TEAP: Traffic Engineering and ALR policy based Power-aware solutions for green routing and planning problems in backbone networks

Zhang, Jinhong; Wang, Xingwei; Huang, Min

doi:10.1016/j.comcom.2021.02.025

Cited by 6 publications

(4 citation statements)

References 33 publications

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“…The usage of the network lifetime, Packet Delivery Ratio, throughput, End‐to‐End delay, spectral efficiency, jitter, energy consumption, latency, scalability, and routing efficiency are performed in the estimation of efficiency. The existing methods of this work represent that their performance is not higher, among other models namely Ethernet Passive Optical Network (EPON), 11 Traffic Engineering and Adaptive Link Rate Policy (TEAP), 18 Online Sequence Extreme Learning Machine (OS‐ELM) 17 and Deep Q‐learning Multiple Networks (DQMN) 15 methods, the developed model effectiveness is better and superior.…”

Section: Resultsmentioning

confidence: 99%

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Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Swamidoss,

Samiayya,

Gunasekar

2024

Concurrency and Computation

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SummaryIn green optical networking, designing an adaptive energy‐saving scheme plays a vital role, in optimizing energy consumption by dynamically adjusting resources based on network traffic and environmental conditions, to a more sustainable and efficient optical communication infrastructure. Traditional methods in optical networking face challenges such as static resource allocation, limited adaptability, inefficient power usage, environmental insensitivity, and scalability issues. Therefore this article proposed a novel method named Dynamic Quality of Service based Random update Genghis Khan (DQ‐RGK) algorithm, the proposed model can tackle the abovementioned complexities. In this study, cluster head dynamic placement is utilized to optimize the network's performance by adapting the placement of cluster heads to the current topology, load distribution, and energy levels in the network nodes. Additionally, Dynamic Quality of Service (QoS) is employed to respond dynamically to changes in network conditions, adapting to varying traffic patterns and resource availability. In this work, the Genghis Khan Shark optimization with a random update strategy is implemented for hyperparameter optimization to enhance the performance of the DQ‐RGK method. The DQ‐RGK adjusts the parameters of QoS in real‐time, and this ensures that network resources based on the requirements changed and priorities of applications, which ultimately optimizes performance and enhances user experience. By dynamically assigning and reallocating resources based on the current demand the algorithm enhances overall network efficiency and reduces energy consumption. Then, this work analyzes the experimental results, where some evaluation measures estimate the DQ‐RGK method's performance. Routing efficiency, latency, scalability, spectral efficiency, Packet Delivery Ratio, throughput, network lifetime, energy consumption, jitter, and energy consumption are the measures employed by the DQ‐RGK model. In The results, other routing models that do not provide efficiency are utilized, a comparison of these other routing models is represented in results. The overall DQ‐RGK model's effectiveness is represented in the experimental results and its effectiveness is greater among other methods.

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Section: Resultsmentioning

confidence: 99%

“…Zhang et al 18 introduced Planning problems of the Green Networking (RPGN) model with a combination of Adaptive Link Rate (ALR) policy and Traffic Engineering (TE). Here, in the backbone networks, the analysis of the effective applicability and the power‐saving potentialities was conducted.…”

Section: Related Workmentioning

confidence: 99%

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Swamidoss,

Samiayya,

Gunasekar

2024

Concurrency and Computation

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“…Considering that statistical inference approaches can usually provide more reliable estimates for the traffic matrices than the deterministic ones owing to using more complete traffic data throughout a long time, we make use of the statistical methodology proposed in our previous work [42] to obtain the estimated traffic matrices as the input of our proposed routing and planning problems. For CERNET2, we use a traffic dataset on links provided by the Aladdin network management information platform available in [43].…”

Section: Mathematical Formulation Of Task Offloading Modelmentioning

confidence: 99%

Multi-Stage Power-Aware Intelligent Adaptive Routing Algorithms in Bundled Links Based Backbone Networks

et al. 2022

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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.INDEX TERMS Green networking, power awareness, traffic engineering, low power idle, bundled links

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“…This change brings many challenges as well, particularly in the bandwidth management and data routing [ 4 ], and can impact the performance in the IoT backbone network (in terms of congestion, delays, packets loss, etc.). This is because the multimedia services are high bandwidth-consuming, and the current IGP routing protocols (such as OSPF and RIP) cannot handle this enormous quantity of flow because they are originally designed for the Traditional Internet [ 5 ]. Since all IGP routing protocols use Ford–Fulkerson algorithm to find the maximum flow, only one flow (i.e., single commodity) is routed through the shortest-path even when there are a lot of data flows, they will be routed one by one.…”

Section: Introductionmentioning

confidence: 99%

Maximum Concurrent Flow Solutions for Improved Routing in IoT Future Networks

et al. 2022

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Due to the advent of IoT and the increasing interest of billions of Internet users towards video contents, a huge multimedia flows has been generated, and as a consequence, a massive load is applied on the underlying core network. This change can affect the network stability and lead to potential performance degradation (such as congestion and delays). This is because multimedia flows are hungry in bandwidth, and also the classical routing protocols currently used in IoT core network (like OSPF) are not adapted yet to support the heavy and the large scale of multimedia traffics with a good quality of service (QoS). In this paper, we introduce the multicommodity-based routing by proposing two contributions, Maximum Concurrent Flow Protocol (MCFPr) and Cache-based Maximum Concurrent Flow (C-MCF). They are conceived based on the Maximum Concurrent Flow approach in order to optimize the routing of multimedia data in the backbone of IoT networks. Both contributions are studied and compared with the state-of-art approaches under different scenarios showing good results, especially in the number of data packets sent (improvement by 50%), and in the transmission time (50% faster compared to the majority), which makes them promising solutions for a rapid and efficient routing in IoT core networks.

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TEAP: Traffic Engineering and ALR policy based Power-aware solutions for green routing and planning problems in backbone networks

Cited by 6 publications

References 33 publications

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Next‐generation energy‐efficient optical networking: DQ‐RGK algorithm for dynamic quality of service and adaptive resource allocation

Multi-Stage Power-Aware Intelligent Adaptive Routing Algorithms in Bundled Links Based Backbone Networks

Maximum Concurrent Flow Solutions for Improved Routing in IoT Future Networks

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