REDUCED WORKLOAD ALLOCATION RESPONSE TIME INEDGE-CLOUD ENVIRONMENT USING ARTIFICIALINTELLIGENCE

Abstract

Edge-cloud computing paradigms increase the Quality of Experience (QoE) for real-time applications by offering many benefits, such as reducing the response time. Many strategies are proposed to handle the data in the edge cloud environment. Hence, where to execute the data generated by the end device is considered an important issue. In this paper, an Artificial Intelligence (AI) model is proposed based on a neural network for workload allocation decisions. The model deals with an AI application that runs on an edge server and a cloud center. The model was trained using a pre-generated dataset based on several features. The features considered are the data size, model complexity, application priority, edge server utilization, and delay of execution on the edge and the cloud. The model decides where to perform the task generated by the end device, either in the edge server or on the cloud. Four AI applications are considered. The model has been implemented using Tensorflow platform with the required libraries. The proposed model employee multi-feature with multi-application addressing workload allocation decisions in a hybrid edge-cloud environment by creating and utilizing a dataset based on proposed algorithm. The proposed model achieved accuracy reached 98.3% and reduced the response time of task execution compared to the based line approach considered in this paper.