An Adaptive Energy-Saving Scheme for Green Optical Networking: Combining Flow-Aware Load Adaptive Routing and Flow-Aware Distance Adaptive Routing

被引:0
|
作者
Swamidoss, Mathumohan [1 ]
Samiayya, Duraimurugan [2 ]
Gunasekar, Manikandan [3 ]
机构
[1] Unnamalai Inst Technol, Dept Comp Sci & Engn, Kovilpatti, India
[2] St Josephs Coll Engn, Dept Informat Technol, Chennai, India
[3] Anna Univ, St Josephs Coll Engn, Dept Artificial Intelligence & Machine Learning, Chennai, India
关键词
Flow-aware distance adaptive scheme; Flow-aware load adaptive schemes; routing; traffic demand; link utilization; SPECTRUM ASSIGNMENT; TECHNOLOGIES; MODULATION; EFFICIENCY;
D O I
10.1080/01468030.2023.2268565
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
Nowadays, there is an increasing need for advanced technological solutions due to concerns about the energy crisis and environmental protection. In particular, there has been a growing focus on developing green Information and Communication Technologies (ICT) solutions, as the energy consumption of ICT devices has been rapidly increasing in recent decades. This paper introduces EPSO-based flow-aware routing mechanisms that aim to minimize Queueing Delay, Power Consumption (Energy Dissipation), and Load Deviation. Two flow-aware mechanisms, namely, the Flow-aware load adaptive scheme (FA-LAR) and the flow-aware distance adaptive scheme (FA-DAR), are utilized in this study. The goal is to avoid traffic congestion across the routes by selecting the most direct and efficient paths for the flows. These routed paths are optimized using the MEPSO-based FA-DAR routing algorithm, resulting in significant energy savings. Extensive numerical simulations are conducted to evaluate the proposed algorithms using metrics such as throughput, routing efficiency, queuing delay, load deviation, and energy efficiency. The simulation results demonstrate that the proposed method outperforms existing benchmark methods in all metrics. Furthermore, it ensures lower power consumption, queuing delay, and load deviation. The method exhibits a 30% improvement in throughput and a 24% improvement in energy efficiency compared to existing benchmark methods.
引用
收藏
页码:185 / 217
页数:33
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