Artificial immune based hybrid GA for QoS based multicast routing in large scale networks (AISMR)

2008 ◽  
Vol 31 (17) ◽  
pp. 3984-3994 ◽  
Author(s):  
K. Vijayalakshmi ◽  
S. Radhakrishnan
Keyword(s):  
Large Scale ◽  
Multicast Routing ◽  
Artificial Immune ◽  
Large Scale Networks

scholarly journals A Multicast Routing Scheme for the Internet: Simulation and Experimentation in Large-Scale Networks

2021 ◽  
Vol 11 (18) ◽  
pp. 8645
Author(s):  
Davide Careglio ◽  
Fernando Agraz ◽  
Dimitri Papadimitriou
Keyword(s):  
Large Scale ◽  
Multicast Routing ◽  
Multicast Tree ◽  
Comparative Performance ◽  
Memory Space ◽  
Multicast Protocol ◽  
Unicast Routing ◽  
Transmission Cost ◽  
Expected Performance ◽  
Large Scale Networks

With the globalisation of the multimedia entertainment industry and the popularity of streaming and content services, multicast routing is (re-)gaining interest as a bandwidth saving technique. In the 1990’s, multicast routing received a great deal of attention from the research community; nevertheless, its main problems still remain mostly unaddressed and do not reach the acceptance level required for its wide deployment. Among other reasons, the scaling limitation and the relative complexity of the standard multicast protocol architecture can be attributed to the conventional approach of overlaying the multicast routing on top of the unicast routing topology. In this paper, we present the Greedy Compact Multicast Routing (GCMR) scheme. GMCR is characterised by its scalable architecture and independence from any addressing and unicast routing schemes; more specifically, the local knowledge of the cost to direct neighbour nodes is enough for the GCMR scheme to properly operate. The branches of the multicast tree are constructed directly by the joining destination nodes which acquire the routing information needed to reach the multicast source by means of an incremental two-stage search process. In this paper we present the details of GCMR and evaluate its performance in terms of multicast tree size (i.e., the stretch), the memory space consumption, the communication cost, and the transmission cost. The comparative performance analysis is performed against one reference algorithm and two well-known protocol standards. Both simulation and emulation results show that GCMR achieves the expected performance objectives and provide the guidelines for further improvements.

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.

scholarly journals BFL: a node and edge betweenness based fast layout algorithm for large scale networks

2009 ◽  
Vol 10 (1) ◽  
pp. 19 ◽  
Author(s):  
Tatsunori B Hashimoto ◽  
Masao Nagasaki ◽  
Kaname Kojima ◽  
Satoru Miyano
Keyword(s):  
Large Scale ◽  
Layout Algorithm ◽  
Edge Betweenness ◽  
Large Scale Networks
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