• International journal of advanced smart convergence
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• v.11 no.2
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• pp.1-6
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• 2022

With the emergence of the new era of the 4th industrial revolution, compute-bound workloads with large memory footprint like big data processing increase dramatically. Even in such compute-bound workloads, however, we observe bulky I/Os while loading big data from storage to memory. Although file I/O cache plays a role of accelerating the performance of storage I/O, we found out that the cache hit rate in such environments is not improved even though we increase the file I/O cache capacity because of some special I/O references generated by compute-bound workloads. To cope with this situation, we propose a new file I/O cache management policy that improves the cache hit rate for compute-bound workloads significantly. Trace-driven simulations by replaying file I/O reference logs of compute-bound workloads show that the proposed cache management policy improves the cache hit rate compared to the well-acknowledged CLOCK algorithm by a large margin.

• Journal of the Korea Society of Computer and Information
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• v.20 no.8
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• pp.1-6
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• 2015

The MapReduce Program of Hadoop Distributed File System operates on any unspecified nodes due to distributed-parallel process and block replicate for data stability. Since it is difficult to guarantee the cache locality when a Solid State Drive is used as a cache in hadoop, cache hit-rate is decreased. In this paper, we suggest a method to improve cache hit rate by pre-loading the input data of the MapReduce onto the SSD cache. To perform this method, we estimated the blocks that are used on each node by using capacity scheduler and block metadata. Eventually we could increase the performance of SSD cache by loading the blocks onto SSD cache before the Map Task run.

• Journal of the Korean Data and Information Science Society
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• v.24 no.6
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• pp.1421-1428
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• 2013

In this paper, we would like to test whether the hit at a particular bat has a dependency with the hitting results at the previous bats in professional baseball games. For this purpose, we used the 2011 Korean Baseball League data. We find out that the hitting percentage at a particular bat has no dependency with the hit at the previous bat, after reviewing the conditional probability of hit at each bat and the lift. From the independence test of hits at consecutive bats, and hit at a particular bat with no hits at previous bats, we can conclude that hits at particular bats are not dependent on the hits at previous bats in most cases. Hence, we can safely conclude that a hit at a particular bat is statistically independent from the hits at the previous bats.

• Journal of KIISE:Computer Systems and Theory
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• v.37 no.5
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• pp.292-303
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• 2010

On-chip cache memories play an important role in both performance and energy consumption points of view in resource-constrained embedded systems by filtering many off-chip memory accesses. We propose a 2-level data cache architecture with a low energy-delay product tailored for the embedded systems. The L1 data cache is small and direct-mapped, and employs a write-through policy. In contrast, the L2 data cache is set-associative and adopts a write-back policy. Consequently, the L1 data cache is accessed in one cycle and is able to provide high cache bandwidth while the L2 data cache is effective in reducing global miss rate. To reduce the penalty of high miss rate caused by the small L1 cache and power consumption of address generation, we propose an ECP(Early Cache hit Predictor) scheme. The ECP predicts if the L1 cache has the requested data using both fast address generation and L1 cache hit prediction. To reduce high energy cost of accessing the L2 data cache due to heavy write-through traffic from the write buffer laid between the two cache levels, we propose a one-way write scheme. From our simulation-based experiments using a cycle-accurate simulator and embedded benchmarks, the proposed 2-level data cache architecture shows average 3.6% and 50% improvements in overall system performance and the data cache energy consumption.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.26 no.4
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• pp.101-109
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• 2018

In order to predict the future needs of the aircraft repair parts, each military group develops and applies various techniques to their characteristics. However, the aircraft and the equipped weapon systems are becoming increasingly advanced, and there is a problem in improving the hit rate by applying the existing demand prediction technique due to the change of the aircraft condition according to the long term operation of the aircraft. In this study, we propose a new prediction model based on the conventional time-series analysis technique to improve the prediction accuracy of aircraft repair parts by using machine learning model. And we show the most effective predictive method by demonstrating the change of hit rate based on actual data.

