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Development of an Apartment Price Change Rate Prediction Model with Geographical Adjacency
http://doi.org/10.5626/JOK.2022.49.6.424
Recently, in the real estate market, decoupling in which housing prices fluctuate by the region has been escalating. This phenomenon implies that each region is composed of districts that are adjacent to one another. This thesis confirms that the prices of a district change in synchronization with that of the adjacent districts and proves that the fluctuations in apartment prices in the districts within Seoul are due to the neighbors. The rate of change in apartment prices, macroeconomic indicators, and private education indicators are used to test the hypothesis with a 3D (time, distance, and attribute) model, which is further deciphered using CNN. The model considers the situation of neighbors and is subdivided into the following three sub-models: consideration only for the target area (I), consideration for long-distance areas (II), and change in the number of neighbors (III). The metrics used are mean absolute error and mean directional accuracy. It was observed that the model with neighbors performed better than the persistence model and XGBoost. Furthermore, its sub-models showed good performance in the order of model III (with 3 neighbors), II, and I. This study clearly exhibits that the factor “neighbor” affects the rate of change in apartment prices.
An Analysis of the Performance Interference among Column Families in RocksDB
Hoyoung Lee, Minho Lee, Young Ik Eom
http://doi.org/10.5626/JOK.2021.48.7.835
RocksDB is one of the representative LSM-tree-based key-value stores and provides a column family feature to classify key-value pairs based on the characteristics of data. Each column family has its write buffer and manages the classified key-value pairs with it, whereas column families have to share a single WAL file for data consistency. However, sharing the WAL file induces performance interference among the column families and reduces the write performance of RocksDB. In this paper, we have analyzed the performance degradation of RocksDB caused by performance interference among multiple column families. Consequently, we measured the write performance of RocksDB by varying the size of WAL file and the number of column families. Experimental results clearly show that the write performance of RocksDB decreases by up to 57.08% according to the size of the WAL file and the number of the constructed column families.
Ontology and CNN-based Inference of the Threat Relationship Between UAVs and Surrounding Objects
MyungJoong Jeon, MinHo Lee, HyunKyu Park, YoungTack Park, Hyung-Sik Yoon, Yun-Geun Kim
http://doi.org/10.5626/JOK.2020.47.4.404
The technology that identifies the relationship between surrounding objects and recognizes the situation is considered as an important and necessary technology in various areas. Numerous methodologies are being studied for this purpose. Most of the studies have solved the problem by building the domain knowledge into ontology for reasoning of situation awareness. However, based on the existing approach; it is difficult to deal with new situations in the absence of domain experts due to the dependency of experts on relevant domain knowledge. In addition, it is difficult to build the knowledge to infer situations that experts have not considered. Therefore, this study proposes a model for using ontology and CNN for reasoning of the relationships between UAVs and surrounding objects to solve the existing problems. Based on the assumption that the accuracy of ontology reasoning is insufficient, first, the reasoning was performed using the information from the detected surrounding objects. Later, the results of ontology reasoning are revised by CNN inference. Due to the limitations of actual data acquisition, data generator was built to generate data similar to real data. For evaluation of this study, two models of relationships between two objects were built and evaluated; both the models showed over 90% accuracy.
I/O Completion Technique of Virtualized System Considering CPU Usage with High-Performance Storage Devices
Hyeji Lee, Taehyung Lee, Minho Lee, Yongju Song, Young Ik Eom
http://doi.org/10.5626/JOK.2019.46.7.612
Recently, the advent of high-performance storage devices such as Samsung Z-SSD and Intel Optane SSD has shifted the I/O systems’ performance overhead from the storage devices to the software I/O layer. To optimize the I/O performance of high-performance storage devices, the hypervisor and operating system have focused on the effectiveness of polling technique, which is one of the I/O completion techniques applied in virtualized systems, and new techniques such as hybrid and adaptive polling are being adopted. This paper reveals the problem of the existing adaptive polling techniques provided by QEMU-KVM hypervisor and proposes a new I/O completion technique, which saves on CPU usage while fully utilizing high-performance storage devices. Our evaluation indicates that the proposed technique reduces CPU usage by up to 39.7% while delaying I/O latency to less than 5.3% only, in comparison to conventional systems.
