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

DQR: An Efficient Deep Q-Based Routing Approach in Multi-Controller Software Defined WAN (SD-WAN)

2020 ◽  
Vol 20 (04) ◽  
pp. 2150002
Author(s):  
MANEL MAJDOUB ◽  
ALI EL KAMEL ◽  
HABIB YOUSSEF
Keyword(s):  
Load Balancing ◽  
Resource Utilization ◽  
Large Scale ◽  
Network Technology ◽  
Network Resources ◽  
Routing Optimization ◽  
Multi Agent ◽  
Large Scale Networks ◽  
Network Scalability ◽  
Better Than

Software Defined Networking (SDN) is a promising paradigm in the field of network technology. This paradigm suggests the separation between the control plane and the data plane which brings flexibility, efficiency and programmability to network resources. SDN deployment in large scale networks raises many issues which can be overcame using a collaborative multi-controller approaches. Such approaches can resolve problems of routing optimization and network scalability. In large scale networks, such as SD-WAN, routing optimization consists of achieving a trade-off between per-flow QoS, the load balancing in each domain as well as the resource utilization in inter-domain links. Multi-Agent Reinforcement Learning paradigm(MARL) is one of the most popular solutions that can be used to optimize routing strategies in SD-WAN. This paper proposes an efficient approach based on MARL which is able to ensure a load balancing among each network as well as optimized resource utilization of inter-domain links. This approach profits from our previous work, denoted SPFLR, and tries to balance the load of the whole network using Deep Q-Networks (DQN) algorithms. Simulation results show that the proposed solution performs better than parallel solutions such as BGP-based routing and random routing.

Multiple Path Load Balancing for TCP Large Scale Networks

2019 ◽  
Author(s):  
Konstantinos Paximadis ◽  
Giannis Tzimas ◽  
Anna Galanopoulou ◽  
Pavlos Kalpakioris ◽  
Zlatan Sabic
Keyword(s):  
Load Balancing ◽  
Large Scale ◽  
Multiple Path ◽  
Large Scale Networks

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.

Sign in / Sign up

Export Citation Format

Share Document