Node Degree-Aware Link Cost for Traffic Load-Distribution in Large-Scale Networks

2011 ◽  
Author(s):  
Hitomi Tamura ◽  
Mario Koppen ◽  
Masato Uchida ◽  
Masato Tsuru ◽  
Yuji Oie
Keyword(s):  
Load Distribution ◽  
Large Scale ◽  
Traffic Load ◽  
Node Degree ◽  
Link Cost ◽  
Large Scale Networks

Routing Metric Based on Node Degree for Load-Balancing in Large-Scale Networks

2011 ◽  
Author(s):  
Hitomi Tamura ◽  
Masato Uchida ◽  
Masato Tsuru ◽  
Jun'ichi Shimada ◽  
Takeshi Ikenaga ◽  
...  
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Node Degree ◽  
Routing Metric ◽  
Large Scale Networks

scholarly journals Node Degree Based Routing Metric for Traffic Load Distribution in the Internet

2013 ◽  
Vol E96.D (2) ◽  
pp. 202-212 ◽  
Author(s):  
Jun'ichi SHIMADA ◽  
Hitomi TAMURA ◽  
Masato UCHIDA ◽  
Yuji OIE
Keyword(s):  
Load Distribution ◽  
Traffic Load ◽  
Node Degree ◽  
The Internet ◽  
Routing Metric

scholarly journals Intelligent and Resizable Control Plane for Software Defined Vehicular Network : A Deep Reinforcement Learning Approach

2021 ◽  
Author(s):  
karima Smida ◽  
Hajer Tounsi ◽  
Mounir Frikha
Keyword(s):  
Reinforcement Learning ◽  
Network Architecture ◽  
Large Scale ◽  
Vehicular Network ◽  
Optimal Number ◽  
Traffic Load ◽  
Control Plane ◽  
Distributed Approach ◽  
Large Scale Networks ◽  
Application Requirements

Abstract Software-Defined Networking (SDN) has become one of the most promising paradigms to manage large scale networks. Distributing the SDN Control proved its performance in terms of resiliency and scalability. However, the choice of the number of controllers to use remains problematic. A large number of controllers may be oversized inducing an overhead in the investment cost and the synchronization cost in terms of delay and traffic load. However, a small number of controllers may be insufficient to achieve the objective of the distributed approach. So, the number of used controllers should be tuned in function of the traffic charge and application requirements. In this paper, we present an Intelligent and Resizable Control Plane for Software Defined Vehicular Network architecture (IRCP-SDVN), where SDN capabilities coupled with Deep Reinforcement Learning (DRL) allow achieving better QoS for Vehicular Applications. Interacting with SDVN, DRL agent decides the optimal number of distributed controllers to deploy according to the network environment (number of vehicles, load, speed etc.). To the best of our knowledge, this is the first work that adjusts the number of controllers by learning from the vehicular environment dynamicity. Experimental results proved that our proposed system outperforms static distributed SDVN architecture in terms of end-to-end delay and packet loss.

An Efficient Cluster-Based Flooding Using Fuzzy Logic Scheme (CBF2S) In Mobile Adhoc Networks

2020 ◽  
Vol 14 ◽  
Author(s):  
S. Mahima ◽  
N. Rajendran
Keyword(s):  
Fuzzy Logic ◽  
Ad Hoc ◽  
Link Quality ◽  
Traffic Load ◽  
Node Degree ◽  
Communication Overhead ◽  
Delivery Ratio ◽  
Node Mobility ◽  
Centralized Control ◽  
Hop Count

