information visualization and traffic prediction analysis...
TRANSCRIPT
Information Visualization and Traffic Prediction Analysis Platform Development in Electric Power
Communication Network
WeiDong Feng***, Yong Sun *,**, Ran Zhan*, ZhenChao Sun*, Geng Zhang****, ShiDong Liu**** * School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, China
** Beijing Key Laboratory of Network System Architecture and Convergence, China *** State Grid Hubei electric power company, China **** China Electric Power Research Institute, China
Abstract—In order to present the status of the electric power communication network, information collection, presentation and business flow analysis prediction technology are discussed in this project. The realized platform can show the integration of data visualization image rendering and give the business flow prediction. Keywords—Information visualization, traffic prediction, platform development, electric power, communication network
I. INTRODUCTION Communication network of electric power
industry provided transmission and data channels for the electric power production and management. According to business attribute classification can be roughly divided into two major categories, production and management. With the rapid development of new energy, such as solar, wind, biomass, tidal power, etc. A distributed energy supply system becomes the future development trend[1].
Distributed power supply system contains many scattered distributed power supply, and species tend to more than one. With the energy storage device and the load balance device of power, the structure could be quite complex. This would require the development of the information-center smart grid and the energy Internet[2].
In order to present the status of the electric power communication network, information collection, presentation and business flow analysis prediction technology are researched in this project. Mainly to solve three problems in the electric communication
network:(1) The status of the network operation can't been known. Including how many application in network; which application consumes most of the bandwidth resources; (2)The network can't be effective to control. (3) The network bottleneck problem can't be effectively solved.
Figure 1. The rapid development of new energy
II. BACKGROUND AND PRESENT SITUATION In recent years, with the continuous development
of power grid, electric power communication network as management, operation and control information of power grid is developing fast. Due to the construction of electric power communication network is mainly in accordance with a system of the planning stage, stage construction, is not considered as the characteristics of the network itself and optimization design, and different investment main body, to a certain extent influence the whole performance of the electric power communication network, redundant construction, poor equipment compatibility and risk scattered points hidden, network not give full play to the function. In order to further improve the reliability
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of communication network and business carrying capacity, the electric power communication network risk analysis evaluation work is necessary, through the assessment, timely detection and early warning of network risk point, so as to realize the optimized allocation of network resources, and further enhance the safety, reliability and transmission efficiency of the network.
Present Situation in the electric communication network now can be summarized:
(1) The status of the network operation can't been known. Including: Do not understand what are mainly on the network application in operation; Do not know how many application is running, which application consumes most of the bandwidth resources; Do not have the network running history data, and could not forecast the trend.
(2)The network cannot be effective to control. Including: P2P and streaming media applications consume the bandwidth resources, but can't be effective controlled; key business can't be guaranteed; network quality of key users can't get effective guarantee.
(3) The network bottleneck problem can't be effectively solved. LAN scale is more and more big, the access speed faster and faster. But WAN speed does not increases with LAN. In WAN, the contradictions of the three aspects of the user, application and bandwidth can't completely solve.
In the early research of network traffic, traffic modeling are mainly Poisson distribution and the basis of markov process[3], using the public switched telephone network (PSTN) flow mode and Poisson model to describe traffic data. With the development of network technology and the increasing of the network business, feature of self-similar have been found by Leland widely exists in every network traffic[4,5]. Because of the influence of the self-similar characteristic of network is very huge, therefore has the attention of the researchers, a variety of self-similar model is established to describe and simulation flow characteristic, such as fractal Brownian motion model, multi-fractal wavelet model, and so on[6].
III. SYSTEM DESIGN AND PLATFORM DEVELOPMENT The key technology in the project are data
visualization technology, large data analysis, data
mining technology, and engine technology based on the strategy of business flow analysis, traffic prediction and early warning mechanism. Project of technology innovation points are: integration of data visualization image rendering technology, analysis of data mining technology, the business flow prediction technology, etc.
The project uses the mode of combining software and hardware. The hardware uses the sensors to collect data, and upload to server. Software part is independent developed with server. Through the project implementation, the platform can show data visualization, and by the depth data mining, a phase of the data flow is predicted with the future trend.
Figure 2. The mode of combining software and hardware
A. Actual Network Traffic Flow Features Through the observation and analysis, which is
found that the actual flow has the following several important features: (1) the sudden (also called a peak), the business flow of most evident in the small time scale. (2) long related with long-range dependence, with the passage of time, at different time scales of business flow sequence have the same statistical properties, namely theory with statistical properties; (3) cyclical, also known as seasonal, flow sequence for a long time also reflects a cyclical change; (4) chaotic, seemingly without rules similar to random phenomenon.
To conduct business visual analysis and traffic prediction, the network business flow analysis is carried on firstly. The research points are: 1) the network node port traffic analysis. Namely the network node equipment port inflows and outflows of packets of information statistics. It includes the number of packets, the number of bytes, package size distribution and the number of packet loss a lot of statistical information. Through the analysis of the port traffic network node, the basic status of network can be known; 2) end-to-end IP traffic
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analysis. In the network layer from a source to a destination IP packet statistics. With the analysis of it, we can understand the purpose of users to have access to network. This is the important basis of network analysis, planning, design and optimization; 3) the business layer flow analysis. the analysis of the transport layer port information, and the type information application service, with the use of these information a more detailed analysis can be done; 4) completely user business data flow analysis, to improve network security.
