Dynamic Task Placement for Deadline-Aware IoT Applications in Federated Fog Networks

Abstract

In the era of the Internet of Things (IoT), fog computing has become an enticing concept for supporting delay-sensitive tasks by offering versatile and convenient computing and communication services to the end users, in conjunction with cloud services. Most of the existing research mainly draws attention to the communication delay minimization and completion time reduction in the hierarchical fog networks without giving the priority to select the suitable computing device during failure or resource unavailability of the current computing devices. By motivating the above-mentioned challenges, in this article, we propose a deadline-aware dynamic task placement (DDTP) strategy to offload and place the tasks to a suitable computing device in fog networks. In this context, we design a new federated fog framework consisting of several fog clusters in which the cluster head, termed as master fog node, acts as a fog controller that controls and manages the data distribution among the other fog nodes, termed as slave fog nodes. The proposed DDTP strategy selects the suitable computing device for each incoming task as per the deadline and ensures to meet the deadline constraints of the tasks using a dynamic task allocation policy. Finally, a dispatch-constrained offloading policy is developed to reassign the failed tasks to the available fog nodes in the network. Comprehensive simulation results depict the efficiency of the proposed strategy over the existing baseline algorithms in terms of various performance matrices.