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

• MISULJARYO - National Museum of Korea Art Journal
• /
• v.98
• /
• pp.22-53
• /
• 2020

This paper analyzes the contents, characteristics, and historical significance of the dedicatory inscriptions (josanggi) on the Amitabha Buddha and the Maitreya Bodhisattva statues of Gamsansa Temple, two masterpieces of Buddhist sculpture from the Unified Silla period. In the first section, I summarize research results from the past century (divided into four periods), before presenting a new perspective and methodology that questions the pre-existing notion that the Maitreya Bodhisattva has a higher rank than the Amitabha Buddha. In the second section, through my own analysis of the dedicatory inscriptions, arrangement, and overall appearance of the two images, I assert that the Amitabha Buddha sculpture actually held a higher rank and greater significance than the Maitreya Bodhisattva sculpture. In the third section, for the first time, I provide a new interpretation of two previously undeciphered characters from the inscriptions. In addition, by comparing the sentence structures from the respective inscriptions and revising the current understanding of the author (chanja) and calligrapher (seoja), I elucidate the possible meaning of some ambiguous phrases. Finally, in the fourth section, I reexamine the content of both inscriptions, differentiating between the parts relating to the patron (josangju), the dedication (josang), and the prayers of the patrons or donors (balwon). In particular, I argue that the phrase "for my deceased parents" is not merely a general axiom, but a specific reference. To summarize, the dedicatory inscriptions can be interpreted as follows: when Kim Jiseong's parents died, they were cremated and he scattered most of their remains by the East Sea. But years later, he regretted having no physical memorial of them to which to pay his respects. Thus, in his later years, he donated his estate on Gamsan as alms and led the construction of Gamsansa Temple. He then commissioned the production of the two stone sculptures of Amitabha Buddha and Maitreya Bodhisattva for the temple, asking that they be sculpted realistically to reflect the actual appearance of his parents. Finally, he enshrined the remains of his parents in the sculptures through the hole in the back of the head (jeonghyeol). The Maitreya Bodhisattva is a standing image with a nirmanakaya, or "transformation Buddha," on the crown. As various art historians have pointed out, this iconography is virtually unprecedented among Maitreya images in East Asian Buddhist sculpture, leading some to speculate that the standing image is actually the Avalokitesvara. However, anyone who reads the dedicatory inscription can have no doubt that this image is in fact the Maitreya. To ensure that the sculpture properly embodied his mother (who wished to be reborn in Tushita Heaven with Maitreya Bodhisattva), Kim Jiseong combined the iconography of the Maitreya and Avalokitesvara (the reincarnation of compassion). Hence, Kim Jiseong's deep love for his mother motivated him to modify the conventional iconography of the Maitreya and Avalokitesvara. A similar sentiment can be found in the sculpture of Amitabha Buddha. To this day, any visitor to the temple who first looks at the sculptures from the front before reading the text on the back will be deeply touched by the filial love of Kim Jiseong, who truly cherished the memory of his parents.