Artificial intelligence based load balancing in SDN: A comprehensive survey
In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the cont...
Saved in:
Published in | arXiv.org |
---|---|
Main Authors | , |
Format | Paper |
Language | English |
Published |
Ithaca
Cornell University Library, arXiv.org
04.08.2023
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network resource usage and overall performance due to the fast growth of the AI field. This survey focuses on the following: Firstly, analyzing the SDN architecture and investigating the problem of load balancing in SDN. Secondly, categorizing AI-based load balancing methods and thoroughly assessing these mechanisms from various perspectives, such as the algorithm/technique employed, the tackled problem, and their strengths and weaknesses. Thirdly, summarizing the metrics utilized to measure the effectiveness of these techniques. Finally, identifying the trends and challenges of AI-based load balancing for future research. |
---|---|
AbstractList | In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to traditional networks, SDN separates the control and data planes for efficient domain-wide traffic routing and management. The controllers in the control plane are responsible for programming data plane forwarding devices, while the top layer, the application plane, enforces policies and programs the network. The different levels of the SDN use interfaces for communication. However, SDN faces challenges with traffic distribution, such as load imbalance, which can negatively affect the network performance. Consequently, developers have developed various SDN load-balancing solutions to enhance SDN effectiveness. In addition, researchers are considering the potential of implementing some artificial intelligence (AI) approaches into SDN to improve network resource usage and overall performance due to the fast growth of the AI field. This survey focuses on the following: Firstly, analyzing the SDN architecture and investigating the problem of load balancing in SDN. Secondly, categorizing AI-based load balancing methods and thoroughly assessing these mechanisms from various perspectives, such as the algorithm/technique employed, the tackled problem, and their strengths and weaknesses. Thirdly, summarizing the metrics utilized to measure the effectiveness of these techniques. Finally, identifying the trends and challenges of AI-based load balancing for future research. |
Author | Ahmed Hazim Alhilali Montazerolghaem, Ahmadreza |
Author_xml | – sequence: 1 fullname: Ahmed Hazim Alhilali – sequence: 2 givenname: Ahmadreza surname: Montazerolghaem fullname: Montazerolghaem, Ahmadreza |
BookMark | eNqNit0KgjAYQEcUZOU7DLoW5qZp3Uk_BEE3dS9LP22yvtmmQm-fFz1AV-fAOQsyRYMwIR4XIgzSiPM58Z1rGGN8k_A4Fh65ZLZTlSqU1FRhB1qrGrAA-pAOSqqNLEfVEguF9XjQ2-G6oxktzKu18AR0agDqejvAZ0VmldQO_B-XZH063vfnoLXm3YPr8sb0FseU8zRKwphtUyb-u75B0T6M |
ContentType | Paper |
Copyright | 2023. This work is published under http://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
Copyright_xml | – notice: 2023. This work is published under http://creativecommons.org/publicdomain/zero/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
DBID | 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PIMPY PQEST PQQKQ PQUKI PTHSS |
