scholarly journals A New Algorithm for Controller Placement in SDN

2019 ◽  
Vol 8 (6) ◽  
pp. 1196-1201
Keyword(s):  
Large Scale ◽  
Optimum Number ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
And Performance ◽  
Multiple Controller ◽  
And Control ◽  
Multiple Scenarios ◽  
Installation Time

SDN network supports centralized network management by splitting control plane and data plane of forwarding devices and places the network intelligence in a software entity called controller. The controller can be placed in selective places of network to effectively monitor and control network activities. Large scale network needs multiple controller to manage control activities of network. In order to identify the optimum number of controllers and its effective locations in the network, a new algorithm is proposed using cut-vertex concept from graph theory. The proposed algorithm is simulated using Mininet SDN emulator. To study the performance of the proposed algorithm, multiple scenarios were used in the simulation and performance was analysed using parameters viz., flow installation time, average latency of network, throughput.

scholarly journals Synthesizing Brain-network-inspired Interconnections for Large-scale Network-on-chips

2022 ◽  
Vol 27 (1) ◽  
pp. 1-30
Author(s):  
Mengke Ge ◽  
Xiaobing Ni ◽  
Xu Qi ◽  
Song Chen ◽  
Jinglei Huang ◽  
...  
Keyword(s):  
Large Scale ◽  
Small World ◽  
Brain Network ◽  
Graph Processing ◽  
Hop Count ◽  
Scale Free ◽  
Large Scale Network ◽  
Average Latency ◽  
Scale Network ◽  
The Brain

Brain network is a large-scale complex network with scale-free, small-world, and modularity properties, which largely supports this high-efficiency massive system. In this article, we propose to synthesize brain-network-inspired interconnections for large-scale network-on-chips. First, we propose a method to generate brain-network-inspired topologies with limited scale-free and power-law small-world properties, which have a low total link length and extremely low average hop count approximately proportional to the logarithm of the network size. In addition, given the large-scale applications, considering the modularity of the brain-network-inspired topologies, we present an application mapping method, including task mapping and deterministic deadlock-free routing, to minimize the power consumption and hop count. Finally, a cycle-accurate simulator BookSim2 is used to validate the architecture performance with different synthetic traffic patterns and large-scale test cases, including real-world communication networks for the graph processing application. Experiments show that, compared with other topologies and methods, the brain-network-inspired network-on-chips (NoCs) generated by the proposed method present significantly lower average hop count and lower average latency. Especially in graph processing applications with a power-law and tightly coupled inter-core communication, the brain-network-inspired NoC has up to 70% lower average hop count and 75% lower average latency than mesh-based NoCs.

Study on Message Transmission Technology Based on Dynamic Priority

2012 ◽  
Vol 263-266 ◽  
pp. 1759-1763
Author(s):  
Jia Si Wang ◽  
Song Yang Du ◽  
Xiong Gao Wang ◽  
Di Ming Ai
Keyword(s):  
Large Scale ◽  
Transmission Efficiency ◽  
Transmission Mechanism ◽  
Message Queue ◽  
Dynamic Priority ◽  
Message Transmission ◽  
Large Scale Network ◽  
Scale Network ◽  
Military Command ◽  
And Control

Through analyzing the unique requirements of military command and control software and the characteristics of wireless message transmission, this paper studies a new message transmission mechanism based on wireless narrow band transmission, bring forward the proposal of improving wireless message transmission efficiency through improving the message queue transmission mechanism of the software, and verifies it in the new software version. The results show that new transmission technology greatly improves large-scale network transmission effect.

Large-Scale Network Analysis for Online Social Brand Advertising

MIS Quarterly ◽  
2016 ◽  
Vol 40 (4) ◽  
pp. 849-868 ◽  
Author(s):  
Kunpeng Zhang ◽  
◽  
Siddhartha Bhattacharyya ◽  
Sudha Ram ◽  
◽  
...  
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Large Scale Network ◽  
Brand Advertising ◽  
Scale Network

Attention Biases Visual Activity in Visual Short-term Memory

2014 ◽  
Vol 26 (7) ◽  
pp. 1377-1389 ◽  
Author(s):  
Bo-Cheng Kuo ◽  
Mark G. Stokes ◽  
Alexandra M. Murray ◽  
Anna Christina Nobre
Keyword(s):  
Large Scale ◽  
Short Term Memory ◽  
Control Mechanisms ◽  
Top Down ◽  
Short Term ◽  
Visual Activity ◽  
Visual Short Term Memory ◽  
Large Scale Network ◽  
Visual Areas ◽  
Scale Network

In the current study, we tested whether representations in visual STM (VSTM) can be biased via top–down attentional modulation of visual activity in retinotopically specific locations. We manipulated attention using retrospective cues presented during the retention interval of a VSTM task. Retrospective cues triggered activity in a large-scale network implicated in attentional control and led to retinotopically specific modulation of activity in early visual areas V1–V4. Importantly, shifts of attention during VSTM maintenance were associated with changes in functional connectivity between pFC and retinotopic regions within V4. Our findings provide new insights into top–down control mechanisms that modulate VSTM representations for flexible and goal-directed maintenance of the most relevant memoranda.

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