• Journal of Internet Computing and Services
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• v.17 no.1
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• pp.7-14
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• 2016

Recently, as cloud computing technologies are developed, it enable to work anytime and anywhere by smart phone and computer. Also, cloud computing technologies are suited to reduce costs of maintaining IT infrastructure and initial investment, so cloud computing has been developed. As demand about cloud computing has risen sharply, problems of power consumption are occurred to maintain the environment of data center. To solve the problem, first of all, power consumption has been measured. Although using power meter to measure power consumption obtain accurate power consumption, extra cost is incurred. Thus, we propose prediction method about power consumption without power meter. To proving accuracy about proposed method, we perform CPU and Hard disk test on cloud computing environment. During the tests, we obtain both predictive value by proposed method and actual value by power meter, and we calculate error rate. As a result, error rate of predictive value and actual value shows about 4.22% in CPU test and about 8.51% in Hard disk test.

• Journal of Computing Science and Engineering
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• v.6 no.4
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• pp.310-319
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• 2012

Next generation wireless receivers demand low computational complexity algorithms with high computing power in order to perform fast signal detections and error estimations. Several signal detection and estimation algorithms have been proposed for next generation wireless receivers which are primarily designed to provide reasonable performance in terms of signal to noise ratio (SNR) and bit error rate (BER). However, none of them have been chosen for direct implementation as they offer high computational complexity with relatively lower computing power. This paper presents a low-complexity power-efficient algorithm that improves the computing power and provides relatively faster signal detection for next generation wireless multiuser receivers. Measurement results of the proposed algorithm are provided and the overall system performance is indicated by BER and the computational complexity. Finally, in order to verify the low-complexity of the proposed algorithm we also present a formal mathematical proof.

• Journal of Information Processing Systems
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• v.17 no.3
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• pp.489-504
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• 2021

By distributing computing tasks among devices at the edge of networks, edge computing uses virtualization, distributed computing and parallel computing technologies to enable users dynamically obtain computing power, storage space and other services as needed. Applying edge computing architectures to Internet of Vehicles can effectively alleviate the contradiction among the large amount of computing, low delayed vehicle applications, and the limited and uneven resource distribution of vehicles. In this paper, a predictive offloading strategy based on the MEC load state is proposed, which not only considers reducing the delay of calculation results by the RSU multi-hop backhaul, but also reduces the queuing time of tasks at MEC servers. Firstly, the delay factor and the energy consumption factor are introduced according to the characteristics of tasks, and the cost of local execution and offloading to MEC servers for execution are defined. Then, from the perspective of vehicles, the delay preference factor and the energy consumption preference factor are introduced to define the cost of executing a computing task for another computing task. Furthermore, a mathematical optimization model for minimizing the power overhead is constructed with the constraints of time delay and power consumption. Additionally, the simulated annealing algorithm is utilized to solve the optimization model. The simulation results show that this strategy can effectively reduce the system power consumption by shortening the task execution delay. Finally, we can choose whether to offload computing tasks to MEC server for execution according to the size of two costs. This strategy not only meets the requirements of time delay and energy consumption, but also ensures the lowest cost.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2239-2256
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• 2022

In this paper, a model is proposed to comprehensively evaluate the computational power. The five forces model of computational power solves the problem that the measurement units of different indexes are not unified in the process of computational power evaluation. It combines the bidirectional projection method with TOPSIS method. This model is more scientific and effective in evaluating the comprehensive situation of computational power. Lastly, an example shows the validity and practicability of the model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.8
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• pp.1827-1834
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• 2013

The existing PC-based desktop environment is being changed to a server-based virtual desktop environment due to various advantages such as security, mobility, and upgrade cost reduction. In this paper, a virtual computing lab service system which is applicable to the existing computer lab is designed and implemented using both an open source-based cloud computing platform and hypervisor. In addition, a node power scheduler is proposed in order to reduce power consumption in a server farm. The experimental results show that the power scheduler reduces power consumption considerably over the server farm without the power scheduler.

