Multi-Stage Power-Aware Intelligent Adaptive Routing Algorithms in Bundled Links Based Backbone Networks
The increasing energy consumption in backbone networks has been one of crucial concerns in Information and Communications Technology (ICT) sector. From a viewpoint of intelligent adaption, we propose Green Routing in Backbone networks with Bundled links, which is referred to as GRB2, to investigate...
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| Published in: | IEEE access Vol. 10; pp. 109863 - 109893 |
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| Main Authors: | , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Piscataway
IEEE
2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subjects: | |
| ISSN: | 2169-3536, 2169-3536 |
| Online Access: | Get full text |
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| Summary: | The increasing energy consumption in backbone networks has been one of crucial concerns in Information and Communications Technology (ICT) sector. From a viewpoint of intelligent adaption, we propose Green Routing in Backbone networks with Bundled links, which is referred to as GRB2, to investigate the substantial power saving in a bundled link based backbone network for above enormous challenges from severe alarming statistics of the issues on economy, energy and environment. We formulate the above problems as Mixed Integer Linear Programming (MILP) models and develop power-aware greedy heuristics to solve them. We have investigated and compared the different characterizations of the solutions to the proposed problems by evaluating network power consumption including Power Saving Ratio (PSR) profile over time, PSR profile under different Maximum Cable Utilization (MCU) and PSR profile under different Bundled Sizes (BSs) and network performance including Powered-Off Cable Rate (POCR), Mean State Switching Times (MSST) and Mean Running Time (MRT) for various traffic demands during PPs and OPPs under different real backbone network topology scenarios compared with MSPF, SSPF, and HDEER. Experiment results show the different power-saving potential of these solutions once applied in the backbone network. |
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| Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2169-3536 2169-3536 |
| DOI: | 10.1109/ACCESS.2022.3213057 |