• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.9
• /
• pp.4144-4165
• /
• 2018

Software-defined networking (SDN) has emerged as a promising technology for network programmability and experiments. In this work, we focus on virtual network embedding in multiple controllers SDN network. In SDN virtualization environment, virtual SDN networks (vSDNs) operate on the shared substrate network and managed by their each controller, the placement and load of controllers affect vSDN embedding process. We consider controller placement, vSDN embedding, controller adjustment as a joint problem, together considering different quality of service (QoS) requirement for users, formulate the problem into mathematical models to minimize the average time delay of control paths, the load imbalance degree of controllers and embedding cost. We propose a heuristic method which places controllers and partitions control domains according to substrate SDN network, embeds different QoS constraint vSDN requests by corresponding algorithms, and migrates switches between control domains to realize load balance of controllers. The simulation results show that the proposed method can satisfy different QoS requirement of tenants, keep load balance between controllers, and work well in the acceptance ratio and revenue to cost ratio for vSDN embedding.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.62 no.2
• /
• pp.65-71
• /
• 2013

Nonlinear loads with harmonics exist in an actual power system where harmonic currents make voltage distortion. The sum of reactive power measured at individual load is different from the measured reactive power at a bus in a power system with linear and non-linear loads. In this study, ANN(artificial neural network) load modeling technique with consideration of harmonics is introduced for more accurate component load modeling and an impact coefficient is proposed for aggregation of component loads. Results of this research show more accurate load modeling method. Since precise data for power system analysis can be acquired, the proposed method will be used for power system planning and maintenance.

• Proceedings of the KIEE Conference
• /
• 1999.07c
• /
• pp.1522-1522
• /
• 1999

This paper proposes the methods of short-term load forecasting using Kohonen neural networks and back-propagation neural networks. Historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Normal days and holidays are forecasted. For load forecasting in summer, max-, and min-temperature data are included in neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation. (1993-1997)

• 한국정보통신설비학회:학술대회논문집
• /
• 2005.08a
• /
• pp.382-387
• /
• 2005

There are many studies on arbitrarily divisible load scheduling problem in a distributed computing network consisting of processors interconnected through communication links. It is not efficient to arbitrarily distribute the load that comes into the system. In this paper, how to schedule in case that arbitrarily indivisible load comes into the system is studied. Also, the cases of the divisible load mixed with the indivisible load that come into network were dealt with optimal load distribution in parallel processing system by scheduling applied to linear programming.

• Structural Engineering and Mechanics
• /
• v.34 no.2
• /
• pp.159-174
• /
• 2010

Elastic buckling load of perforated steel plates is typically predicted using the finite element or conjugate load/displacement methods. In this paper an artificial neural network (ANN)-based formula is presented for the prediction of the elastic buckling load of rectangular plates having a circular cutout. By using this formula, the elastic buckling load of perforated plates can be calculated easily without setting up an ANN platform. In this study, the center of a circular cutout was chosen at different locations along the longitudinal x-axis of plates subjected to linearly varying loading. The results of the finite element method (FEM) produced by the commercial software package ANSYS are used to train and test the network. The accuracy of the proposed formula based on the trained ANN model is evaluated by comparing with the results of different researchers. The results show that the presented ANN-based formula is practical in predicting the elastic buckling load of perforated plates without the need of an ANN platform.

• Journal of the Korea Convergence Society
• /
• v.9 no.5
• /
• pp.1-6
• /
• 2018

In smart grid an accurate load forecasting is crucial in planning resources, which aids in improving its operation efficiency and reducing the dynamic uncertainties of energy systems. Research in this area has included the use of shallow neural networks and other machine learning techniques to solve this problem. Recent researches in the field of computer vision and speech recognition, have shown great promise for Deep Neural Networks (DNN). To improve the performance of daily electric peak load forecasting the paper presents a new deep neural network model which has the architecture of two multi-layer neural networks being serially connected. The proposed network model is progressively pre-learned layer by layer ahead of learning the whole network. For both one day and two day ahead peak load forecasting the proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange (KPX).

• ETRI Journal
• /
• v.29 no.2
• /
• pp.255-257
• /
• 2007

We propose an improved selective randomized load balancing (ISRLB) robust scheme under the hose uncertainty model for a special double-hop routing network architecture. The ISRLB architecture maintains the resilience properties of Valiant's load balancing and reduces the network cost/propagation delay in all other robust routing schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.9
• /
• pp.3539-3558
• /
• 2015

This paper investigates and analyzes the default block placement strategy of HDFS. HDFS is a typical representative distributed file system to stream vast amount of data effectively at high bandwidth to user applications. However, the default HDFS block placement policy assumes that all nodes in the cluster are homogeneous, and places blocks with a simple RoundRobin strategy without considering any nodes' resource characteristics, which decreases self-adaptability of the system. The primary contribution of this paper is the proposition of a network load sensitive block placement strategy. We have implemented our algorithm and justify it through extensive simulations and comparison with similar existing studies. The results indicate that our work not only performs much better in the data distribution but also improves write performance more significantly than the others.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.53 no.10
• /
• pp.529-535
• /
• 2004

Load forecasting is essential in the electricity market for the participants to manage the market efficiently and stably. A wide variety of techniques/algorithms for load forecasting has been reported in many literatures. These techniques are as follows: multiple linear regression, stochastic time series, general exponential smoothing, state space and Kalman filter, knowledge-based expert system approach (fuzzy method and artificial neural network). These techniques have improved the accuracy of the load forecasting. In recent 10 years, many researchers have focused on artificial neural network and fuzzy method for the load forecasting. In this paper, we propose an algorithm of a hybrid load forecasting method using fuzzy linear regression and general exponential smoothing and considering the sensitivities of the temperature. In order to consider the lower load of weekends and Monday than weekdays, fuzzy linear regression method is proposed. The temperature sensitivity is used to improve the accuracy of the load forecasting through the relation of the daily load and temperature. And the normal load of weekdays is easily forecasted by general exponential smoothing method. Test results show that the proposed algorithm improves the accuracy of the load forecasting in 1996.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.17 no.9
• /
• pp.2419-2435
• /
• 2023

Command and control networks(C2N) exhibit evident multi-network interdependencies owing to their complex hierarchical associations, interleaved communication links, and dynamic network changes. However, the existing command and control networks do not consider the effects of dependent nodes on the load distribution. Thus, we proposed a command and control networks load allocation strategy based on interdependence strength. First, a new measure of interdependence strength was proposed based on the edge betweenness, which was followed by proposing the inter-layer load allocation strategy based on the interdependence strength. Eventually, the simulation experiments of the aforementioned strategy were designed to analyze the network invulnerability with different initial load capacity parameters, allocation model parameters, and allocation strategies. The simulation indicates that the strategy proposed in this study improved the node survival rate of the interdependent command and control networks model and successfully prevented cascade failures.