• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.13 no.6
• /
• pp.2964-2985
• /
• 2019

Global data center IP traffic is expected to reach 20.6 zettabytes (ZB) by the end of 2021. Intra-data center networks (Intra-DCN) will account for 71.5% of the data center traffic flow and will be the largest portion of the traffic. The understanding of traffic distribution in IntraDCN is still sketchy. It causes significant amount of bandwidth to go unutilized, and creates avoidable choke points. Conventional transport protocols such as Optical Packet Switching (OPS) and Optical Burst Switching (OBS) allow a one-sided view of the traffic flow in the network. This therefore causes disjointed and uncoordinated decision-making at each node. For effective resource planning, there is the need to consider joining the distributed with centralized management which anticipates the system's needs and regulates the entire network. Methods derived from Kalman filters have proved effective in planning road networks. Considering the network available bandwidth as data transport highways, we propose an intelligent enhanced SDN concept applied to OBS architecture. A management plane (MP) is added to conventional control (CP) and data planes (DP). The MP assembles the traffic spatio-temporal parameters from ingress nodes, uses Kalman filtering prediction-based algorithm to estimate traffic demand. Prior to packets arrival at edges nodes, it regularly forwards updates of resources allocation to CPs. Simulations were done on a hybrid scheme (1+1) and on the centralized OBS. The results demonstrated that the proposition decreases the packet loss ratio. It also improves network latency and throughput-up to 84 and 51%, respectively, versus the traditional scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.15 no.10
• /
• pp.3858-3874
• /
• 2021

As an essential part of the urban transportation system, precise perception of the traffic flow parameters at the traffic signal intersection ensures traffic safety and fully improves the intersection's capacity. Traditional detection methods of road traffic flow parameter can be divided into the micro and the macro. The microscopic detection methods include geomagnetic induction coil technology, aerial detection technology based on the unmanned aerial vehicles (UAV) and camera video detection technology based on the fixed scene. The macroscopic detection methods include floating car data analysis technology. All the above methods have their advantages and disadvantages. Recently, indoor location methods based on wireless signals have attracted wide attention due to their applicability and low cost. This paper extends the wireless signal indoor location method to the outdoor intersection scene for traffic flow parameter estimation. In this paper, the detection scene is constructed at the intersection based on the received signal strength indication (RSSI) ranging technology extracted from the wireless signal. We extracted the RSSI data from the wireless signals sent to the road side unit (RSU) by the vehicle nodes, calibrated the RSSI ranging model, and finally obtained the traffic flow parameters of the intersection entrance road. We measured the average speed of traffic flow through multiple simulation experiments, the trajectory of traffic flow, and the spatiotemporal map at a single intersection inlet. Finally, we obtained the queue length of the inlet lane at the intersection. The simulation results of the experiment show that the RSSI ranging positioning method based on wireless signals can accurately estimate the traffic flow parameters at the intersection, which also provides a foundation for accurately estimating the traffic flow state in the future era of the Internet of Vehicles.

• The Korean Journal of Applied Statistics
• /
• v.21 no.6
• /
• pp.1037-1044
• /
• 2008

In this paper, we show the performance of the power transformation GARCH(PGARCH) model to analyze the internet traffic data. The long memory property which is the typical characteristic of internet traffic data can be explained by the PGARCH model rather than the linear GARCH model. Small simulation and the analysis of the real internet traffic show the out-performance of the PARCH MODEL over the linear GARCH one.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.2
• /
• pp.841-854
• /
• 2020

The paper explains the method to process, analyze and predict traffic patterns in Los Angeles county using Big Data and Machine Learning. The dataset is used from a popular navigating platform in the USA, which tracks information on the road using connected users' devices and also collects reports shared by the users through the app. The dataset mainly consists of information about traffic jams and traffic incidents reported by users, such as road closure, hazards, accidents. The major contribution of this paper is to give a clear view of how the large-scale road traffic data can be stored and processed using the Big Data system - Hadoop and its ecosystem (Hive). In addition, analysis is explained with the help of visuals using Business Intelligence and prediction with classification machine learning model on the sampled traffic data is presented using Azure ML. The process of modeling, as well as results, are interpreted using metrics: accuracy, precision and recall.

