Scalable and Cost Efficient Resource Allocation Algorithms Using Deep Reinforcement Learning

The emergence of a new generation of applications led to the appearance of new challenges that represent improvements in current communication technologies. For this, a new network paradigm's including edge computing that allows the process of data at the edge of the network. And the 5G network slicing that represents a new generation of communication increases the capacity of mobile networks by supporting the slicing technology that allows virtual 'cutting' of a telecommunications network in several slices that provide high performance in terms of bandwidth and latency. Slice allocation and placement is an important networking optimization task that still painstakingly tune heuristics to get a sufficient solution. These algorithms use data as input and outputs near-optimal solutions. Thus, we are motivated by replacing this tedious process with the recent deep reinforcement learning algorithms. In this paper, we propose three approaches for Virtual Network Functions (VNFs) slices placement in edge computing (Integer linear programming (ILP), reinforcement learning (RL), and deep reinforcement learning (DRL)). Then they are implemented and evaluated. Several scenarios are considered to study the behavior of the algorithms and to quantify the impact of network size. The results show the feasibility and efficiency of the proposed techniques in terms of server utilization, placement time, and energy consumption.