Optimal Task Allocation in Near-Far Computing Enhanced C-RAN for Wireless Big Data Processing

TL;DR

This paper introduces a novel Near-Far Computing Enhanced C-RAN architecture that optimizes task allocation between near and far edge computing units to improve big data processing in wireless networks.

Contribution

It proposes a new architecture combining near and far edge computing in C-RAN and introduces task allocation strategies for efficient big data processing.

Findings

Task allocation improves processing efficiency.

Simulation and experiments validate the architecture.

Enhanced response to delay-sensitive tasks.

Abstract

With the increasing popularity of user equipments (UEs), the corresponding UEs' generating big data (UGBD) is also growing substantially, which makes both UEs and current network structures struggling in processing those data and applications. This paper proposes a Near-Far Computing Enhanced C-RAN (NFC-RAN) architecture, which can better process big data and its corresponding applications. NFC-RAN is composed of near edge computing (NEC) and far edge computing (FEC) units. NEC is located in remote radio head (RRH), which can fast respond to delay sensitive tasks from the UEs, while FEC sits next to baseband unit (BBU) pool which can do other computational intensive tasks. The task allocation between NEC or FEC is introduced in this paper. Also WiFi indoor positioning is illustrated as a case study of the proposed architecture. Moreover, simulation and experiment results are provided to show the effectiveness of the proposed task allocation and architecture.