An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing

Edge Computing (EC) is a promising concept to overcome some obstacles of traditional cloud data centers to support Internet of Things (IoT) applications, especially time-sensitive applications. However, EC faces some challenges, including the resource allocation for heterogeneous applications at a n...

Full description

Saved in:
Bibliographic Details
Published in:Computer networks (Amsterdam, Netherlands : 1999) Vol. 194; p. 108146
Main Authors: Maia, Adyson M., Ghamri-Doudane, Yacine, Vieira, Dario, Franklin de Castro, Miguel
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 20.07.2021
Elsevier Sequoia S.A
Elsevier
Subjects:
5G network
Algorithms
Application
Cloud computing
Computer Science
Data centers
Decision making
Edge computing
Genetic algorithm
Genetic algorithms
Genetics
Heuristic
Internet
Internet of Things
Load distribution
Load distribution (forces)
Multi-objective optimization
Objectives
Placement
Resource allocation
Service placement
Violations
Workloads
Internet of things
Load distribution
Genetic algorithm
5G network
Multi-objective optimization
Service placement
Edge computing
Edge Computing
5G Network slicing
IoT
ISSN:1389-1286, 1872-7069
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Edge Computing (EC) is a promising concept to overcome some obstacles of traditional cloud data centers to support Internet of Things (IoT) applications, especially time-sensitive applications. However, EC faces some challenges, including the resource allocation for heterogeneous applications at a network edge composed of distributed and resource-restricted nodes. A relevant issue that needs to be addressed by a resource manager is the service placement problem, which is the decision-making process of determining where to place different services (or applications). A related issue of service placement is how to distribute workloads of an application placed on multiple locations. Hence, we jointly investigate the load distribution and placement of IoT applications to minimize Service Level Agreement (SLA) violations due to the limitations of EC resources and other conflicting objectives. In order to handle the computational complexity of the formulated problem, we propose a multi-objective genetic algorithm with the initial population based on random and heuristic solutions to obtain near-optimal solutions. Evaluation results show that our proposal outperforms other benchmark algorithms in terms of response deadline violation, as well as terms of other conflicting objectives, such as operational cost and service availability.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1389-1286
1872-7069
DOI:10.1016/j.comnet.2021.108146