DatabaseName | ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central ProQuest Central Essentials ProQuest Central Technology Collection ProQuest One Community College ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database Publicly Available Content Database ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection |
DatabaseTitle | Publicly Available Content Database Engineering Database Technology Collection ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest One Academic Engineering Collection |
DatabaseTitleList | Publicly Available Content Database |
Database_xml | – sequence: 1 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Physics |
EISSN | 2331-8422 |
Genre | Working Paper/Pre-Print |
GroupedDBID | 8FE 8FG ABJCF ABUWG AFKRA ALMA_UNASSIGNED_HOLDINGS AZQEC BENPR BGLVJ CCPQU DWQXO FRJ HCIFZ L6V M7S M~E PIMPY PQEST PQQKQ PQUKI PTHSS |
ID | FETCH-proquest_journals_28471509803 |
IEDL.DBID | BENPR |
IngestDate | Tue Sep 24 22:02:48 EDT 2024 |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-proquest_journals_28471509803 |
OpenAccessLink | https://www.proquest.com/docview/2847150980/abstract/?pq-origsite=%requestingapplication% |
PQID | 2847150980 |
PQPubID | 2050157 |
ParticipantIDs | proquest_journals_2847150980 |
PublicationCentury | 2000 |
PublicationDate | 20230804 |
PublicationDateYYYYMMDD | 2023-08-04 |
PublicationDate_xml | – month: 08 year: 2023 text: 20230804 day: 04 |
PublicationDecade | 2020 |
PublicationPlace | Ithaca |
PublicationPlace_xml | – name: Ithaca |
PublicationTitle | arXiv.org |
PublicationYear | 2023 |
Publisher | Cornell University Library, arXiv.org |
Publisher_xml | – name: Cornell University Library, arXiv.org |
SSID | ssj0002672553 |
Score | 3.4787354 |
SecondaryResourceType | preprint |
Snippet | In the future, it is anticipated that software-defined networking (SDN) will become the preferred platform for deploying diverse networks. Compared to... |
SourceID | proquest |
SourceType | Aggregation Database |
SubjectTerms | Algorithms Artificial intelligence Computer architecture Effectiveness Load balancing Software-defined networking |
Title | Artificial intelligence based load balancing in SDN: A comprehensive survey |
URI | https://www.proquest.com/docview/2847150980/abstract/ |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3dS8MwED-2FcE3P_FjjoC-ltYka1dfZGprUSzDD9jbSPOhwthm2wm--LebxFYFYW8JCSEJx_1yd7_cAZwoIVQQBdo2USF3qbYg3CiXJjceDnMcKY5zm-0zC9InejPuj1uQNn9hDK2y0YlWUYs5Nz5yz6pRjW4D32O58QLwyjtfvLmmfpSJs9bFNNrg4FNqArbORZyN7n_8LTgI9euZ_FO5FkeSDXBGbCGLTWjJ2RasWfolL7fhdlhYzo4WBvT6J0kmMiAj0HTOhG5OTW6M2bOegR6usjM0RIYQXsiXbxI6KpfFu_zYgeMkfrxM3WYHk1peysnv6cgudLThL_cAEU6YIJL1mfIpjTijkhPly5BIhSOm9qG7aqWD1cOHsG5Kp1syG-1CpyqW8kgDbJX3oD1Irnv1Dere3Wf8BTAyh1Q |
link.rule.ids | 786,790,12792,21416,33408,33779,43635,43840 |
linkProvider | ProQuest |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV1dS8MwFL1oi7g3P9E5NaCvxdJkrfVFpm5UN8vQCXsraXIzhbHNdhP89yaxU0HYWyAhJCGck3tzOBfgXEmpwjjUsYmKhMd0BOHFORpvvCDKg1iJILdun2mYvLCHYXNYJdzKSla5xEQL1HIqTI78wsKoZrdL_3r27pmqUeZ3tSqhsQ4uozpUccC9aaf9p58sSxBG-s1M_wGtZY_OFrh9PsNiG9ZwsgMbVnQpyl3otgqr1NFXgLz9scYkhlokGU-51M2xccSYjPQI8nyXXpEWMTLwAl-_peekXBQf-LkHZ5324DbxlivIqltSZr97ovvg6HAfD4BQQbmkyJtc-YzFgjMUVPkYUVRBzNUhNFbNVF_dfQqbyeCxl_Xu0-4R1EzxdCtnYw1w5sUCjzXFzvOT6hy_ANlwhHA |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Artificial+intelligence+based+load+balancing+in+SDN%3A+A+comprehensive+survey&rft.jtitle=arXiv.org&rft.au=Ahmed+Hazim+Alhilali&rft.au=Montazerolghaem%2C+Ahmadreza&rft.date=2023-08-04&rft.pub=Cornell+University+Library%2C+arXiv.org&rft.eissn=2331-8422 |