Edge-Cloud Collaborative Optimization Scheduling with Micro-Service Architecture

Qiuyan Liu,Mingxuan Li,Jiajun Li,Yi Feng,Zhonghao Zhang,Huazhang Lv

doi:10.4236/jcc.2019.710009

Abstract

The architecture of edge-cloud cooperation is proposed as a compromising solution that combines the advantage of MEC and central cloud. In this paper we investigated the problem of how to reduce the average delay of MEC application by collaborative task scheduling. The collaborative task scheduling is modeled as a constrained shortest path problem over an acyclic graph. By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm. Generally, the edge-cloud collaborative task scheduling scheme performance better than independent scheme in reducing average delay. In heavy workload scenario, high blocking probability and retransmission delay at MEC is the key factor for average delay. Hence, more task executed on central cloud with abundant resource is the optimal scheme. Otherwise, transmission delay is inevitable compared with execution delay. MEC configured with higher priority and deployed close to terminals obtain more performance gain.

Highlights

As digital applications proliferate, 5G network is deeply integrated with enhanced mobile broadband, ultra reliable low latency communications, and massive machine type communication services, such as virtual reality/augmented reality, 4K/8K live video, artificial intelligence decision and big data analysis [1] [2] [3] [4]
In this paper we investigated the problem of how to reduce the average delay of Multiple access edge computing (MEC) application by collaborative task scheduling
By characterizing the optimal solution, the constrained optimization problem is simplified according to one-climb theory and enumeration algorithm

Summary

Introduction

5G network is deeply integrated with enhanced mobile broadband (eMBB), ultra reliable low latency communications (uRLLC), and massive machine type communication (mMTC) services, such as virtual reality/augmented reality, 4K/8K live video, artificial intelligence decision and big data analysis [1] [2] [3] [4]. As a simplified version of central cloud, MEC executes the cloud computing task locally by deep data inspection, which can reduce the transmission delay and avoid the flow storm towards backhaul and core network [7] [8] [9]. The cooperation between MEC and central cloud is significant in task scheduling [21]. In this scenario, backhaul bandwidth and transmission delay with various MEC deployment positions are the critical factors. We investigate problem of how to reduce the average delay of MEC application with cloud assistant in a more generally scenario.

System Model and Problem Formulation

Characterization of Optimal Solution to Task Scheduling Policy

Performance Evaluation

Conclusions