Video Stream Session Migration Method Using Deep Reinforcement Learning in Cloud Computing Environment

Lingling Li,Huixia Liu,Shan Zhong

doi:10.1155/2021/5579637

Lingling Li, Huixia Liu + Show 1 more

Open Access

https://doi.org/10.1155/2021/5579637

Copy DOI

Abstract

In the resource scheduling of streaming Media Edge Cloud (MEC), in order to balance the cost and load of migration, this paper proposes a video stream session migration method based on deep reinforcement learning in cloud computing environment. First, combined with the current popular OpenFlow technology, a novel MEC architecture is designed, which separates streaming media service processing in application layer from forwarding path optimization in network layer. Second, taking the state information of the system as the attribute feature, the session migration is calculated, and gradient reinforcement learning is combined with in-depth learning and deterministic strategy for video stream session migration to solve the user request access problem. The experimental results show that the method has a better request access effect, can effectively improve the request acceptance rate, and can reduce the migration cost, while shortening the running time.

Highlights

In recent years, with the maturity of cloud computing technology, streaming media services are gradually transforming to cloud form, that is, streaming Media Cloud
According to the resource allocation of streaming media edge cloud, in order to balance the cost and load of migration, considering the cost of migration, load balancing, and other constraints, this paper proposes a video stream session migration method based on deep reinforcement learning
Streaming Media Edge Cloud is located on the edge of the network, which is responsible for local video services

Summary

Introduction

With the maturity of cloud computing technology, streaming media services are gradually transforming to cloud form, that is, streaming Media Cloud. The subcloud can adapt to the changes of system load and the size of user requests, so that to effectively solve the problem of traditional streaming media services [2]. According to the resource allocation of streaming media edge cloud, in order to balance the cost and load of migration, considering the cost of migration, load balancing, and other constraints, this paper proposes a video stream session migration method based on deep reinforcement learning. (1) This paper improves the resource utilization by effectively utilizing the state information of the MEC system, combining in-depth learning and deterministic strategy for video stream session migration (2) This paper proposes a session migration computing model to process user requests more scientifically, maximize the access rate of user requests, and control the migration cost appropriately, at the same time, make the system achieve load balancing as far as possible

Streaming Media Edge Cloud Architecture

Session Scheduling Strategy Based on Deep Reinforcement Learning

Experiment and Analysis

Findings

Conclusion