• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.127-148
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• 2020

The data center is a physical environment facility for accommodating computer systems and related components, and is an essential foundation technology for next-generation core industries such as big data, smart factories, wearables, and smart homes. In particular, with the growth of cloud computing, the proportional expansion of the data center infrastructure is inevitable. Monitoring the health of these data center facilities is a way to maintain and manage the system and prevent failure. If a failure occurs in some elements of the facility, it may affect not only the relevant equipment but also other connected equipment, and may cause enormous damage. In particular, IT facilities are irregular due to interdependence and it is difficult to know the cause. In the previous study predicting failure in data center, failure was predicted by looking at a single server as a single state without assuming that the devices were mixed. Therefore, in this study, data center failures were classified into failures occurring inside the server (Outage A) and failures occurring outside the server (Outage B), and focused on analyzing complex failures occurring within the server. Server external failures include power, cooling, user errors, etc. Since such failures can be prevented in the early stages of data center facility construction, various solutions are being developed. On the other hand, the cause of the failure occurring in the server is difficult to determine, and adequate prevention has not yet been achieved. In particular, this is the reason why server failures do not occur singularly, cause other server failures, or receive something that causes failures from other servers. In other words, while the existing studies assumed that it was a single server that did not affect the servers and analyzed the failure, in this study, the failure occurred on the assumption that it had an effect between servers. In order to define the complex failure situation in the data center, failure history data for each equipment existing in the data center was used. There are four major failures considered in this study: Network Node Down, Server Down, Windows Activation Services Down, and Database Management System Service Down. The failures that occur for each device are sorted in chronological order, and when a failure occurs in a specific equipment, if a failure occurs in a specific equipment within 5 minutes from the time of occurrence, it is defined that the failure occurs simultaneously. After configuring the sequence for the devices that have failed at the same time, 5 devices that frequently occur simultaneously within the configured sequence were selected, and the case where the selected devices failed at the same time was confirmed through visualization. Since the server resource information collected for failure analysis is in units of time series and has flow, we used Long Short-term Memory (LSTM), a deep learning algorithm that can predict the next state through the previous state. In addition, unlike a single server, the Hierarchical Attention Network deep learning model structure was used in consideration of the fact that the level of multiple failures for each server is different. This algorithm is a method of increasing the prediction accuracy by giving weight to the server as the impact on the failure increases. The study began with defining the type of failure and selecting the analysis target. In the first experiment, the same collected data was assumed as a single server state and a multiple server state, and compared and analyzed. The second experiment improved the prediction accuracy in the case of a complex server by optimizing each server threshold. In the first experiment, which assumed each of a single server and multiple servers, in the case of a single server, it was predicted that three of the five servers did not have a failure even though the actual failure occurred. However, assuming multiple servers, all five servers were predicted to have failed. As a result of the experiment, the hypothesis that there is an effect between servers is proven. As a result of this study, it was confirmed that the prediction performance was superior when the multiple servers were assumed than when the single server was assumed. In particular, applying the Hierarchical Attention Network algorithm, assuming that the effects of each server will be different, played a role in improving the analysis effect. In addition, by applying a different threshold for each server, the prediction accuracy could be improved. This study showed that failures that are difficult to determine the cause can be predicted through historical data, and a model that can predict failures occurring in servers in data centers is presented. It is expected that the occurrence of disability can be prevented in advance using the results of this study.

• Korean Journal of Heritage: History & Science
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• v.49 no.2
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• pp.4-19
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• 2016

