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首页> 外文期刊>IEEE transactions on industrial informatics >Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications
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Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications

机译：基于IOT的雾计算应用中的任务调度的能量感知海洋捕食者算法

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To improve the quality of service (QoS) needed by several applications areas, the Internet of Things (IoT) tasks are offloaded into the fog computing instead of the cloud. However, the availability of ongoing energy heads for fog computing servers is one of the constraints for IoT applications because transmitting the huge quantity of the data generated using IoT devices will produce network bandwidth overhead and slow down the responsive time of the statements analyzed. In this article, an energy-aware model basis on the marine predators algorithm (MPA) is proposed for tackling the task scheduling in fog computing (TSFC) to improve the QoSs required by users. In addition to the standard MPA, we proposed the other two versions. The first version is called modified MPA (MMPA), which will modify MPA to improve their exploitation capability by using the last updated positions instead of the last best one. The second one will improve MMPA by the ranking strategy based reinitialization and mutation toward the best, in addition to reinitializing, the half population randomly after a predefined number of iterations to get rid of local optima and mutated the last half toward the best-so-far solution. Accordingly, MPA is proposed to solve the continuous one, whereas the TSFC is considered a discrete one, so the normalization and scaling phase will be used to convert the standard MPA into a discrete one. The three versions are proposed with some other metaheuristic algorithms and genetic algorithms based on various performance metrics such as energy consumption, makespan, flow time, and carbon dioxide emission rate. The improved MMPA could outperform all the other algorithms and the other two versions.
机译：为了提高几个应用领域所需的服务质量（QoS），事物互联网（物联网）任务卸载到雾计算而不是云中。然而，雾计算服务器的持续能量头的可用性是IOT应用程序的约束之一，因为传输使用物联网设备生成的大量数据将产生网络带宽开销，并减慢分析的语句的响应时间。在本文中，提出了一种在船舶捕食者算法（MPA）上的能量感知模型基础，用于解决雾计算（TSFC）中的任务调度，以改善用户所需的QoS。除标准MPA外，我们还提出了另外两个版本。第一个版本称为修改的MPa（MMPA），它将通过使用最后更新的位置而不是最后一个最新的位置来修改MPA以提高其开发能力。第二个将通过基于排名策略的Repitialization和突变来改善MMPA，除了重新初始化之外，一半人口在预定义的迭代之后随机摆脱当地最佳，并突破最后一半的迭代朝着最佳突变 - 解决方案。因此，提出了MPA来解决连续的MPA，而TSFC被认为是一个离散的，因此将使用归一化和缩放阶段将标准MPA转换为离散的MPa。基于各种性能度量，诸如能耗，Makespan，流量和二氧化碳排放率的各种性能度量，提出了这三个版本。改进的MMPA可以优于所有其他算法和其他两个版本。

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