2012, Vol.17, No.5

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

pISSN : 1598-849X / eISSN : 2383-9945
https://journal.kci.go.kr/jksci

In this paper, we design and implement the simulator for WAN storage replication model performance evaluation in cloud computing environment. Each cloud of simulator is composed of virtual machine emulator and storage emulator. The virtual machine emulator is composed of read/write ratio module, the read/write sequence combination module, and the read/write request module. The storage emulator is composed of storage management module, data transfer module, read/write operations module, and overhead processing module. Using the simulator, we evaluate performance of migration scheme, pre-copy and the post-copy, considering about read/write ratio, network delay, and network bandwidth. Through simulation, we have confirmed that the average migration time of pre-copy was decreased proportional to the read operation. However, average migration time of post-copy was on the increase. Also, the average migration time of post-copy was increased proportional to the network delay. However, average migration time of pre-copy was shown uniformly. Therefore, we show that pre-copy model more effective to reduce the average migration time than the post-copy model. The average migration time of pre-copy and post-copy were not affected by the change of network bandwidth. Therefore, these results show that selects the storage replication model to be, the network bandwidth know not being the important element.

As the clock frequency increases, CPU performance improves continuously. However, power and thermal problems in the CPU become more serious as the clock frequency increases. For this reason, utilizing the GPU to reduce the workload of the CPU becomes one of the most popular methods in recent high-performance computer systems. The GPU is a specialized processor originally designed for graphics processing. Recently, the technologies such as CUDA which utilize the GPU resources more easily become popular, leading to the improved performance of the computer system by utilizing the CPU and GPU simultaneously in executing various kinds of applications. In this work, we analyze the temperature and the energy efficiency of the computer system where the CPU and the GPU are utilized simultaneously, to figure out the possible problems in upcoming high-performance computer systems. According to our experimentation results, the temperature of both CPU and GPU increase when the application is executed on the GPU. When the application is executed on the CPU, CPU temperature increases whereas GPU temperature remains unchanged. The computer system shows better energy efficiency by utilizing the GPU compared to the CPU, because the throughput of the GPU is much higher than that of the CPU. However, the temperature of the system tends to be increased more easily when the application is executed on the GPU, because the GPU consumes more power than the CPU.

Recently, the necessity for good techniques of detecting network traffic attack has increased. In this paper, we suggest a new method of detecting abnormal patterns of network traffic data by visualizing their IP and port information into two dimensional images. The proposed approach first generates four 2D images from IP data of transmitters and receivers, and makes one 2D image from port data. Analyzing those images, it then extracts their major features such as linear patterns or high intensity values, and determines if traffic data contain DDoS or DoS Attacks. To comparatively evaluate the performance of the proposed algorithm, we show that our abnormal pattern detection method outperforms the existing algorithm in terms of accuracy and speed.