A Concurrent Computing Model for Fog Assisted Edge Network Applications

Abstract

The Fog Computing paradigm is designed to improve the feasibility of service provisioning in the local network applications. This paradigm is proficient in granting services, computations, storage, and communication features for heterogeneous devices. By this consideration, this paper discusses a novel proposal of proliferating computing model (PCM) for improving the robustness in storage level processing. The proposed model makes use of deep learning techniques for improving the concurrency in storage level processing for data storage and access. The learning classifies the functions of requesting and responding devices to improve the rate of data handling along with latency-less processing. This helps to improve the rate of processing by reducing the time along with response rate and less overhead.