PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

Feng Hong,Jing Fan,Tianming Zhang,Bin Cao,Yuyu Yin

doi:10.1155/2022/6430612

Abstract

With the development of the smart Internet of Things (IoT), an increasing number of tasks are deployed on the edge of the network. Considering the substantially limited processing capability of IoT devices, task scheduling as an effective solution offers low latency and flexible computation to improve the system performance and increase the quality of services. However, limited computing resources make it challenging to assign the right tasks to the right devices at the edge of the network. To this end, we propose a polynomial-time solution, which consists of three steps, i.e., identifying available devices, estimating device quantity, and searching for feasible schedules. In order to shrink the number of potential schedules, we present a pairwise-allocated strategy (PA). Based on these, a capability average matrix (CAM)-based index is designed to further boost efficiency. In addition, we evaluate the schedules by the technique for order preference by similarity to an ideal solution (TOPSIS). Extensive experimental evaluation using both real and synthetic datasets demonstrates the efficiency and effectiveness of our proposed approach.

Highlights

As an essential function, task scheduling of edge computing has widespread applications in various domains such as the Internet of vehicle [1], transportation [2], health emergency [3], and smart homes [4]
We propose a pairwise-allocated strategy and capability average matrix-based approach (PACAM), which is a polynomial solution
We experimentally evaluate PACAM using a variety of real and synthetic datasets coming from China Telecom company. e real part of datasets contains daily call arrivals for six months of 2020, and the synthetic part is the devices and their capabilities in the corresponding months

Summary

Introduction

Task scheduling of edge computing has widespread applications in various domains such as the Internet of vehicle [1], transportation [2], health emergency [3], and smart homes [4]. With the development of the smart Internet of ings (IoT) [5,6,7], an increasing number of tasks deployed on the edge of the network and the IoT devices require to process the heavy tasks with finite response time. Considering the substantially limited processing capability, how to assign tasks to these devices is a significant issue to improve the performance of the system and increase the quality of services.

Methods

Discussion

Conclusion