Neural-based routing strategy with transmission information for complex communication networks

Kimura, Takayuki; Takamizawa, Tohru; Kimura, Koichiro; Jin’no, Kenya

doi:10.1587/nolta.6.263

Cited by 6 publications

(7 citation statements)

References 15 publications

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“…However, as mentioned above, the real-time information might not be instantaneously available in practical systems. To overcome these problems, we have already proposed a routing strategy by using congestion signals [6,7]. In our strategy, the binary information, called a congestion signal, is employed to diversify the transmitting routes of packets in the networks.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, the number of packets in the networks increases with these exchanges. However, the congestion signals in our strategy [6,7] are only transmitted when the local state of the networks changes. This functionality dramatically reduces the number of exchanges of information.…”

Section: Introductionmentioning

confidence: 99%

“…This functionality dramatically reduces the number of exchanges of information. In this study, we comprehensively investigate the routing performance of our strategy [6,7] if other types of centralities and other topologies of the networks are incorporated. Numerical experiments then show that our strategy using the congestion signals successfully mitigates the congestion in the communication network even if different types of centralities are incorporated, and various topologies of networks are applied to the communication networks.…”

Section: Introductionmentioning

confidence: 99%

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An effective routing strategy using congestion signaling for various types of communication networks

Hiraki

Adachi

Matsuura

et al. 2023

NOLTA

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Finding the shortest paths for packets in communication networks is one of the vital engineering challenges. Recently, a routing strategy using gravitational centrality has been proposed to find efficient transmitting routes for packets to their destinations. This routing strategy successfully mitigates the congestion of packets in the networks and effectively transmits many packets to their destinations by using gravitational centrality. However, this routing strategy is difficult to transmit the packets to their destinations if large volumes of packets flow in the communication networks because fixed costs are used to determine the shortest paths for every source-destination pair. To overcome these problems, we have already proposed a routing strategy using congestion signals. This routing strategy further mitigates the congestion of packets compared to conventional routing strategies such as the routing strategy with gravitational centrality. In this study, we comprehensively evaluate the routing strategy with the congestion signals if different types of centralities are incorporated into the costs of links and various topologies are applied to the communication network models. Numerical experiments illustrate that our routing strategy using the congestion signals mitigates the congestion of packets even if other types of centralities are incorporated. In addition, our proposed routing strategy shows effective transmitting performance for the various topologies of networks.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An effective routing strategy using congestion signaling for various types of communication networks

Hiraki

Adachi

Matsuura

et al. 2023

NOLTA

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“…On the other hand, as one of the effective metaheuristics, an algorithm using chaotic dynamics, or the chaotic search, has already been proposed to escape from undesirable local minima. The chaotic search shows good performance for solving various N P-hard combinatorial optimization problems, such as the traveling salesman problems [13][14][15][16], the quadratic assignment problems [17][18][19], the vehicle routing problems [20,21], the packet routing problems [22][23][24][25][26][27][28][29], and the motif extraction problems [30,31]. However, the Steiner tree problem in graphs has a different feature from the abovementioned combinatorial optimization problems; local searches we introduced in this paper produce not only feasible solutions but also infeasible solutions during the search.…”

Section: Introductionmentioning

confidence: 99%

Solving the Steiner tree problem in graphs by chaotic search

Kimura

Ikeguchi

2020

NOLTA

Self Cite

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The Steiner tree problem in graphs is an N P-hard combinatorial optimization problem. To solve the N P-hard combinatorial optimization problems, such as the traveling salesman problems, the quadratic assignment problems, and the vehicle routing problems, an algorithm for searching solutions by chaotic dynamics, or the chaotic search, exhibits good performance. From this viewpoint, this paper proposes an algorithm for solving the Steiner tree problem in graphs with chaotic dynamics. Comparing the performance of the chaotic search with that of the tabu search, we analyze the searching characteristics of the chaotic search. From the results of numerical experiments, we found that if parameters of the chaotic search are set to appropriate values, the chaotic search exhibits good performance and the chaotic search outputs optimal solutions more frequently than the tabu search during the searching processes, which implies that the chaotic search has higher ability than the tabu search as metaheuristics.

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“…To evaluate the congestion levels of the nodes, we employ a computer network model in which a fixed number of packets are flowing [23] [24] [28]. As in the references [23] [24] [28], we introduce a buffer constraint and a packet moving constraint. Packets are then removed from the communication network if they are transmitted to nodes that have full buffers.…”

Section: Introductionmentioning

confidence: 99%

Neural-Based Routing Method for Alleviating Congestion in Complex Networks

Kimura¹,

Takamizawa²,

Matsuura³

2016

AJOR

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In this paper, we propose a new packet routing strategy that incorporates memory information for reducing congestion in communication networks. First, we study the conventional routing strategy which selects the paths for transmitting packets to destinations using the distance information and the dynamical information such as the number of accumulating packets at adjacent nodes. Then, we evaluate the effectiveness of this routing strategy for the scale-free networks. From results of numerical simulations, we conclude that this routing strategy is not effective when the density of the packets increases due to the impermeability of the communication network. To avoid this undesirable problem, we incorporate memory information to the routing strategy. By using memory information effectively, packets are spread into the communication networks, achieving a higher performance than conventional routing strategies for various network topologies, such as scale-free networks, small-world networks, and scale-free networks with community structures.

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Neural-based routing strategy with transmission information for complex communication networks

Cited by 6 publications

References 15 publications

An effective routing strategy using congestion signaling for various types of communication networks

An effective routing strategy using congestion signaling for various types of communication networks

Solving the Steiner tree problem in graphs by chaotic search

Neural-Based Routing Method for Alleviating Congestion in Complex Networks

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