Real-time pricing for on-demand bandwidth reservation in SDN-enabled networks

Kahn at Penn ◽  
2017 ◽  
Author(s):  
Bo Gu ◽  
Mianxiong Dong ◽  
Cheng Zhang ◽  
Zhi Liu ◽  
Yoshiaki Tanaka
Keyword(s):  
Real Time ◽  
Bandwidth Reservation ◽  
On Demand ◽  
Real Time Pricing

On Demand Bandwidth Reservation for Real-Time Traffic in Cellular IP Network using Particle Swarm Optimization

2011 ◽  
pp. 215-228 ◽  
Author(s):  
Mohammad Anbar ◽  
D.P. Vidyarthi
Keyword(s):  
Particle Swarm Optimization ◽  
Real Time ◽  
Particle Swarm ◽  
Bandwidth Reservation ◽  
Swarm Optimization ◽  
Available Bandwidth ◽  
On Demand ◽  
Real Time Traffic ◽  
Ip Network ◽  
Cellular Ip

Cellular IP network deals with micro mobility of the mobile devices. An important challenge in wireless communication, especially in cellular IP based network, is to provide good Quality of Service (QoS) to the users in general and to the real-time users (users involved in the exchange of real-time packets) in particular. Reserving bandwidth for real time traffic to minimize the connection drop (an important parameter) is an activity often used in Cellular IP network. Particle Swarm Optimization (PSO) algorithm simulates the social behavior of a swarm or flock to optimize some characteristic parameter. PSO is effectively used to solve many hard optimization problems. The work, in this paper, proposes an on demand bandwidth reservation scheme to improve Connection Dropping Probability (CDP) in cellular IP network by employing PSO. The swarm, in the model, consists of the available bandwidth in the seven cells of the cellular IP network. The anytime bandwidth demand for real-time users is satisfied by the available bandwidth of the swarm. The algorithm, used in the model, searches for the availability of the bandwidth and reserves it in the central cell of the swarm. Eventually, it will allocate it on demand to the cell that requires it. Simulation experiments reveal the efficacy of the model.

scholarly journals Optimal Real-Time Pricing of Electricity Based on Demand Response

2019 ◽  
Vol 159 ◽  
pp. 304-308 ◽  
Author(s):  
Jiangfeng Jiang ◽  
Yu Kou ◽  
Zhaohong Bie ◽  
Gengfeng Li
Keyword(s):  
Real Time ◽  
Demand Response ◽  
On Demand ◽  
Real Time Pricing

Real-Time Routing Based on On-Demand Multi-Hop Lookahead in Wireless Sensor Networks

2011 ◽  
Vol E94-B (2) ◽  
pp. 569-572
Author(s):  
Soochang PARK ◽  
Euisin LEE ◽  
Juhyun JUNG ◽  
Sang-Ha KIM
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Real Time ◽  
Wireless Sensor ◽  
On Demand
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