Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
University College Dublin, Dublin, Ireland.
PLoS One. 2024 Jun 4;19(6):e0304067. doi: 10.1371/journal.pone.0304067. eCollection 2024.
Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse and independent environment like Fog-Edge, resource management is a critical issue. Hence, scheduling is a vital process to enhance efficiency and allocation of resources properly to the tasks. The manuscript proposes an Artificial Neural Network (ANN) inspired Antlion algorithm for task orchestration Edge environments. Its aim is to enhance resource utilization and reduce energy consumption. Comparative analysis with different algorithms shows that the proposed algorithm balances the load on the Edge layer, which results in lower load on the cloud, improves power consumption, CPU utilization, network utilization, and reduces average waiting time for requests. The proposed model is tested for healthcare application in Edge computing environment. The evaluation shows that the proposed algorithm outperforms existing fuzzy logic algorithms. The performance of the ANN inspired Antlion based orchestration approach is evaluated using performance metrics, power consumption, CPU utilization, network utilization, and average waiting time for requests respectively. It outperforms the existing fuzzy logic, round robin algorithm. The proposed technique achieves an average cloud energy consumption improvement of 95.94%, and average Edge energy consumption improvement of 16.79%, 19.85% in average CPU utilization in Edge computing environment, 10.64% in average CPU utilization in cloud environment, and 23.33% in average network utilization, and the average waiting time decreases by 96% compared to fuzzy logic and 1.4% compared to round-robin respectively.
边缘计算是一种可扩展的、现代的分布式计算架构，它将计算工作负载更接近智能网关或边缘设备。这种计算模型提供物联网 (IoT) 计算，并处理来自网络边缘的 IoT 请求。在像雾-边缘这样多样化和独立的环境中，资源管理是一个关键问题。因此，调度是提高效率和正确分配资源的重要过程。本文提出了一种受人工神经网络 (ANN) 启发的蚁狮算法，用于任务编排边缘环境。其目的是提高资源利用率并降低能耗。与不同算法的比较分析表明，所提出的算法平衡了边缘层的负载，从而降低了云的负载，提高了功率消耗、CPU 利用率、网络利用率，并减少了请求的平均等待时间。该模型在边缘计算环境中的医疗保健应用中进行了测试。评估表明，所提出的算法优于现有的模糊逻辑算法。使用性能指标、功率消耗、CPU 利用率、网络利用率和请求的平均等待时间分别评估基于 ANN 启发的蚁狮编排方法的性能。它优于现有的模糊逻辑、轮询算法。所提出的技术在平均云能耗提高了 95.94%，平均边缘能耗提高了 16.79%，在边缘计算环境中的平均 CPU 利用率提高了 19.85%，在云环境中的平均 CPU 利用率提高了 10.64%，平均网络利用率提高了 23.33%，与模糊逻辑相比，平均等待时间减少了 96%，与轮询相比减少了 1.4%。
