Provisioning network slice for mobile content delivery in uncertain MEC environment

Abstract

Network Slicing (NS) and Mobile Edge Computing (MEC) are recognized as a promising paradigm for mobile Content Delivery Network (CDN) by instantiating Virtual Network Functions (VNFs) on MEC platforms. Mobile network exhibits intrinsic dynamics, where user requests patterns vary in time and space due to human behaviors. The requirements and distribution of users are arbitrary spatio-temporal stochastic variables, which makes the deployment and reconfiguration of a CDN slice a challenging task. In this work, by adopting a periodical reconfiguration strategy, we consider a CDN slice system which operates in discrete time intervals. Within each time interval, we formalize the CDN-MEC-VNFs Planning (CMVP) problem based on uncertain programming to tackle the dynamic and uncertain mobile network environment, where user requests are treated as random variables with arbitrary pattern. Then, we propose a data-driven and learning-based algorithm which integrates Stochastic Simulation (SS), Deep Neural Networks (DNN) and meta-heuristic algorithm to determine the provisioning of the CDN slice from historical user requests information. The practicability of the proposed mechanism in offline training and online running are also discussed. Finally, we conducted intensive real-trace driven simulations to demonstrate the effectiveness of our approach on planning CDN slice with higher QoS under dynamic system environment and arbitrary user requests by comparing towards several baselines.