Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN
To empower advanced traffic engineering (TE) mechanism, while considering the infeasibility of one-step migration to software-defined networking (SDN), SDN nodes are incrementally deployed into legacy network, which gives rise to hybrid SDN. In hybrid SDN, redirecting flow of every source-destinatio...
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doaj-1b729675920a424cb3a893bfd2fef27b2021-03-30T02:10:31ZengIEEEIEEE Access2169-35362020-01-018291112912410.1109/ACCESS.2020.29721038985283Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDNCheng Ren0https://orcid.org/0000-0002-3864-5336Shiwei Bai1https://orcid.org/0000-0003-4836-1393Yu Wang2https://orcid.org/0000-0002-3646-2819Yaxin Li3https://orcid.org/0000-0002-7659-4138School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, ChinaSchool of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, ChinaSchool of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, ChinaSchool of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, ChinaTo empower advanced traffic engineering (TE) mechanism, while considering the infeasibility of one-step migration to software-defined networking (SDN), SDN nodes are incrementally deployed into legacy network, which gives rise to hybrid SDN. In hybrid SDN, redirecting flow of every source-destination pair through at least one SDN node, can enhance TE performance and obtain flow manageability, while on the other hand leading to increasing demands of TCAM resources in SDN nodes. In this paper, we make minimization of maximum link utilization as the TE objective, and comply with SDN waypoint enforcement and TCAM resource limitation. We first formulate the TE problem as an integer linear programming (ILP) model and solve it in a centralized manner, where SDN waypoint selection and splitting fractions for each flow are jointly determined. Then, based on a fact that the logically centralized control plane in hybrid SDN is composed of multiple physically decentralized controllers, each of which manages part of SDN nodes, as well as considering a real situation that a centralized solution is infeasible or too fragile for large-scale network, we develop a distributed algorithm deriving from Lagrangian decomposition theory to effectively solve the TE problem. The simulation results indicate that, when 30% of the SDN nodes are deployed, the proposed traffic engineering-aware distributed routing (TEDR) algorithm obtains maximum link utilization comparable to that of full SDN, and has a limited influence on the routing efficiency.https://ieeexplore.ieee.org/document/8985283/Software-defined networkinghybrid SDNtraffic engineeringresource allocationdistributed algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cheng Ren Shiwei Bai Yu Wang Yaxin Li |
spellingShingle |
Cheng Ren Shiwei Bai Yu Wang Yaxin Li Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN IEEE Access Software-defined networking hybrid SDN traffic engineering resource allocation distributed algorithm |
author_facet |
Cheng Ren Shiwei Bai Yu Wang Yaxin Li |
author_sort |
Cheng Ren |
title |
Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN |
title_short |
Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN |
title_full |
Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN |
title_fullStr |
Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN |
title_full_unstemmed |
Achieving Near-Optimal Traffic Engineering Using a Distributed Algorithm in Hybrid SDN |
title_sort |
achieving near-optimal traffic engineering using a distributed algorithm in hybrid sdn |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2020-01-01 |
description |
To empower advanced traffic engineering (TE) mechanism, while considering the infeasibility of one-step migration to software-defined networking (SDN), SDN nodes are incrementally deployed into legacy network, which gives rise to hybrid SDN. In hybrid SDN, redirecting flow of every source-destination pair through at least one SDN node, can enhance TE performance and obtain flow manageability, while on the other hand leading to increasing demands of TCAM resources in SDN nodes. In this paper, we make minimization of maximum link utilization as the TE objective, and comply with SDN waypoint enforcement and TCAM resource limitation. We first formulate the TE problem as an integer linear programming (ILP) model and solve it in a centralized manner, where SDN waypoint selection and splitting fractions for each flow are jointly determined. Then, based on a fact that the logically centralized control plane in hybrid SDN is composed of multiple physically decentralized controllers, each of which manages part of SDN nodes, as well as considering a real situation that a centralized solution is infeasible or too fragile for large-scale network, we develop a distributed algorithm deriving from Lagrangian decomposition theory to effectively solve the TE problem. The simulation results indicate that, when 30% of the SDN nodes are deployed, the proposed traffic engineering-aware distributed routing (TEDR) algorithm obtains maximum link utilization comparable to that of full SDN, and has a limited influence on the routing efficiency. |
topic |
Software-defined networking hybrid SDN traffic engineering resource allocation distributed algorithm |
url |
https://ieeexplore.ieee.org/document/8985283/ |
work_keys_str_mv |
AT chengren achievingnearoptimaltrafficengineeringusingadistributedalgorithminhybridsdn AT shiweibai achievingnearoptimaltrafficengineeringusingadistributedalgorithminhybridsdn AT yuwang achievingnearoptimaltrafficengineeringusingadistributedalgorithminhybridsdn AT yaxinli achievingnearoptimaltrafficengineeringusingadistributedalgorithminhybridsdn |
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1724185599323144192 |