Accurate Prediction of Required Virtual Resources via Deep Reinforcement Learning

Abstract

Resource provisioning for the ever-increasing applications to host the necessary network functions necessitates the efficient and accurate prediction of required resources. However, the current efforts fail to leverage the inherent features hidden in network traffic, such as temporal stability, service correlation and periodicity, to predict the required resources in an intelligent manner, incurring coarse-grain prediction accuracies. To tackle this problem, in this paper, we propose an Accurate Prediction of Required virtual Resources (APRR) approach via Deep Reinforcement Learning (DRL). We first confirm the resource requests have more similar features and identify the high-dimensional required resources in computing, storage and bandwidth can be effectively consolidated into a single standardized value. Built upon these observations, we then model the required resources as a time-variant network matrix, which includes a number of elements, obtained from the network measurements, and some missing elements needed to be inferred. To obtain accurately predicted results, DRL-based matrix factorization with a set of available rules has been introduced into APRR and alternately executed in agent to minimize the prediction errors. Moreover, the error-prioritized designed for model training with quicker convergence. Simulation experiments on real-world datasets illustrate that APRR can accurately predict the required virtual resources compared with the related approaches.