A Feedback Prediction Model for Resource Usage and Offloading Time in Edge Computing

Nowadays, edge computing which provides low delay services has gained much attention in the research filed. However, the limited resources of the platform make it necessary to predict the usage, execution time exactly and further optimize the resource utilization during offloading. In this paper, we propose a feedback prediction model (FPM), which includes three processes: the usage prediction process, the time prediction process and the feedback process. Firstly, we use average usage instead of instantaneous usage for usage prediction and calibrate the prediction results with real data. Secondly, building the time prediction process with the predicted usage values, then project the time error to usage value and update the usage values. Meanwhile, our model re-executes the time prediction process. Thirdly, setting a judgment and feedback number to the correction process. If prediction values meet the requirement or reach the number, FPM stops error feedback and skips to the next training. We compare the testing results to other two model which are BP neural network and FPM without feedback process (NO-FP FPM). The average usage and time prediction errors of BP and NO-FP FPM are 10%, 25% and 16%, 12%. The prediction accuracy in FPM has a great improvements. The average usage prediction errors can reach less than 8% and time error reach about 6%.