An enhanced hyper-angle exploitative searching algorithm for fog computing closed loop

Abstract

Internet of Things (IoT) emergence in the revolution of technology has led to rising of a variety of smart devices with several characteristics. The requirements for those devices are high computational structure and real-time response. Traditional Cloud Computing (CC) is connected to IoT devices to provide the demands of services. However, cloud computing lacks the transmission of data because of its infrastructure and limitations of networks which decrease the performance of CC. Consequently, a paradigm was proposed to be a middle layer between the cloud and IoT termed Fog Computing (FC). As an extension to the cloud, the FC provides computing service at the edge of the network and the location enables the capability of this technology to deal with numerous data locally. Despite, the effectiveness of FC technology and based on the literature there is the main issue were addressed by the researchers is Task Allocation problems, which are holding several challenges in Fog Computing such as Execution Time, Energy Consumption, Energy Distribution, Renting Rate, and Reliability Rate. Therefore, in this study, we have proposed a Fog computing model named Hyper-Angel Exploitive Searching (HAES) based Fog Computing Closed Loop (FCCL). This model is named HAES-FCCL. The FCCL is based on the enhanced searching algorithm HAES that can improve the performance of FC. Hence, this study aims to achieve an efficient FC model through the following parameters: energy consumption, execution time, renting rate, energy distribution, and reliability rate. These parameters are evaluated using mathematical models via MATLAB 2019b. The results proved the superiority of the proposed HAES-FCCL compare to the standard benchmark models by 59.3% for energy consumption, 70% for execution time, 69.1% for renting rate, 67% for energy distribution, and 33.5% for reliability rate.