• Journal of KIISE:Computing Practices and Letters
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• v.9 no.4
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• pp.410-419
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• 2003

The buffer cache of general operating systems such as Linux manages file data by using global block replacement policy and read ahead. As a result, multimedia data with a low locality of reference and various consumption rate have low cache hit ratio and consume additional buffers because of read ahead. In this paper we have designed and implemented a new buffer allocation algorithm for multimedia data on Linux. Our approach keeps one read-ahead cache per every opened multimedia file and dynamically changes the read-ahead group size based on the buffer consumption rate of the file. This distributes resources fairly and optimizes the buffer consumption. This paper compares the system performance with that of Linux 2.4.17 in terms of buffer consumption and buffer hit ratio.

• KIISE Transactions on Computing Practices
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• v.21 no.3
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• pp.247-256
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• 2015

Since the Gulf War, air power has played a key role. However, the effect of high-tech weapons, such as laser-guided bombs and electronic optical equipment, drops significantly if they do not match the weather conditions. So, aircraft that are assigned to carry laser-guided bombs must replace these munitions during bad weather conditions. But, there are no objective criteria for when weapons should be replaced. Therefore, in this paper, we propose an algorithm to predict the hit rate of laser-guided bombs using cloud image processing. In order to verify the accuracy of the algorithm, we applied the weather conditions that may affect laser-guided bombs to simulated flight equipment and executed simulated weapon release, then collected and analyzed data. Cloud images appropriate to the weather conditions were developed, and applied to the algorithm. We confirmed that the algorithm can accurately predict the hit rate of laser-guided bombs in most weather conditions.

• The Southeast Asian review
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• v.20 no.3
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• pp.41-64
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• 2010

The recent global financial crisis triggered by the sub-prime mortgage debacle in the United States hit hard most ASEAN countries that have just recovered from the unprecedented economic crisis ten years ago. This paper, using individual time-series and panel data from 1990 to 2009, intends to investigate and compare the impacts of the two aforementioned economic crises on trade in the four developing ASEAN countries that encompass Indonesia, Malaysia, the Philippines and Thailand. In doing so, the paper traces the behaviors of main macroeconomic variables before and after the crises on graphs, and then estimates classical export and import demand functions that include real exchange rate, home and foreign GDPs as explanatory variables. In the estimation functions, two dummy variables are added to consider the effects of the two economic crises separately. Individual country data analyses reveal that by and large the 1997 economic crisis seems hit those ASEAN countries' exports and imports harder than the recent global financial crisis. Surprisingly the recent financial crisis turns out more or less statistically insignificant for those countries' export and import performances. The fixed effect model estimation using panel data of those four ASEAN countries also shows that the 1997 economic crisis had affected exports and imports of those countries negatively while the recent global financial crisis was not statistically significant. These results indicate that overall the effect from the 1997 crisis was more devastating than that of the recent global crisis for those ASEAN countries.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.15 no.2
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• pp.137-143
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• 2015

During the past 30 years, DRAM has been used for the reasons of economic efficiency of the production. Recently, PRAM has been emerged to overcome the shortcomings of DRAM. In this paper, we propose a technique that can reduce energy consumption by use of a narrow values to the write operation of PRAM. For this purpose, we describe the data compression method using a narrow value and the architecture of PRAM, We also experiment under the Simplescalar 3.0e simulator and SPEC CPU2000 benchmark environments. According to the experiments, the data hit rate of PRAM was increased by 39.4% to 67.7% and energy consumption was reduced by 9.2%. In order to use the proposed technique, it requires 3.12% of space overhead per word, and some additional hardware modules.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.369-384
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• 2021

In a personalized product recommendation system, when the amount of log data is large or sparse, the accuracy of model recommendation will be greatly affected. To solve this problem, a personalized product recommendation method using deep factorization machine (DeepFM) to analyze user behavior is proposed. Firstly, the K-means clustering algorithm is used to cluster the original log data from the perspective of similarity to reduce the data dimension. Then, through the DeepFM parameter sharing strategy, the relationship between low- and high-order feature combinations is learned from log data, and the click rate prediction model is constructed. Finally, based on the predicted click-through rate, products are recommended to users in sequence and fed back. The area under the curve (AUC) and Logloss of the proposed method are 0.8834 and 0.0253, respectively, on the Criteo dataset, and 0.7836 and 0.0348 on the KDD2012 Cup dataset, respectively. Compared with other newer recommendation methods, the proposed method can achieve better recommendation effect.