An Analysis of the Overhead of Multiple Buffer Pool Scheme on InnoDB-based Database Management Systems
Yongju Song, Minho Lee, Young Ik Eom
The advent of large-scale web services has resulted in gradual increase in the amount of data used in those services. These big data are managed efficiently by DBMS such as MySQL and MariaDB, which use InnoDB engine as their storage engine, since InnoDB guarantees ACID and is suitable for handling large-scale data. To improve I/O performance, InnoDB caches data and index of its database through a buffer pool. It also supports multiple buffer pools to mitigate lock contentions. However, the multiple buffer pool scheme leads to the additional data consistency overhead. In this paper, we analyze the overhead of the multiple buffer pool scheme. In our experimental results, although multiple buffer pool scheme mitigates the lock contention by up to 46.3%, throughput of DMBS is significantly degraded by up to 50.6% due to increased disk I/O and fsync calls.
Priority-based Hint Management Scheme for Improving Page Sharing Opportunity of Virtual Machines
Yeji Nam, Minho Lee, Dongwoo Lee, Young Ik Eom
Most data centers attempt to consolidate servers using virtualization technology to efficiently utilize limited physical resources. Moreover, virtualized systems have commonly adopted contents-based page sharing mechanism for page deduplication among virtual machines (VMs). However, previous page sharing schemes are limited by the inability to effectively manage accumulated hints which mean sharable pages in stack. In this paper, we propose a priority-based hint management scheme to efficiently manage accumulated hints, which are sent from guest to host for improving page sharing opportunity in virtualized systems. Experimental results show that our scheme removes pages with low sharing potential, as compared with the previous schemes, by efficiently managing the accumulated pages.
Analyses of the Effect of System Environment on Filebench Benchmark
Yongju Song, Junghoon Kim, Dong Hyun Kang, Minho Lee, Young Ik Eom
In recent times, NAND flash memory has become widely used as secondary storage for computing devices. Accordingly, to take advantage of NAND flash memory, new file systems have been actively studied and proposed. The performance of these file systems is generally measured with benchmark tools. However, since benchmark tools are executed by software simulation methods, many researchers get non-uniform benchmark results depending on the system environments. In this paper, we use Filebench, one of the most popular and representative benchmark tools, to analyze benchmark results and study the reasons why the benchmark result variations occur. Our experimental results show the differences in benchmark results depending on the system environments. In addition, this study substantiates the fact that system performance is affected mainly by background I/O requests and fsync operations.
Hybrid Main Memory based Buffer Cache Scheme by Using Characteristics of Mobile Applications
Chansoo Oh, Dong Hyun Kang, Minho Lee, Young Ik Eom
Mobile devices employ buffer cache mechanisms, just as in computer systems such as desktops or servers, to mitigate the performance gap between main memory and secondary storage. However, DRAM has a problem in that it accelerates battery consumption by performing refresh operations periodically to maintain the stored data. In this paper, we propose a novel buffer cache scheme to increase the battery lifecycle in mobile devices based on a hybrid main memory architecture consisting of DRAM and non-volatile PCM. We also suggest a new buffer cache policy that allocates buffers based on process states to optimize the performance and endurance of PCM. In particular, our algorithm allocates each page to the appropriate position corresponding to the state of the application that owns the page, and tries to ensure a rapid response of foreground applications even with a small amount of DRAM memory. The experimental results indicate that the proposed scheme reduces the elapsed time of foreground applications by 58% on average and power consumption by 23% on average without negatively impacting the performance of background applications.
A Case Study of Drug Repositioning Simulation based on Distributed Supercomputing Technology
Jik-Soo Kim, Seungwoo Rho, Minho Lee, Seoyoung Kim, Sangwan Kim, Soonwook Hwang
In this paper, we present a case study for a drug repositioning simulation based on distributed supercomputing technology that requires highly efficient processing of large-scale computations. Drug repositioning is the application of known drugs and compounds to new indications (i.e., new diseases), and this process requires efficient processing of a large number of docking tasks with relatively short per-task execution times. This mechanism shows the main characteristics of a Many-Task Computing (MTC) application, and as a representative case of MTC applications, we have applied a drug repositioning simulation in our HTCaaS system which can leverage distributed supercomputing infrastructure, and show that efficient task dispatching, dynamic resource allocation and load balancing, reliability, and seamless integration of multiple computing resources are crucial to support these challenging scientific applications.
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