SummaryThe rapid expansion of IoT systems has caused network congestion and delays in task placement and resource provisioning as usually the tasks are executed at a far location in the cloud. Fog computing reduces the computing burden of cloud data centers as well as the communication burden of the internet as fog resources are placed near the data generation points. Within Fog computing, an important challenge is the optimal task placement which is an NP‐class problem. This work applies machine learning for task clustering and addresses the task placement problem in a fog computing environment using a hybrid of two recent metaheuristics; Jaya and gray wolf optimization (GWO). The hybrid method considers optimizing the total number of active fog nodes, load balancing in fog nodes, and average response time of the tasks. The performance of the proposed method is evaluated on a real‐time LCG dataset and is compared with reinforcement learning fog scheduling (RLFS), genetic algorithm (GA), dynamic resource allocation mechanism (DRAM), load balancing and scheduling algorithm (LBSSA), and particle swarm optimization with simulated annealing (PSO‐SA) algorithms. The results demonstrate the superiority of the suggested method over the baseline techniques in terms of average improvement of 51.04% in load balance variance, 30.25% in average response time, 24.16% in execution time, and 47.10% in the number of devices used.

中文翻译：

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在雾云生态系统中使用混合 Jaya-灰狼优化的基于 ML 的任务聚类和放置

摘要物联网系统的快速扩展导致了网络拥塞以及任务放置和资源配置的延迟，因为任务通常在云中的较远位置执行。由于雾资源放置在数据生成点附近，雾计算减轻了云数据中心的计算负担以及互联网的通信负担。在雾计算中，一个重要的挑战是最优任务放置，这是一个 NP 类问题。这项工作将机器学习应用于任务集群，并使用两种最近的元启发法的混合来解决雾计算环境中的任务放置问题； Jaya 和灰狼优化 (GWO)。混合方法考虑优化活动雾节点总数、雾节点负载均衡以及任务平均响应时间。该方法的性能在实时 LCG 数据集上进行了评估，并与强化学习雾调度（RLFS）、遗传算法（GA）、动态资源分配机制（DRAM）、负载平衡和调度算法（LBSSA）进行了比较，以及使用模拟退火（PSO-SA）算法的粒子群优化。结果表明，所提出的方法相对于基线技术的优越性在于，负载平衡方差平均提高了 51.04%，平均响应时间提高了 30.25%，执行时间提高了 24.16%，使用的设备数量提高了 47.10%。