Latency‐minimum offloading decision and resource allocation for fog‐enabled Internet of Things networks

Abstract

The rapid growth of the number of sensing devices enables computation offloading to be a promising solution to alleviate the burden of core network communication and provide low delay services, especially for those computation-intensive and delay-sensitive tasks. For meeting the processing requirements of these tasks sufficiently, a latency-minimum offloading decision and resource allocation scheme for fog-enabled Internet of Things (IoT) networks is developed in this article. Specifically, we formulate a joint optimization problem of the offloading decision, the local computation capability, and the computing resource allocation of fog node to minimize the task completion time with energy constraint, in which practically considers M/M/1 waiting queues in the wireless channel and fog node. To solve this mixed integer nonlinear programming problem with low complexity, we first calculate the optimal values of local computation capability and computing resource allocation of fog node by decomposing the original optimization problem into two independent subproblems. Subsequently, we propose a hybrid genetic simulated annealing-based latency-minimum offloading decision algorithm to optimize the offloading decision. Finally, the numerical results verify that our proposed scheme achieves significant advantages compared to other alternative schemes in terms of completion time and energy consumption, and they also confirm the advantage of convergence speed and quality.

Efficient Task Offloading for IoT-Based Applications in Fog Computing Using Ant Colony Optimization

“…The study in [26] investigates the edge server placement problem, where PSO based on a multi-objective function is used to reduce the total energy consumption and maintain an acceptable access delay. In [7], a hybrid genetic-simulated annealing latency-minimum offloading decision algorithm in IoT-fog computing is designed to find the best offloading decision with minimum latency. Also, in [27], a genetic algorithm is proposed for the offloading decisions in IoT-fog computing.…”

“…The IoT-fog network architecture used in this paper consists of three layers, the IoT layer, the fog layer, and the cloud layer, as shown in Figure 1. The same architecture is used in [7], [27]. In the bottom layer, the IoT layer, a number of geographically distributed sensor nodes are connected using a local network.…”

“…Therefore, it is challenging to use the traditional greedy search methods. We propose two evolutionary meta-heuristics algorithms, using the ant colony optimization and particle swarm optimization, to over come this challenge [7]. This section presents the proposed evolutionary algorithms, namely ACO and PSO, for handling the optimization problem of this research.…”

“…However, this problem is NP-hard problem where the difficulty increases exponentially as the number of sensors and fog nodes increases. Therefore, it is challenging to use the traditional greedy search methods [7]. To overcome this problem and to achieve the goals of this research, two evolutionary meta-heuristic task offloading scheduler, namely, ant colony optimization (ACO) and particle swarm optimization (PSO), are proposed.…”

Proactive Forensics in IoT: Privacy-Aware Log-Preservation Architecture in Fog-Enabled-Cloud Using Holochain and Containerization Technologies

“…Therefore, fog node auxiliary offloads the task complexity to the cloud to attain supplementary computing resources. This mutually enhances the computing and communication resources in fog edge and cloud nodes [26,27].…”