• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1712-1732
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• 2011

Cloud provides dynamically scalable virtualized computing resources as a service over the Internet. To achieve higher resource utilization over virtualization technology, an optimized strategy that deploys virtual machines on physical machines is needed. That is, the total number of active physical host nodes should be dynamically changed to correspond to their resource usage rate, thereby maintaining optimum utilization of physical machines. In this paper, we propose a pattern-based prediction model for resource provisioning which facilitates best possible resource preparation by analyzing the resource utilization and deriving resource usage patterns. The focus of our work is on predicting future resource requests by optimized dynamic resource management strategy that is applied to a virtualized data center in a Cloud computing environment. To this end, we build a prediction model that is based on user request patterns and make a prediction of system behavior for the near future. As a result, this model can save time for predicting the needed resource amount and reduce the possibility of resource overuse. In addition, we studied the performance of our proposed model comparing with conventional resource provisioning models under various Cloud execution conditions. The experimental results showed that our pattern-based prediction model gives significant benefits over conventional models.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.5
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• pp.275-285
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• 2006

This paper proposes an efficient market mechanism-based resource transaction model for grid computing. This model predicts the next resource demand of users and suggests reasonable resource price for both of customers and resource providers. This model increases resource transactions between customers and resource providers and reduces the average of transaction response times from resource providers. For prediction accuracy improvement of resource demands and suggestion of reasonable resource price, this model introduces a statistics-based prediction model and a price decision model of microeconomics. For performance evaluating, this paper measures resource demand prediction accuracy rate of users, response time of resource transaction, the number of resource transactions, and resource utilization. With 87.45% of reliable prediction accuracy, this model works on the less 72.39% of response time than existing resource transaction models in a grid computing environment. The number of transactions and the resource utilization increase up to 162.56% and up to 230%, respectively.

• Journal of the Korea Institute of Building Construction
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• v.4 no.1
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• pp.105-110
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• 2004

Resource procurement is an important management area because cost of resource covers 40％ of total construction project cost and resource delivery has direct relationship with project performance. Integration of cost provides various potentials for effective and efficient project control. This study investigates the usefulness of time in resource procurement management focused on materials. These days, construction projects have characterized manufacture because of industrialization and component. Therefore, application of systematic resource planning has been requested in the construction. There are many companies conducting procurement of resource on the web by applying MRP, ERP etc. in the construction. However, in applying them in the construction yet, there is obstruction. MRP has the character doing its function under accurate cost prediction of resource. But prediction of resource is difficult in industry mechanism of the construction. If accurate cost prediction of resource is possible in the construction, it will be expected to reduce cost of procurement of resource substantially by applying successful resource planning model in the manufacture. On the basis of recent current, the purpose of study is to present procurement of resource system that period observance of construction and minimization of stock is possible by reflecting accurate lead-time to apply proactive thought to be able to cope with alteration of construction schedule efficiently in analyzing resource planning of the construction site.

• Journal of Advanced Navigation Technology
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• v.11 no.3
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• pp.252-258
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• 2007

There has been a lot of research done in scheme guaranteeing user's mobility and effective resources management to satisfy the requested by users in the wireless/mobile networks. In this paper, we propose a predictive resource allocation scheme based on ARMA(Auto Regressive Moving Average) prediction model to meet QoS requirements(handoff dropping rate) for guaranteeing users' mobility. The proposed scheme predicts the demanded amount of resource in the future time by ARMA time series prediction model, and then reserves it. The ARMA model can be used to take into account the correlation of future handoff resource demands with present and past handoff demands for provision of targeted handoff dropping rate. Simulation results show that the proposed scheme outperforms the existing RCS(Reserved channel scheme) in terms of handoff connection dropping rate and resource utilization.

