Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding

TL;DR

This paper introduces a deep reinforcement learning approach for admission control in wireless virtual network embedding, optimizing resource allocation for service function chaining requests under varying load conditions.

Contribution

It presents a novel deep RL method for learning admission policies considering request dependencies, outperforming traditional baseline approaches.

Findings

Deep RL achieves higher acceptance rates than baseline.

The approach maintains low rejection rates under high load.

It effectively manages request dependencies like service lifetime and priority.

Abstract

Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources - increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.