Collaborative Task Scheduling for IoT-Assisted Edge Computing

The Internet of Things (IoT) is evolving rapidly and requires IoT devices to have more resources to meet the growing needs in diverse application domains. Despite increasing demands, modern IoT devices do not fully utilize somewhat over-provisioned computing resources. In this work, we introduce a n...

Full description

Saved in:
Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.216593-216606
Main Authors: Kim, Youngjin, Song, Chiwon, Han, Hyuck, Jung, Hyungsoo, Kang, Sooyong
Format: Article
Language:English
Subjects:
Algorithms
Cloud computing
Collaboration
Devices
Edge computing
Edge service
Energy consumption
Internet of Things
IoT service
IoT-assisted edge computing
Processor scheduling
Scheduling
Servers
Task analysis
Task scheduling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The Internet of Things (IoT) is evolving rapidly and requires IoT devices to have more resources to meet the growing needs in diverse application domains. Despite increasing demands, modern IoT devices do not fully utilize somewhat over-provisioned computing resources. In this work, we introduce a new concept of IoT-assisted Edge Computing which makes use of consolidated idle resources in IoT devices for edge services through offloading edge tasks to nearby IoT devices. For the IoT-assisted edge computing be beneficent, two important conditions should be satisfied: 1) offloaded edge tasks to IoT devices do not hurt normal execution of local IoT tasks, and 2) computing resources in IoT devices should be effectively exploited to increase the throughput of edge services. To that end, we propose a collaborative task scheduling for IoT-assisted edge computing, in which an edge node determines where to offload edge tasks among participating IoT devices based on the offloaded execution time and energy consumption, and each IoT device determines when to execute the offloaded tasks considering local tasks execution. Experimental results show that the proposed scheme not only achieves near-optimal task throughput but also outperforms other scheduling algorithms in terms of deadline satisfaction ratio of time critical tasks, while guaranteeing deadlines of local tasks in IoT devices.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3041872