• Journal of Internet Computing and Services
• /
• v.22 no.5
• /
• pp.9-16
• /
• 2021

Recently, the traffic volume of mobile communications increases rapidly and the small-cell is one of the solutions using two offload schemes, i.e., local IP access (LIPA) and selected IP traffic offload (SIPTO), to reduce the end-to-end delay and amount of mobile data traffic in the core network (CN). However, 3GPP describes the concept of LIPA and SIPTO and there is no decision algorithm to decide the path from source nodes (SNs) to destination nodes (DNs). Therefore, this paper proposes a dynamic mobile data traffic offload scheme using small-cells to decide the path based on the SN and DN, i.e., macro user equipment, small-cell user equipment (SUE), and multimedia server, and type of the mobile data traffic for the real-time and non-real-time. Through analytical models, it is shown that the proposed offload scheme outperforms the conventional small-cell network in terms of the delay of end-to-end mobile data communications and probability of the mobile data traffic in the CN for the heterogeneous networks.

• Korean Management Science Review
• /
• v.19 no.1
• /
• pp.29-38
• /
• 2002

Due to the rapid increase In the Internet traffic volume, ISPs are faced with the definite need of the expansion of server capacity. In order to Provide prompt services for customers and still prevent excessive facility cost, it is critical to determine the optimum level of internet server capacity. The purpose of this Paper is to provide a simple but effective strategy on the expansion of servers capacity according to the increase in internet traffic. We model an internet server as an M/G/m/m queueing system and derive an efficient method to compute the loss probability which, In turn, Is used as a basis to determine proper server capacity. The Process of estimating the traffic parameter values at each server based on log data analysis is also given. All the procedures are numerically demonstrated through the process of analyzing actual log data collected from a game company.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.14 no.2
• /
• pp.115-121
• /
• 2021

This paper used big data and artificial intelligence technology to predict the rapidly increasing internet traffic. There have been various studies on traffic prediction in the past, but they have not been able to reflect the increasing factors that induce huge Internet traffic such as smartphones and streaming in recent years. In addition, event-like factors such as the release of large-capacity popular games or the provision of new contents by OTT (Over the Top) operators are more difficult to predict in advance. Due to these characteristics, it was impossible for an ISP (Internet Service Provider) to reflect real-time service quality management or traffic forecasts in the network business environment with the existing method. Therefore, in this study, in order to solve this problem, an Internet traffic collection system was constructed that searches, discriminates and collects traffic data in real time, separate from the existing NMS. Through this, the flexibility and elasticity to automatically register the data of the collection target are secured, and real-time network quality monitoring is possible. In addition, a large amount of traffic data collected from the system was analyzed by machine learning (AI) to predict future traffic of OTT operators. Through this, more scientific and systematic prediction was possible, and in addition, it was possible to optimize the interworking between ISP operators and to secure the quality of large-scale OTT services.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
• /
• v.9 no.4
• /
• pp.131-140
• /
• 2010

Recently, mobile wireless data traffic has been dramatically increased due to not only the popularization of digital convergence devices including smart phone, Net-book, and Tablet PC, but also the vitalization of wireless Internet related eco-systems such as AppStore. In addition, it is expected that a tremendous increase in mobile data is caused by the release of unlimited mobile data plans (flat-fee). In order to deal with such mobile data traffic explosion, it is necessary that network operators should make efforts to offload wireless data traffic. This paper reviews the condition of mobile wireless data traffic in domestic and international telecommunication industry and looks for various action plans to overcome the difficulty of network operators.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.5 no.2
• /
• pp.313-329
• /
• 2011

To provide a seamless network to customers, Internet service providers must promptly detect and control abnormal traffic. One approach is to shorten the traffic information measurement cycle. However, performance degradation is inevitable if traffic measurement servers merely shorten the cycle and measure all traffic. This paper presents a software architecture that can measure traffic more frequently without degrading performance by estimating the level of abnormal traffic. The algorithm in the architecture estimates the values of the interface group objects in MIB by using the IP group objects thereby reducing the number of measurements and the size of measured data. We evaluated this architecture on part of Internet service provider's IP network. When the traffic was measured 5 times more than before, the CPU usage and TPS of the proposed scheme was 7% and 41% less than that of the original scheme while the false positive rate and false negative rate were 3.2% and 2.7% respectively.

• IE interfaces
• /
• v.12 no.4
• /
• pp.625-634
• /
• 1999

On this study we selected some variable which affect on the estimated of the voice traffic, and estimated daily average traffic by years according to the variables. We applied nonlinear growth curve model to future traffic forecast with estimated historical traffic data. As a result of the forecasting, this study investigates the year in which the internet traffic goes far than the voice traffic.