• Journal of Electrical Engineering and Technology
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• v.8 no.1
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• pp.20-30
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• 2013

An intelligent grid computing architecture is proposed and developed for transient stability constrained total transfer capability evaluation of future smart grid. In the proposed intelligent grid computing architecture, a model of generalized compute nodes with 'able person should do more work' feature is presented and implemented to make full use of each node. A timeout handling strategy called conditional resource preemption is designed to improve the whole system computing performance further. The architecture can intelligently and effectively integrate heterogeneous distributed computing resources around Intranet/Internet and implement the dynamic load balancing. Furthermore, the robustness of the architecture is analyzed and developed as well. The case studies have been carried out on the IEEE New England 39-bus system and a real-sized Chinese power system, and results demonstrate the practicability and effectiveness of the intelligent grid computing architecture.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.181-191
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• 2018

Rapid development of information technology makes our environment become smarter and massive high performance computers are providing powerful computing for that. Graphics Processing Unit (GPU) as a typical high performance component is being widely used for both graphics and general-purpose applications. Although it can greatly improve computing power, it also delivers significant power consumption and need sufficient power supplies. To make high performance computing more sustainable, the important step is to measure it. Current power technologies for GPU have some drawbacks, such as they are not applicable for power estimation at the early stage. In this article, we present a novel power technology to correlate power consumption and the characteristics at the programmer perspective, and then to estimate power consumption of source program without prerunning. We conduct experiments on Nvidia's GT740 platform; the results show that our power model is more accurately than regression model and has an average error of 2.34% and the maximum error of 9.65%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2682-2700
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• 2023

To cope with the risks of climate change and promote the realization of carbon peaking and carbon neutrality, this paper first comprehensively considers the policy background, technical trends and carbon reduction paths of energy conservation and emission reduction in data center server industry. Second, we propose a computing power carbon efficiency of data center server, and constructs the carbon emission per performance of server (CEPS) model. According to the model, this paper selects the mainstream data center servers for testing. The result shows that with the improvement of server performance, the total carbon emissions are rising. However, the speed of performance improvement is faster than that of carbon emission, hence the relative carbon emission per unit computing power shows a continuous decreasing trend. Moreover, there are some differences between different products, and it is calculated that the carbon emission per unit performance is 20-60KG when the service life of the server is five years.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.480-483
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• 1997

A fossil power plant can be modeled by a lot of algebraic equations and differential equations. When we simulate a large, complicated fossil power plant by a computer such as workstation or PC, it takes much time until overall equations are completely calculated. Therefore, new processing systems which have high computing speed is ultimately needed to develope real-time simulators. Vital points of real-time simulators are accuracy, computing speed, and deadline observing. In this paper, we present a enhanced strategy in which we can provide powerful computing power by parallel processing of DSP processors with communication links. We designed general purpose DSP modules, and a VME interface module. Because the DSP module is designed for general purpose, we can easily expand the parallel system by just connecting new DSP modules to the system. Additionally we propose methods about downloading programs, initial data to each DSP module via VME bus, DPRAM and processing sequences about computing and updating values between DSP modules and CPU30 board when the simulator is working.

• Journal of the Korea Society of Computer and Information
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• v.16 no.2
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• pp.9-16
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• 2011

GPU computing is emerging in high performance application area since it can easily exploit massive parallelism in a way of cost-effective computing. The power method which finds the eigen vector of a given matrix is widely used in various applications such as PageRank for calculating importance of web pages. In this research we made the power method efficiently parallelized on GPU and also suggested how it can be improved to enhance its performance. The power method mainly consists of matrix-vector product and it can be easily parallelized. However, it should decide the convergence of the eigen vector and need scaling of the vector subsequently. Such operations incur several calls to GPU kernels and data movement between host and GPU memories. We improved the performance of the power method by means of reduced calls to GPU kernels, optimized thread allocation and enhanced decision operation for the convergence.