A new replica placement mechanism for mobile media streaming in edge computing

Abstract

Summary With the advent of the Internet of things era, cloud computing platforms will face the challenges of massive equipment requirements for access, massive data, insufficient bandwidth, and high power consumption. Edge computing, as a new technology, makes it possible to stream media over the edge network. However, many problems related to file sharing among edge nodes and to the files provided by cloud servers exist. We propose a novel replica placement strategy for mobile media streaming in edge computing (RPME) to address the aforementioned problem. First, we introduce a multilevel replica placement model in the RPME. In the RPME, we acquire the user information and the user‐item rating matrix when the user requests the media data. At the server, we cluster the users according the user information, update the user‐item rating matrix, and then generate a replica recommendation sequence. For when the server submits the replica recommendation sequence to the edge node considering the constraints of the edge node storage capacity and the limitations on the service capacity of the requested replica, we propose an effective replica placement mechanism in the RPME. To show the benefits of the RPME, we also present several experiments to prove its validity.

Replica selection and placement techniques on the IoT and edge computing: a deep study

“…Edge computing is developing as the modern paradigm that meets the demands of localized computing and the IoT by utilizing this as a method to reduce resource congestion [35]. It is a novel technology that lets media be streamed across a network's edge [50]. There are two main advantages of edge computing: firstly, edge nodes can assist with much lower latency than the cloud because servers are physically closer to the devices, and secondly, edge nodes can shield the cloud from most requests by serving the requests locally.…”

“…The suggested resource management method may lower the overall financial cost of the rented nodes and the SLA (Service Level Agreement) default rate while also improving CPU (Central Processing Unit) usage as time goes on Tang et al [50] Proposing a new replica placement mechanism for mobile media streaming in edge computing…”

Convergence of Recommender Systems and Edge Computing: A Comprehensive Survey

“…Some researchers focus on how to develop video recommendations for mobile devices [65]. RPME [66] is proposed to relieve the pressure of cloud computing platforms, such as massive equipment requirements for access, massive data, insufficient bandwidth, and high power consumption. RPME combines edge computing for dynamically placing the replicas to improve QoE of users.…”

“…In [66], Tang et al investigate an effective replica placement mechanism for mobile media streaming in edge computing with the constraints of the capacity of edge servers caching and the service capacity of the requested replica. The optimization goal of this mechanism is to improve recommendation accuracy.…”