A convex problem optimization solution for information management in edge computing platforms

Abstract

AbstractIntelligent computing and social optimization techniques have been used to manage mobile edge (ME) environments for pervasive services. However, the service demand is the prime cause for requiring flawless information management. This article proposes a coherent information management (CIM) process for resolving the convex optimization problems by considering the flaws in raw information management. The information management problem for pervasiveness is defined as a nonlinear problem for user‐to‐information availability. According to the CIM, pervasive convergence is identified for the available information. Furthermore, convolutional neural learning identifies unavailable or utilized information between the service provider and the end‐user layers. This identification improves the training rate for convergence improvement and information coherency. Therefore, the nonlinear pervasiveness is distributed between different service providers, improving efficiency. Therefore, the proposed optimization method achieves a 7.4% high admittance ratio, 8.28% high utilization, 25.25% less allocation time, and 24.4% less waiting time for different availability ratios for different information availability ratios.