• Journal of Advanced Navigation Technology
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• v.12 no.1
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• pp.22-27
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• 2008

This paper presents adaptive-MMOSPRED method for prediction of resource demands requested by multimedia calls, and shows the performance of the call admission control based on proposed resource prediction method in multimedia wireless networks. The proposed method determines (I-CDP) random variables of the standard normal distribution by using LMS algorithm that minimize errors of prediction in resource demands, while parameters in an existing method are constant all through the prediction time. Our simulation results show that prediction error in adaptive-MMOSPRED method is much smaller than in fixed-MMOSPRED method. Also we can see via simulation the CAC performance based on the proposed method improves the new call blocking performance compared with the existing method under the desired handoff dropping probability.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.2A
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• pp.26-33
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• 2005

Exact prediction of resource demands for future calls enhances the efficiency of the limited resource utilization in resource reservation methods for potential calls in wireless IP networks. In this paper, we propose a LMS-Wiener resource(bandwidth) prediction for future handoff calls, and then an the proposed method is compared with an existing Wiener-based method in terms of prediction error through our simulations. In our simulations, we assume that handoff call arrivals follow a non-Poisson process and each handoff call has an non-exponentially distributed channel holdingtime in the cell, considering that handoff call arrival pattern is not Poisson distribution but non-Poisson for long periods of time in wireless picocellular IP networks. Simulation results show that the prediction error in the proposed method converges to the lower value while in an existing method increase as time is passed. Therefore we may conclude that the proposed method improves the efficiency of resource utilization by more exactly predicting resource demands for future handoff calls than an existing method.

• Environmental and Resource Economics Review
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• v.11 no.3
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• pp.447-463
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• 2002

The heat demand prediction is an essential issue in management of district heating system. Without an accurate prediction through the lead-time period, it might be impossible to make a rational decision on many issues such as heat production scheduling and heat exchange among the plants which are very critical for the district heating company. The heat demand varies with the temperature as well as the time nonlinearly. And the parametric specification of the heat demand model would cause a misspecification bias in prediction. A nonparametric model for the short-term heat demand prediction has been developed as an alternative to avoiding the misspecification error and tested with the actual data. The prediction errors are reasonably small enough to use the model to predict a few hour ahead heat demand.

• Journal of the Korean Solar Energy Society
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• v.40 no.5
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• pp.13-22
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• 2020

Day ahead forecast is necessary for the electricity market to stabilize the electricity penetration. Numerical weather prediction is usually employed to produce the solar irradiance as well as electric power forecast for longer than 12 hours forecast horizon. Korea Meteorological Administration operates the UM-LDAPS model to produce the 36 hours forecast of hourly total irradiance 4 times a day. This study interpolates the hourly total irradiance into 15 minute instantaneous irradiance and then compare them with observed solar irradiance at four ground stations at 1 minute resolution. Numerical weather prediction model employed here was produced at 00 UTC or 18 UTC from January to December, 2018. To compare the statistical model for the forecast horizon less than 3 hours, smart persistent model is used as a reference model. Relative root mean square error of 15 minute instantaneous irradiance are averaged over all ground stations as being 18.4% and 19.6% initialized at 18 and 00 UTC, respectively. Numerical weather prediction is better than smart persistent model at 1 hour after simulation began.

• 한국신재생에너지학회:학술대회논문집
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• 2010.06a
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• pp.184.1-184.1
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• 2010

In order to research the way to evaluate wind resource without actual Met Mast data, this paper has been carried out on the southeastern region of Jeju island, Korea. Although wind turbine has been an economical alternative energy resource, misjudging the prediction of lifetime or payback period occurs because of the inaccurate assessment of wind resource and the location of wind turbine. Using WindPRO(Ver. 2.7), a software for wind farm design developed by EMD from Denmark, wind resources for the southeastern region of Jeju island was analyzed, and the performance of WindPRO prediction was evaluated in detail. Met Mast data in Su-san 5.5Km far from Samdal wind farm, AWS in Sung-san 4.5km far from Samdal wind farm, and Korea Wind Map data had been collected for this work.

• Journal of Advanced Navigation Technology
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• v.16 no.5
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• pp.794-800
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• 2012

In this paper, we propose a scheme for improving not only the utilization of frequency bands in the guard channel scheme but also the dropping rate of cognitive radio user in the wireless cognitive radio-based cellular network. The proposed scheme enables cognitive radio users to utilize the guard channel for servicing only handoff calls in normal times, but cognitive radio users must vacate the frequency channel when handoff call appearing. At this time our scheme ensures their seamless services for cognitive radio users, by predicting handoff call's appearance by MMOSPRED (Multi-Media One Step Prediction) method and then reserving the demanded channels for spectrum handoff calls. Our simulations show that our scheme performs better than other schemes; GCS(Guard Channel Scheme) and a scheme without prediction in terms of cognitive users call's dropping rate and resource utilization efficiency.