Abstract
The current thinking concerning computations required by Internet of Things (IoT) applications is shifting toward fog computing instead of cloud computing, thereby achieving most of the required computations at the network edge of the IoT devices. Fog computing can thus improve the quality of service of delay-sensitive applications by allowing such applications to take advantage of the low latency provided by fog computing rather than the high latency of the cloud. Therefore, tasks in various IoT applications must be effectively distributed over the fog nodes to improve the quality of service, specifically the task response time. In this paper, two nature-inspired meta-heuristic schedulers, namely ant colony optimization (ACO) and particle swarm optimization (PSO), are used to propose two different scheduling algorithms to effectively load balance IoT tasks over the fog nodes under communication cost and response time considerations. The experimental results of the proposed algorithms are compared with those of the round robin (RR) algorithm. The evaluations show that the proposed ACO-based scheduler achieves an improvement in the response times of IoT applications compared to the proposed PSO-based and RR algorithms and effectively load balances the fog nodes.
Highlights
The Internet of Things (IoT) will dominate urban cities and transform overcrowded cities into smart cities
We investigate the problem of IoT task offloading to the fog with the main goal of reducing the IoT-based application response time by load balancing the tasks on the fog nodes while considering the communication cost, computation time and the existing load conditions on the fog nodes
Algorithm 1 shows the proposed meta-heuristic algorithm using ant colony optimization (ACO) to find an efficient task offloading for the IoT sensors on the available fog nodes that guarantees that the defined quality of service (QoS) constraints, the response time, are satisfied while considering the network characteristics, the service time and the current load on the fog nodes
Summary
The Internet of Things (IoT) will dominate urban cities and transform overcrowded cities into smart cities. IoT task offloading to fog computing instead of the cloud avoids high network congestion and reduces the data analysis response time by taking advantage of low network latency. This concept is called edge computing because it provides computing capabilities, including better response times and low latencies, at the edge of the network near the IoT devices. The experimental evaluation results show that the proposed ACO-based scheduler achieves an improvement compared to the proposed PSO-based scheduler and the round robin (RR) algorithm in terms of the IoT application response time and effective use of fog nodes.
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