B. Status of the Network Operation Based on multimode traffic acquisition flow
measurement methods are designed. According to the characteristics of the monitoring network, the flow acquisition method can be changed flexibly, which could support multiple flow data format capture, like packet level, NetFlow and sFlow, etc. With acquisition traffic flow measurement methods, the system could collect high efficient and accurate the network traffic, analyse the characteristic of network, master the network running status.
Figure 3. The network traffic acquisition and flow measurement
C. Effective Traffic Detect and Control Using data mining technology based on the flow
characteristics of P2P network traffic identification scheme are designed. By real-time traffic attribute characteristics calculation system, the different flow rate and the statistical properties based on the analysis of the data packets are calculated[8]. With the characteristic values of several attributes, method is chosen to detect P2P traffic effectively, and apply the data mining algorithm based on the flow properties selection for validation.
D. Network Bottleneck Prediction Flow prediction is studied according the above
characteristics, obtaining reliable and efficient data, statistics, and model analysis. Steady flow model is divided into two types[6,7]: short and long range dependence. Short dependence model includes Markov process and the Auto Regressive model (AR), Auto Regressive Moving Average (ARMA) model, and the Auto Regressive Integrated Moving Average (ARIMA) model, etc. Long range dependence model includes Fractal Auto Regressive Moving Average model (F-ARIMA) and Fractal Brownian Motion (FBM) model, etc.
Figure 4. The long range dependence and self-similarity
Based on the traffic sudden feature, long range dependence, periodic and chaotic, through statistical analysis of all kinds of business flow data in electric power communication network, many kinds of characteristics of business obtained. According to various complex business scenarios, using Markov process, ARMA model, F-ARIMA and FBM model and so on, flow evaluation model of the electric power communication service system is formed. Demand for business flow, bandwidth, QoS requirements, survivability index modeling, electric power communication network traffic prediction model is established and implemented. Short and long range dependence of the existing business, business stability are evaluated, to achieve acceptable algorithm complexity. The validity is verified by simulation and the real software system platform. Based on self-similarity traffic data, used to distinguish the specific business types of traffic data, combining with the different scenarios, using intelligent forecast model and combined forecasting method, a flexible flow prediction model is developed.
The existing network traffic management platform refers only to the historical data
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visualization. data for real-time data, in the future no anticipation, not involves the function of the network to predict. Through the project implementation, which can realize data visualization, and by the depth of the data mining, a phase of the data flow to predict the future trend.
Figure 5. The flexible flow prediction of network
E. System Platform Development Figures 6 is the realization of data visualization.
From the figure, we can get the characters of network flow. The first one shows the traffic sudden feature. At the time of 23:00-7:00, a sudden data flow is generated. The second figure can show the short range dependence. The flow have the same trend in short time, but changed in a longer time interval. And the Figures 7 shows the chaotic. In 0:00 and 12:00, break happened without continuous trend. Figures 8 show the interface of system platform. In the platform, the flow, CPU, process and service are recorded. With the visualization image rendering, we can directly perceive the trend through the senses.
Figure 6. Example of the realization of data visualization
Figure 7. The visualization of chaotic data
Figure 8. The interface of system platform
IV. CONCLUSIONS In this project, the system platform is
development combining software and hardware. The Software part is designed to show the status of the electric power communication network. Information collection, presentation and business flow analysis prediction technology are discussed. The realized platform can show the integration of data visualization image rendering and give the business flow prediction.
V. ACKNOWLEDGMENT This work was supported by Science and
Technology Projects of the State Grid Corporation of China (XXN17201400030), State Grid Hubei Electric Power Company, NSFC (No.61101106), and Research Innovation Fund for College Students of Beijing University of Posts and Telecommunications.
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[3] Scott, Steven L., and Smyth, Padhraic. "The Markov Modulated Poisson Process and Markov Poisson Cascade with Applications to Web Traffic Modeling." Bayesian Statistics, pp.671-680, 2003.
[4] Victor S. Frost; Benjamin Melamed. "Traffic Modeling for Telecommunications Networks". IEEE Communications, vol.32 pp.70-81, 1994.
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[5] Abdelnaser Adas. "Traffic Models in Broadband Networks". IEEE Communications Magazine, vol 35, pp. 82-89, 1997.
[6] POP-Yager. C.Quek; A.Singh. "A novel self-organizing fuzzy neural network based on the Yager iferece". Expert Systems with Application, vol 29, pp. 229-242, 2005.
[7] Takahashi, Y., Aida, H., Saito, T."ARIMA model's superiority over f-ARIMA model". International Conference on Communication Technology. Vol.1, pp.66 - 69, 2000.
[8] Vapnik V N. Statistical Learning Theory. New York: New York Wiley,1998.
Weidong Feng received the master degree from Wuhan University, Wuhan, China, in 2009. He is currently a senior engineer of information and communication center of Hubei electric power company. His current work include the maintenance and management of communication network. His current research interests include image processing. Yong Sun (M’12) received the Ph.D. degree from Beiijng University of Posts Telecommunications, Beijing, China, in 2008. He is currently a Lecturer with the School of information and communication engineering, Beiijng University of Posts Telecommunications, Beijing, China. He became a Member (M) of IEEE in 2012. His current research interests include heterogeneous networks, wireless resource allocation, and network management.
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