: Mobile ad hoc networks (MANET) hold a set of numerous mobile computing devices useful for communication with one another with no centralized control. Due to the inherent features of MANET such as dynamic topology, constrained on bandwidth, energy and computing resources, there is a need to design the routing protocols efficiently. Flooding is a directive for managing traffic since it makes use of only chosen nodes for transmitting data from one node to another. This paper intends to develop a new Cluster-Based Flooding using Fuzzy Logic Scheme (CBF2S). To construct clusters and choose proper cluster heads (CHs), thefuzzy logic approach is applied with the use of three parameters namely link quality, node mobility and node degree. The presented model considerably minimizes the number of retransmissions in the network. The presented model instructs the cluster members (CM) floods the packets inside a cluster called intra-cluster flooding and CHs floods the packets among the clusters called inter-cluster flooding. In addition, the gateway sends a packet to another gateway for minimizing unwanted data retransmissions when it comes under different CH. The presented CBF2S is simulated using NS2 tool under the presence of varying hop count. The CBF2S model exhibits maximum results over the other methods interms of overhead, communication overhead, traffic load, packet delivery ratio and the end to end delay.

scholarly journals Contribution of Ionotropic Glutamatergic Receptors to Excitability and Attentional Signals in Macaque Frontal Eye Field

2021 ◽  
Author(s):  
Miguel Dasilva ◽  
Christian Brandt ◽  
Marc Alwin Gieselmann ◽  
Claudia Distler ◽  
Alexander Thiele
Keyword(s):  
Attentional Control ◽  
Large Scale ◽  
Neuronal Excitability ◽  
Cell Types ◽  
Receptor Activation ◽  
Frontal Eye Field ◽  
Control Signals ◽  
Cognitive Operations ◽  
Eye Field ◽  
Large Scale Networks

Abstract Top-down attention, controlled by frontal cortical areas, is a key component of cognitive operations. How different neurotransmitters and neuromodulators flexibly change the cellular and network interactions with attention demands remains poorly understood. While acetylcholine and dopamine are critically involved, glutamatergic receptors have been proposed to play important roles. To understand their contribution to attentional signals, we investigated how ionotropic glutamatergic receptors in the frontal eye field (FEF) of male macaques contribute to neuronal excitability and attentional control signals in different cell types. Broad-spiking and narrow-spiking cells both required N-methyl-D-aspartic acid and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor activation for normal excitability, thereby affecting ongoing or stimulus-driven activity. However, attentional control signals were not dependent on either glutamatergic receptor type in broad- or narrow-spiking cells. A further subdivision of cell types into different functional types using cluster-analysis based on spike waveforms and spiking characteristics did not change the conclusions. This can be explained by a model where local blockade of specific ionotropic receptors is compensated by cell embedding in large-scale networks. It sets the glutamatergic system apart from the cholinergic system in FEF and demonstrates that a reduction in excitability is not sufficient to induce a reduction in attentional control signals.

scholarly journals How does the position of firms in the supply chain affect their performance? An empirical study

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Siddharth Arora ◽  
Alexandra Brintrup
Keyword(s):  
Supply Chain ◽  
Empirical Data ◽  
Large Scale ◽  
Individual Performance ◽  
Supply Network ◽  
Supply Chain Networks ◽  
Focal Firm ◽  
Cooperative Competition ◽  
Large Scale Networks ◽  
The Relationship

AbstractThe relationship between a firm and its supply chain has been well studied, however, the association between the position of firms in complex supply chain networks and their performance has not been adequately investigated. This is primarily due to insufficient availability of empirical data on large-scale networks. To addresses this gap in the literature, we investigate the relationship between embeddedness patterns of individual firms in a supply network and their performance using empirical data from the automotive industry. In this study, we devise three measures that characterize the embeddedness of individual firms in a supply network. These are namely: centrality, tier position, and triads. Our findings caution us that centrality impacts individual performance through a diminishing returns relationship. The second measure, tier position, allows us to investigate the concept of tiers in supply networks because we find that as networks emerge, the boundaries between tiers become unclear. Performance of suppliers degrade as they move away from the focal firm (i.e., Toyota). The final measure, triads, investigates the effect of buying and selling to firms that supply the same customer, portraying the level of competition and cooperation in a supplier’s network. We find that increased coopetition (i.e., cooperative competition) is a performance enhancer, however, excessive complexity resulting from being involved in both upstream and downstream coopetition results in diminishing performance. These original insights help understand the drivers of firm performance from a network perspective and provide a basis for further research.

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