Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt.
Academy of Scientific Research and Technology (ASRT), 101 Qasr Al Aini St., Cairo PO Box 11516, Cairo, Egypt.
Comput Intell Neurosci. 2021 Dec 24;2021:9114113. doi: 10.1155/2021/9114113. eCollection 2021.
Instead of the cloud, the Internet of things (IoT) activities are offloaded into fog computing to boost the quality of services (QoSs) needed by many applications. However, the availability of continuous computing resources on fog computing servers is one of the restrictions for IoT applications since transmitting the large amount of data generated using IoT devices would create network traffic and cause an increase in computational overhead. Therefore, task scheduling is the main problem that needs to be solved efficiently. This study proposes an energy-aware model using an enhanced arithmetic optimization algorithm (AOA) method called AOAM, which addresses fog computing's job scheduling problem to maximize users' QoSs by maximizing the makespan measure. In the proposed AOAM, we enhanced the conventional AOA searchability using the marine predators algorithm (MPA) search operators to address the diversity of the used solutions and local optimum problems. The proposed AOAM is validated using several parameters, including various clients, data centers, hosts, virtual machines, tasks, and standard evaluation measures, including the energy and makespan. The obtained results are compared with other state-of-the-art methods; it showed that AOAM is promising and solved task scheduling effectively compared with the other comparative methods.
物联网 (IoT) 活动不依赖于云，而是卸载到雾计算中，以提高许多应用程序所需的服务质量 (QoS)。然而，雾计算服务器上连续计算资源的可用性是物联网应用程序的限制之一，因为使用物联网设备生成的大量数据传输会产生网络流量并导致计算开销增加。因此，任务调度是需要有效解决的主要问题。本研究提出了一种使用增强型算术优化算法 (AOA) 方法的节能模型，称为 AOAM，该方法解决了雾计算的作业调度问题，通过最大化截止时间来最大限度地提高用户的 QoS。在提出的 AOAM 中，我们使用海洋捕食者算法 (MPA) 搜索算子增强了常规 AOA 的搜索能力，以解决所使用解决方案的多样性和局部最优问题。使用包括各种客户端、数据中心、主机、虚拟机、任务和标准评估指标（包括能量和截止时间）在内的多个参数对所提出的 AOAM 进行了验证。将获得的结果与其他最先进的方法进行了比较；结果表明，与其他比较方法相比，AOAM 很有前途，并且能够有效地解决任务调度问题。
