Intelligent Task Prediction and Partial Computation Offloading in Mobile Edge Cloud Computing

Abstract

The risk of fraudulent software or apps undermining user privacy is rising for users of cell phones and other portable electronics. Because malicious apps need less permission to run, they are more intrusive than necessary. Due to its open-source nature, support for third-party app stores, and stringent app assessment, the Android platform is more susceptible to assaults. Thus, the Android platform has also led to an increase in the use of portable computing apps. Using edge computing and cloud services, Mobile Edge Cloud Computing (MECC), showing promise in the fractional computation offload approach, has opened up fresh opportunities for mobile apps that are delay-sensitive and computationally demanding. We thoroughly examine the unpredictability method, calculating the arrivals of requests, assistance latency, and variable processing resources to solve this problem. High traffic volumes are produced by many devices that the MEC architecture can manage. First, we give a comprehensive introduction to MCC/MEC technology in this paper, covering the history and development of remote computation techniques. This paper’s main body then examines current research regarding the ideas of computing offloading, offloading granularities, and offloading procedures techniques. Furthermore, we go over optimization techniques as well as both static and dynamic offloading mechanisms. Environments. We also go over the difficulties and possible paths for MEC research in the future.