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
Path planning is a critical technology that could help mobile robots accomplish their tasks quickly. However, some path planning algorithms tend to fall into local optimum in complex environments. A path planning method using a modified Harris hawks optimization (MHHO) algorithm is proposed to address the problem and improve the path quality. The proposed method improves the performance of the algorithm through multiple strategies. A linear path strategy is employed in path planning, which could straighten the corner segments of the path, making the obtained path smooth and the path distance short. Then, to avoid getting into the local optimum, a local search update strategy is applied to the HHO algorithm. In addition, a nonlinear control strategy is also used to improve the convergence accuracy and convergence speed. The performance of the MHHO method was evaluated through multiple experiments in different environments. Experimental results show that the proposed algorithm is more efficient in path length and speed of convergence than the ant colony optimization (ACO) algorithm, improved sparrow search algorithm (ISSA), and HHO algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.