Jangkei(狀啓) made to the Royal Court by Yi Sun-sin during the Japanese invasions of Korea is handed down under the names of Jangcho(狀草), Keicho(啓草), Keibon(啓本) and others depending on copying patterns of those times and later times as it was copied out by a third person. In particular, "YimjinJangcho(壬辰狀草)" which Yi drew up during his service as the director of the naval forces in Jeolla Jwasooyeong is known as the most popular Jangkei. "ChungMinKongKeicho" which has been re-located recently after loss is a national treasure level cultural property as valuable as "YimjinJangcho" and should be treated as a model of Yi Sun-sin's other Jangkeis by next generations. As of now, however it is not confirmed if it is a totally new book related to Yi Sun-sin or is supplementary to the lost Jangkei, this study decided to ascertain relevant information through a bibliographic discussion on the question. "Chungmin(忠愍)" was the title that was used after the death of Yi Sun-sin, and "ChungMinKongKeicho" was completed when Jangkei was copied in 1662. 12 books that would not be found in YimjinJangcho are included in the book and such books are also present in the Jangkei supplement which has been known lost so far. What should be especially focused on here is that the forms and contents of these (11) photographs that Japanese shot from "ChungMinKongKeicho" in 1928 turned out to be completely identical to those of the original copy. The point that Korean History Compilation Committee added the 12 books to Jangkei as referring to the book as "One Keicho(啓草) partially copied(抄寫) in separation" and that Cho Sung-do categorized the 12 books into a supplement and others can be solid proofs to make the Jangkei supplement called "ChungMinKongKeicho". In terms of "ChungMooKongKeicho", since it consists of 62 books in total, it is not reasonable to see the book as Jangkei supplement which has the extra 12 more books for itself. "ChungMooKongKeibon" in "ChungMooKongYusa" was written with a total of 16 books. In the body, Yidumun is only clearly present, and the three books in the later part are same with the original copy of "ChungMooKongKeicho". "YimjinJangcho" by Korean History Compilation Committee has been the only book in which Yidumun was observed so far but now, it is assumed that the publication date of "ChungMooKongKeibon" goes before that of the former. The counterargument to the opinion that "ChungMinKongKeicho" is the supplement to Jangkei is based on Lee Eun-sang's comment "One page of a log in the Jangkei copy supplement." At first Seol Ui-sik introduced a piece photo of the rough draft of "MoosulIlki" in a drawing form through "Nanjung Ilkicho by Yi Sun-sin" in 1953. Lee Eun-sang also added two pages of the handwritten Yilkicho in the Jangkeichobon supplement to "MoosulIlki" and for the second time, the phrase "One page of a log written during the last 10 days after the Jangkei copy supplement" and "Supplement" were used. Those views are originated from the comment "One photograph of the rough draft of "MoosulIlki"" which Seol Ui-sik introduced without knowledge of the exact source. Lee Eun-sang said, "One page of a log in the Jangkei copy supplement" because Lee mistook "ChungMooKongYusa" for a book related to Jangkei. Since it is the wrong argument different from the actual situation of the original copy, if it has to be corrected, it should be rephrased "One page of a log in ChungMooKongYusa." After all, the source of the counterargument is the mistake because there has never been the Jangkei supplement with one page of a log included. All the Jangkeis other than "YimjinJangcho" can be said as the Jangkei supplements but still, they are separated from the other Jangkeis for the extra 12 more books are present in the commonly-called Jangkei supplement. Due to that reason, the argument on how "ChungMinKongKeicho" with the 12 books added is the popular Jangkei supplement should be considered more reasonable.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.191-207
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• 2021

Up to this day, mobile communications have evolved rapidly over the decades, mainly focusing on speed-up to meet the growing data demands of 2G to 5G. And with the start of the 5G era, efforts are being made to provide such various services to customers, as IoT, V2X, robots, artificial intelligence, augmented virtual reality, and smart cities, which are expected to change the environment of our lives and industries as a whole. In a bid to provide those services, on top of high speed data, reduced latency and reliability are critical for real-time services. Thus, 5G has paved the way for service delivery through maximum speed of 20Gbps, a delay of 1ms, and a connecting device of 106/㎢ In particular, in intelligent traffic control systems and services using various vehicle-based Vehicle to X (V2X), such as traffic control, in addition to high-speed data speed, reduction of delay and reliability for real-time services are very important. 5G communication uses high frequencies of 3.5Ghz and 28Ghz. These high-frequency waves can go with high-speed thanks to their straightness while their short wavelength and small diffraction angle limit their reach to distance and prevent them from penetrating walls, causing restrictions on their use indoors. Therefore, under existing networks it's difficult to overcome these constraints. The underlying centralized SDN also has a limited capability in offering delay-sensitive services because communication with many nodes creates overload in its processing. Basically, SDN, which means a structure that separates signals from the control plane from packets in the data plane, requires control of the delay-related tree structure available in the event of an emergency during autonomous driving. In these scenarios, the network architecture that handles in-vehicle information is a major variable of delay. Since SDNs in general centralized structures are difficult to meet the desired delay level, studies on the optimal size of SDNs for information processing should be conducted. Thus, SDNs need to be separated on a certain scale and construct a new type of network, which can efficiently respond to dynamically changing traffic and provide high-quality, flexible services. Moreover, the structure of these networks is closely related to ultra-low latency, high confidence, and hyper-connectivity and should be based on a new form of split SDN rather than an existing centralized SDN structure, even in the case of the worst condition. And in these SDN structural networks, where automobiles pass through small 5G cells very quickly, the information change cycle, round trip delay (RTD), and the data processing time of SDN are highly correlated with the delay. Of these, RDT is not a significant factor because it has sufficient speed and less than 1 ms of delay, but the information change cycle and data processing time of SDN are factors that greatly affect the delay. Especially, in an emergency of self-driving environment linked to an ITS(Intelligent Traffic System) that requires low latency and high reliability, information should be transmitted and processed very quickly. That is a case in point where delay plays a very sensitive role. In this paper, we study the SDN architecture in emergencies during autonomous driving and conduct analysis through simulation of the correlation with the cell layer in which the vehicle should request relevant information according to the information flow. For simulation: As the Data Rate of 5G is high enough, we can assume the information for neighbor vehicle support to the car without errors. Furthermore, we assumed 5G small cells within 50 ~ 250 m in cell radius, and the maximum speed of the vehicle was considered as a 30km ~ 200 km/hour in order to examine the network architecture to minimize the delay.