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首页> 外文会议>International Conference on Advances in Big Data, Computing and Data Communication Systems >Multi-Objective Optimization Approach for Task Scheduling in Fog Computing
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Multi-Objective Optimization Approach for Task Scheduling in Fog Computing

机译：雾计算中任务调度的多目标优化方法

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摘要

The Fog computing paradigm allow applications to be processed at the edge of a network. This paradigm is designed to mitigate high latency and the burden of task requests sent to centralized cloud servers by end devices. Fog computing permits different portions of applications to be scheduled to Fog nodes available at the edge. These Fog nodes offer cloud processing services and have appeared as a feasible technique for real time applications. However, scheduling a task among available Fog nodes must be effective, meaning it must not over consume available resources because of limited resources at the edge. Consuming extra amount of energy than available on the Fog nodes can lead to network breakdown or application failure which is not acceptable for real-time applications. Therefore, to address this challenge, this paper presents an application scheduling technique based on virtualization technology to find an efficient algorithm that can optimize energy consumption and average delay of real-time applications in Fog computing networks. This is achieved by implementing four task scheduling policies in a Fog node scheduler to assess their performance and efficiency. Simulations were conducted using the iFogSim tool and the results demonstrate that the FCFS scheduling policy achieved improvement in energy consumption by 11 %, average task delay 7.78 %, 4.4 % network usage and execution time 15.1 % better than other algorithms.
机译：雾计算范例允许在网络边缘处理应用程序。此范例旨在缓解高延迟和终端设备发送到集中式云服务器的任务请求的负担。雾计算允许将应用程序的不同部分调度到边缘可用的雾节点。这些Fog节点提供云处理服务，并已成为实时应用程序的可行技术。但是，在可用的Fog节点之间调度任务必须有效，这意味着由于边缘资源有限，该任务一定不能过度消耗可用资源。消耗比Fog节点上可用能量多的能量会导致网络崩溃或应用程序故障，这对于实时应用程序是不可接受的。因此，为解决这一挑战，本文提出了一种基于虚拟化技术的应用程序调度技术，以寻找一种可以优化Fog计算网络中实时应用的能耗和平均延迟的高效算法。这是通过在Fog节点调度程序中实施四个任务调度策略以评估其性能和效率来实现的。使用iFogSim工具进行了仿真，结果表明，与其他算法相比，FCFS调度策略的能耗降低了11％，平均任务延迟提高了7.78％，网络使用率提高了4.4％，执行时间提高了15.1％。

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