An Innovative Priority Queueing Strategy for Mitigating Traffic Congestion in Complex Networks

被引:2
作者
Wu, Ganhua [1 ]
机构
[1] Univ Shanghai Sci & Technol, Business Sch, Shanghai 200093, Peoples R China
关键词
generalized priority queueing strategy; routing strategy; traffic congestion; complex networks; TRANSPORT;
D O I
10.3390/math13030495
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
Optimizing transportation in both natural and engineered systems, particularly within complex network environments, has become a pivotal area of research. Traditional methods for mitigating congestion primarily focus on routing strategies that utilize first-in-first-out (FIFO) queueing disciplines to determine the processing order of packets in buffer queues. However, these approaches often fail to explore the benefits of incorporating priority mechanisms directly within the routing decision-making processes, leaving significant room for improvement in congestion management. This study introduces an innovative generalized priority queueing (GPQ) strategy, specifically designed as an enhancement to existing FIFO-based routing methods. It is important to note that GPQ is not a new queue scheduling algorithm (e.g., deficit round robin (DRR) or weighted fair queuing (WFQ)), which typically manage multiple queues in broader queue management scenarios. Instead, GPQ integrates a dynamic priority-based mechanism into the routing layer, allowing the routing function to adaptively prioritize packets within a single buffer queue based on network conditions and packet attributes. By focusing on the routing strategy itself, GPQ improves the process of selecting packets for forwarding, thereby optimizing congestion management across the network. The effectiveness of the GPQ strategy is evaluated through extensive simulations on single-layer, two-layer, and dynamic networks. The results demonstrate significant improvements in key performance metrics, such as network throughput and average packet delay, when compared to traditional FIFO-based routing methods. These findings underscore the versatility and robustness of the GPQ strategy, emphasizing its capability to enhance network efficiency across diverse topologies and configurations. By addressing the inherent limitations of FIFO-based routing strategies and proposing a generalized yet scalable enhancement, this study makes a notable contribution to network optimization. The GPQ strategy provides a practical and adaptable solution for improving transportation efficiency in complex networks, bridging the gap between conventional routing techniques and emerging demands for dynamic congestion management.
引用
收藏
页数:21
相关论文
共 42 条
[1]   Flow-Aware Forwarding in SDN Datacenters Using a Knapsack-PSO-Based Solution [J].
Abdollahi, Sahar ;
Deldari, Arash ;
Asadi, Hamid ;
Montazerolghaem, AhmadReza ;
Mazinani, Sayyed Majid .
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT, 2021, 18 (03) :2902-2914
[2]   A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System [J].
Afrin, Tanzina ;
Yodo, Nita .
SUSTAINABILITY, 2020, 12 (11)
[3]   A Review of Optimal Charging Strategy for Electric Vehicles under Dynamic Pricing Schemes in the Distribution Charging Network [J].
Amin, Adil ;
Tareen, Wajahat Ullah Khan ;
Usman, Muhammad ;
Ali, Haider ;
Bari, Inam ;
Ben Horan ;
Mekhilef, Saad ;
Asif, Muhammad ;
Ahmed, Saeed ;
Mahmood, Anzar .
SUSTAINABILITY, 2020, 12 (23) :1-28
[4]   Hidden geometry of traffic jamming [J].
Andjelkovic, Miroslav ;
Gupte, Neelima ;
Tadic, Bosiljka .
PHYSICAL REVIEW E, 2015, 91 (05)
[5]  
[Anonymous], 2001, Juniper Netw
[6]   Communication in networks with hierarchical branching [J].
Arenas, A ;
Díaz-Guilera, A ;
Guimerà, R .
PHYSICAL REVIEW LETTERS, 2001, 86 (14) :3196-3199
[7]   Shortest paths and loop-free routing in dynamic networks [J].
Awerbuch, B. .
Computer Communications Review, 1990, 20 (04)
[8]   An adaptive intelligent routing algorithm based on deep reinforcement learning [J].
Bai, Jie ;
Sun, Jingchuan ;
Wang, Zhigang ;
Zhao, Xunwei ;
Wen, Aijun ;
Zhang, Chunling ;
Zhang, Jianguo .
COMPUTER COMMUNICATIONS, 2024, 216 :195-208
[9]   Emergence of scaling in random networks [J].
Barabási, AL ;
Albert, R .
SCIENCE, 1999, 286 (5439) :509-512
[10]   A Comparative Study of Ensemble Models for Predicting Road Traffic Congestion [J].
Bokaba, Tebogo ;
Doorsamy, Wesley ;
Paul, Babu Sena .
APPLIED SCIENCES-BASEL, 2022, 12 (03)