• Journal of Internet Computing and Services
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• v.15 no.3
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• pp.1-10
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• 2014

The cloud computing paradigm introduced pay-per-use models in which IT services can be created and scaled on-demand. However, service providers are still concerned about the constraints imposed by their physical infrastructures. In order to keep the required QoS and achieve the goal of upholding the SLA, virtualized resources must be efficiently consolidated to maximize system throughput while keeping energy consumption at a minimum. Using ANN, we propose a predictive SLA-aware approach for consolidating virtualized resources in a cloud environment. To maintain the QoS and to establish an optimal trade-off between performance and energy efficiency, the server's utilization threshold dynamically adapts to the physical machine's resource consumption. Furthermore, resource-intensive VMs are prevented from getting underprovisioned by assigning them to hosts that are both capable and reputable. To verify the performance of our proposed approach, we compare it with non-optimized conventional approaches as well as with other previously proposed techniques in a heterogeneous cloud environment setup.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.1-8
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• 2014

Cloud computing helps big data processing to make various information using IT resources. The government has to start the RPS(Renewable Portfolio Standard) and induce the production of electricity using renewable energy equipment. And the government manages system to gather big data that is distributed geographically. The companies can purchase the REC(Renewable Energy Certificate) to other electricity generation companies to fill shortage among their duty from the system. Because of the RPS use voluntary competitive market in REC trade and the prices have the large variation, RPS is necessary to predict the equitable REC price using RPS big data. This paper proposed REC price prediction method base on fuzzy logic using the price trend and trading condition infra in REC market, that is modeled in cloud computing environment. Cloud computing helps to analyze correlation and variables that act on REC price within RPS big data and the analysis can be predict REC price by simulation. Fuzzy logic presents balanced REC average trading prices using the trading quantity and price. The model presents REC average trading price using the trading quantity and price and the method helps induce well-converged price in the long run in cloud computing environment.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.232-235
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• 2011

Radiation data are the best source of information for estimating average incident radiation. Lacking this or data from nearby locations of similar climate, it is possible to use empirical relationships to estimate radiation from days of cloudiness. It is necessary to estimate the regression coefficients in order to predict the daily global radiation on a horizontal surface. There fore many different equations have proposed to evaluate them for certain areas. In this work a new correlation has been made to predict the solar radiation for 16 different areas over Korea by estimating the regression coefficients taking into account cloud cover. Particularly, the straight line regression model proposed shows reliable results for estimating the global radiation on a horizontal surface with monthly average deviation of-0.26 to +0.53% and each station annual average deviation of -1.61 to +1.7% from measured values.

• Journal of Institute of Convergence Technology
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• v.6 no.2
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• pp.29-33
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• 2016

Recently, the authors of this paper proposed newly the correlation model to calculate the horizontal global solar radiation in Korea based the Zhang-Huang (ZH) model proposed in 2002 for China. Previous study was pronounced the correlation with a new term of the duration of sunshine proved as being closely related with the hourly solar radiation in Korea into ZH model. And then another modified correlation for the regions without measuring cloud cover was proposed and evaluated the accuracy and validity for those regions. So, this study was performed to propose modified correlation to calculate the horizontal global solar irradiance of non-measuring cloud-cover regions. Finally, this study proposed the new correlation that could well predict hourly and daily total solar radiation for all regions, various seasons, and various weather conditions including overcast and clear, with higher accuracy and lower error than other models proposed ever before in Korea for non-measuring cloud-cover regions.

• Proceedings of the Korean Radioactive Waste Society Conference
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• 2018.11a
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• pp.492-493
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• 2018

• Journal of the Korean Solar Energy Society
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• v.22 no.3
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• pp.21-30
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• 2002

Images taken by geostationary satellite may be used to estimate solar irradiance fluxes at earth's surface. It is based on the empirical correlation between a satellite derived cloud index and the irradiance at the ground. For the validation. estimated solar radiation fluxes are compared with observed solar radiation fluxes at 16 sites over the Korean peninsular from January 1982 to December 2000. Estimated solar radiation fluxes show reliable results for estimating the global radiation with average deviation of -5.6 to +2.8% from the measured values and the yearly averaged horizontal global insolation of Korean peninsula was turned out to be $3.038kcal/m^2.day$.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.557-571
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• 2020

The existing methods for detection of fire smoke in a video easily lead to misjudgment of cloud, fog and moving distractors, such as a moving person, a moving vehicle and other non-smoke moving objects. Therefore, an algorithm for detection of fire smoke in a video based on wavelet energy slope fitting is proposed in this paper. The change in wavelet energy of the moving target foreground is used as the basis, and a time window of 40 continuous frames is set to fit the wavelet energy slope of the suspected area in every 20 frames, thus establishing a wavelet-energy-based smoke judgment criterion. The experimental data show that the algorithm described in this paper not only can detect smoke more quickly and more accurately, but also can effectively avoid the distraction of cloud, fog and moving object and prevent false alarm.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.1
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• pp.7-12
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• 2019

Clouds in the atmosphere are important variables that affect the temperature change by reflecting the radiant energy of the earth surface as well as changing the amount of sunshine by reflecting the sun's radiation energy. Especially, the amount of sunshine on the surface is very important It is essential information. Therefore, eye-observations of the sky on the surface of the earth have been enhanced by satellite photographs or relatively narrowed observation equipments. Therefore, cloud automatic observing systems have been developed in order to replace the human observers, but depending on the seasons, the reliability of observations is not high enough to be applied in the field due to pollutants or fog in the atmosphere. Therefore, we have developed a cloud observation algorithm that is robust against smog and fog. It is based on the calculation of the degree of aerosol from the all-sky image, and is added to the developed cloud reader to develop season- and climate-insensitive algorithms to improve reliability. The result compared to existing cloud readers and the result of cloud cover is improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.2914-2935
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• 2016

With the increasing popularization of cloud computing, how to reduce physical energy consumption and increase resource utilization while maintaining system performance has become a research hotspot of virtual machine deployment in cloud platform. Although some related researches have been reported to solve this problem, most of them used the traditional heuristic algorithm based on greedy algorithm and only considered effect of single-dimensional resource (CPU or Memory) on energy consumption. With considerations to multi-dimensional resource utilization, this paper analyzed impact of multi-dimensional resources on energy consumption of cloud computation. A multi-dimensional resource constraint that could maintain normal system operation was proposed. Later, a novel virtual machine deployment method (NVMDM) based on improved particle swarm optimization (IPSO) and Euclidean distance was put forward. It deals with problems like how to generate the initial particle swarm through the improved first-fit algorithm based on resource constraint (IFFABRC), how to define measure standard of credibility of individual and global optimal solutions of particles by combining with Bayesian transform, and how to define fitness function of particle swarm according to the multi-dimensional resource constraint relationship. The proposed NVMDM was proved superior to existing heuristic algorithm in developing performances of physical machines. It could improve utilization of CPU, memory, disk and bandwidth effectively and control task execution time of users within the range of resource constraint.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.147-158
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• 2022

Cloud Computing offers flexible, on demand, ubiquitous resources for cloud users. Cloud users are provided computing resources in a virtualized environment. In order to meet the growing demands for computing resources, data centres contain a large number of physical machines accommodating multiple virtual machines. However, cloud data centres cannot utilize their computing resources to their total capacity. Several policies have been proposed for improving energy proficiency and computing resource utilization in cloud data centres. Virtual machine placement is an effective method involving efficient mapping of virtual machines to physical machines. However, the availability of many physical machines accommodating multiple virtual machines in a data centre has made the virtual machine placement problem a non deterministic polynomial time hard (NP hard) problem. Metaheuristic algorithms have been widely used to solve the NP hard problems of multiple and conflicting objectives, such as the virtual machine placement problem. In this context, we presented essential concepts regarding virtual machine placement and objective functions for optimizing different parameters. This paper provides a taxonomy of metaheuristic algorithms for the virtual machine placement method. It is followed by a review of prominent research of virtual machine placement methods using meta heuristic algorithms and comparing them. Finally, this paper provides a conclusion and future research directions in virtual machine placement of cloud computing.