Practice and Experience ( IF 2.6 ) Pub Date : 2020-08-18 , DOI: 10.1002/spe.2867
Saeed Javanmardi 1 , Mohammad Shojafar 2 , Valerio Persico 1 , Antonio Pescapè 1
Affiliation  

In the Internet of Things (IoT) scenario, the integration with cloud-based solutions is of the utmost importance to address the shortcomings resulting from resource-constrained things that may fall short in terms of processing, storing, and networking capabilities. Fog computing represents a more recent paradigm that leverages the wide-spread geographical distribution of the computing resources and extends the cloud computing paradigm to the edge of the network, thus mitigating the issues affecting latency-sensitive applications and enabling a new breed of applications and services. In this context, efficient and effective resource management is critical, also considering the resource limitations of local fog nodes with respect to centralized clouds. In this article, we present FPFTS, fog task scheduler that takes advantage of particle swarm optimization and fuzzy theory, which leverages observations related to application loop delay and network utilization. We evaluate FPFTS using an IoT-based scenario simulated within iFogSim, by varying number of moving users, fog-device link bandwidth, and latency. Experimental results report that FPFTS compared with first-come first-served (respectively, delay-priority) allows to decrease delay-tolerant application loop delay by 85.79% (respectively, 86.36%), delay sensitive application loop delay by 87.11% (respectively, 86.61%), and network utilization by 80.37% (respectively, 82.09%), on average.

中文翻译:

FPFTS:物联网设备雾任务调度算法的联合模糊粒子群优化移动性感知方法

在物联网 (IoT) 场景中,与基于云的解决方案的集成对于解决资源受限的事物在处理、存储和网络能力方面可能不足的缺点至关重要。雾计算代表了一种更新的范式,它利用计算资源的广泛地理分布,并将云计算范式扩展到网络边缘,从而减轻影响延迟敏感应用程序的问题,并支持新型应用程序和服务. 在这种情况下,高效和有效的资源管理至关重要,同时还要考虑本地雾节点相对于集中式云的资源限制。在本文中,我们介绍 FPFTS,雾任务调度器利用粒子群优化和模糊理论,利用与应用程序循环延迟和网络利用率相关的观察结果。我们使用 iFogSim 中模拟的基于物联网的场景评估 FPFTS,通过不同数量的移动用户、雾设备链接带宽和延迟。实验结果报告说,FPFTS 与先来先服务(分别为延迟优先)相比,允许将延迟容忍应用程序循环延迟降低 85.79%(分别为 86.36%),延迟敏感应用程序循环延迟降低 87.11%(分别为, 86.61%),平均网络利用率为 80.37%(分别为 82.09%)。通过不同数量的移动用户、雾设备链接带宽和延迟。实验结果报告说,FPFTS 与先来先服务(分别为延迟优先)相比,允许将延迟容忍应用程序循环延迟降低 85.79%(分别为 86.36%),延迟敏感应用程序循环延迟降低 87.11%(分别为, 86.61%),平均网络利用率为 80.37%(分别为 82.09%)。通过不同数量的移动用户、雾设备链接带宽和延迟。实验结果报告说,FPFTS 与先来先服务(分别为延迟优先)相比,允许将延迟容忍应用程序循环延迟降低 85.79%(分别为 86.36%),延迟敏感应用程序循环延迟降低 87.11%(分别为, 86.61%),平均网络利用率为 80.37%(分别为 82.09%)。
更新日期:2020-08-18
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