Prediction of Network Traffic Load on High Variability Data Based on Distance Correlation

Author(s):  
Lo Pang-Yun Ting ◽  
Tiago Koketsu Rodrigues ◽  
Nei Kato ◽  
Kun-Ta Chuang
Author(s):  
Dimitar Radev ◽  
Izabella Lokshina ◽  
Svetla Radeva

The paper examines self-similar properties of real telecommunications network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Simulation with stochastic and long range dependent traffic source models is performed, and the algorithms for buffer overflow simulation for finite buffer single server model under self-similar traffic load SSM/M/1/B are explained. The algorithms for modeling fixed-length sequence generators that are used to simulate self-similar behavior of wireless IP network traffic are developed and applied. Numerical examples are provided, and simulation results are analyzed.


Author(s):  
Takeshi Kitahara ◽  
Shuichi Nawata ◽  
Masaki Suzuki ◽  
Norihiro Fukumoto ◽  
Shigehiro Ano

2012 ◽  
Vol 23 (02) ◽  
pp. 1250016 ◽  
Author(s):  
ZHONG-YUAN JIANG ◽  
MAN-GUI LIANG

Since the betweenness of nodes in complex networks can theoretically represent the traffic load of nodes under the currently used routing strategy, we propose an improved efficient (IE) routing strategy to enhance to the network traffic capacity based on the betweenness centrality. Any node with the highest betweenness is susceptible to traffic congestion. An efficient way to improve the network traffic capacity is to redistribute the heavy traffic load from these central nodes to non-central nodes, so in this paper, we firstly give a path cost function by considering the sum of node betweenness with a tunable parameter β along the actual path. Then, by minimizing the path cost, our IE routing strategy achieved obvious improvement on the network transport efficiency. Simulations on scale-free Barabási–Albert (BA) networks confirmed the effectiveness of our strategy, when compared with the efficient routing (ER) and the shortest path (SP) routing.


2014 ◽  
Vol 23 (4) ◽  
pp. 437-450 ◽  
Author(s):  
Yi Xie ◽  
Xilong Sun ◽  
Pengfei Yuan ◽  
Xijian Chen

AbstractWireless devices consume large amounts of energy during wireless communication. As the energy storage of battery is limited, improving energy efficiency has become an important approach to prolong the lifetime of devices. The IEEE 802.11 protocol supports the power save mode (PSM) in wireless local area networks (WLANs). However, the standard PSM cannot adapt to the changes of traffic load or channel conditions. Therefore, this article proposes an adaptive traffic-aware PSM mechanism (APSM) that improves energy efficiency of wireless devices in a WLAN with an access point (AP). According to the current channel condition and traffic load, the AP adjusts the interval of beacons that give devices different priorities to fetch buffered packets. The devices can adaptively adjust listening intervals according to network traffic, and adopt different congestion backoff timers when channel collisions happen or the network topology changes. The APSM has been implemented and evaluated in NS-2. The simulation results have shown that devices using the APSM can improve energy efficiency by 115% at most compared with the ones using the standard PSM. The benefit of adaptive beacon interval and listening intervals is significant, while the improvement due to the adaptive backoff timer is minor. The improvement of the APSM over the PSM is more significant when the network traffic level decreases and the ratio of idle power to sleeping power increases. Additionally, the APSM increases the delay of data frames within a limited range, which does not bring any bad effect on network throughput.


2014 ◽  
Vol 16 (3) ◽  
pp. 84-87
Author(s):  
Jopxon P J ◽  
◽  
Arun Soman

Author(s):  
Benhui Chen ◽  
◽  
Jinglu Hu ◽  
Lihua Duan ◽  
Yinglong Gu ◽  
...  

In this research we design a network administrator assistance system based on traffic measurement and fuzzy c-means (FCM) clustering analysis method. Network traffic measurement is an essential tool for monitoring and controlling communication network. It can provide valuable information about network traffic-load patterns and performances. The proposed system utilizes the FCM method to analyze users' network behaviors and traffic-load patterns based on traffic measurement data of IP network. Analysis results can be used as assistance for administrator to determine efficient controlling and configuring parameters of network management systems. The system is applied in Dali University campus network, and it is effective in practice.


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