z-logo
open-access-imgOpen Access
A Smart Semipartitioned Real‐Time Scheduling Strategy for Mixed‐Criticality Systems in 6G‐Based Edge Computing
Author(s) -
Wenle Wang,
Chengying Mao,
Shuai Zhao,
Yuanlong Cao,
Yugen Yi,
Shaolong Chen,
Qinghua Liu
Publication year - 2021
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2021/6663199
Subject(s) - computer science , scheduling (production processes) , distributed computing , mixed criticality , edge computing , criticality , enhanced data rates for gsm evolution , real time computing , telecommunications , mathematical optimization , nuclear physics , physics , mathematics
With the rapid growth of 6G communication and smart sensor technology, the Internet of Things (IoT) has attracted much attention now. In the 6G-based IoT applications on the multiprocessor platform, the partitioned scheduling has been widely applied. However, these partitioned scheduling approaches could cause system resource waste and uneven workload among processors. In this paper, a smart semipartitioned scheduling strategy (SSPS) was proposed for mixed-criticality systems (MCS) in 6G-based edge computing. Besides tasks’ acceptance rate and weighted schedulability, QoS is considered in SSPS to improve the service quality of the system. The SSPS allocates tasks into each processor, and some tasks can migrate to other processors as soon as possible. By comparing with the several existing algorithms, the experimental results show that the SSPS achieves the best in the schedulability and QoS of the system.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom