Citation: Liu, L.; Chen, H.; Xu, Z.
SPMOO: A Multi-Objective
Offloading Algorithm for Dependent
Tasks in IoT Cloud-Edge-End
Collaboration. Information 2022, 13,
75. https://doi.org/10.3390/
info13020075
Academic Editor: Corinna Schmitt
Received: 12 January 2022
Accepted: 2 February 2022
Published: 5 February 2022
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Article
SPMOO: A Multi-Objective Offloading Algorithm for
Dependent Tasks in IoT Cloud-Edge-End Collaboration
Liu Liu
1
, Haiming Chen
1,2,
* and Zhengtao Xu
3
1
Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo 315211, China;
Lliu661004@163.com
2
Zhejiang Provincial Key Laboratory of Mobile Network Application Technology, Ningbo University,
Ningbo 315211, China
3
Chu Kochen Honors College, Zhejiang University, Hangzhou 310063, China; xuzhengtao@zju.edu.cn
* Correspondence: chenhaiming@nbu.edu.cn
Abstract:
With the rapid development of the internet of things, there are more and more end devices,
such as wearable devices, USVs and intelligent automobiles, connected to the internet. These devices
tend to require large amounts of computing resources with stringent latency requirements, which
inevitably increases the burden on edge server nodes. Therefore, in order to alleviate the problem that the
computing capacity of edge server nodes is limited and cannot meet the computing service requirements
of a large number of end devices in the internet of things scenario, we combined the characteristics of
rich computing resources of cloud servers and low transmission delay of edge servers to build a hybrid
computing task-offloading architecture of cloud-edge-end collaboration. Then, we study offloading
based on this architecture for complex dependent tasks generated on end devices. We introduce a two-
dimensional offloading decision factor to model latency and energy consumption, and formalize the
model as a multi-objective optimization problem with the optimization objective of minimizing the
average latency and average energy consumption of the task’s computation offloading. Based on this,
we propose a multi-objective offloading (SPMOO) algorithm based on an improved strength Pareto
evolutionary algorithm (SPEA2) for solving the problem. A large number of experimental results show
that the algorithm proposed in this paper has good performance.
Keywords:
internet of things (IoT); cloud-edge-end collaboration; task offloading; delay;
energy consumption
1. Introduction
With the rapid development of various internet-of-things (IoT) fields, such as intelli-
gent traffic, intelligent home and intelligent manufacturing [
1
–
4
], the number of various
IoT devices (e.g., wearable devices, unmanned surface vehicles (USVs) and intelligent
automobiles [
5
–
7
] etc.) has increased significantly. According to Cisco, there are now more
than 30 billion mobile IoT devices worldwide, generating about 2.5 EB of data per day,
which often requires further processing and analysis. However, IoT devices have limited
computing power and storage resources because they are mostly small, battery-powered
and equipped with sensors. So, many latency-sensitive computing tasks generated in
real time, such as face recognition, virtual reality (VR) and augmented reality (AR) [
8
–
10
],
are present difficulties in guaranteeing users’ real-time experience when executed on local
devices. These tasks are usually passed to the cloud, which costs time and computation
power in maintaining the long-distance connection [
11
]. However, since the ultra-long-
distance communication between IoT devices and remote clouds in real-world scenarios
requires a large amount of bandwidth resources, sending all locally generated tasks to
remote clouds for processing would bring serious problems, such as high latency and
network congestion.
To alleviate these problems, one of the more effective approaches today is to offload all
complex computing tasks from the local device to a nearby edge server. The edge servers
Information 2022, 13, 75. https://doi.org/10.3390/info13020075 https://www.mdpi.com/journal/information
