Reinforcement optimization for decentralized service placement policy in IoT-centric fog environment

Sulimani, H; Sajjad, AM; Alghamdi, WY; Kaiwartya, O; Jan, T; Simoff, S; Prasad, M

Reinforcement optimization for decentralized service placement policy in IoT-centric fog environment

Sulimani, H

Sajjad, AM Alghamdi, WY Kaiwartya, O Jan, T Simoff, S Prasad, M

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Publisher:: WILEY
Publication Type:: Journal Article
Citation:: Transactions on Emerging Telecommunications Technologies, 2022
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Sulimani, H https://orcid.org/0000-0001-6121-9370
dc.contributor.author	Sajjad, AM
dc.contributor.author	Alghamdi, WY
dc.contributor.author	Kaiwartya, O
dc.contributor.author	Jan, T
dc.contributor.author	Simoff, S
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.date.accessioned	2023-03-24T10:01:52Z
dc.date.available	2023-03-24T10:01:52Z
dc.date.issued	2022-01-01
dc.identifier.citation	Transactions on Emerging Telecommunications Technologies, 2022
dc.identifier.issn	2161-3915
dc.identifier.issn	2161-3915
dc.identifier.uri	http://hdl.handle.net/10453/168363
dc.description.abstract	A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real-time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time-sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well-known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice-based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real-time IoT applications.
dc.language	English
dc.publisher	WILEY
dc.relation.ispartof	Transactions on Emerging Telecommunications Technologies
dc.relation.isbasedon	10.1002/ett.4650
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Reinforcement optimization for decentralized service placement policy in IoT-centric fog environment
dc.type	Journal Article
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-24T10:01:50Z
pubs.publication-status	Published

Abstract:

A decentralized service placement policy plays a key role in distributed systems, such as fog computing, where sharing workloads fairly among active computing nodes is critical. A decentralized policy is an inherent feature of the service placement process that may improve load balancing among computers and can reduce the latency in many real-time Internet of Things (IoT) applications. This article proposes reinforcement optimization for a decentralized service placement policy, which attempts to mitigate some of the drawbacks of existing service placement policies. Matching task size with node specifications and the allocation of less popular but time-sensitive applications in the fog layer are the primary contributions of this study. Extensive experimental comparisons are made between the proposed algorithm and other well-known algorithms over service latency, network usage, and computing usage using the iFogSim simulator. A microservice-based application with varying sizes of computing requests are tested experimentally and show that the proposed algorithm effectively serves computing instances that are closer to users, reducing service latency and network usage. Compared to the existing models, the proposed modified algorithm reduces service latency by 24.1%, network usage by 4%, and computing usage by 20%, thus highlighting positive outcomes when using the proposed algorithm for fog analytics in future real-time IoT applications.

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http://hdl.handle.net/10453/168363