Harmful algal blooms (HABs), commonly known as red tides, are aquatic phenomena caused by the rapid growth and accumulation of certain microalgae, which can lead to marked discoloration of surface waters, and severe impacts on public health, commerce, and the environment. In South Korea, the red tides have been a serious and recurrent problem, especially along the south coast. Plenty of yellow loess was used to control an outbreak of the red tides for 15 years from 1996 until now. Yellow loess was almost sprayed in the vicinity of a large fish farming industry. In this research, the distribution characteristics and density distribution of zooplankton were investigated in autumn (Oct. 2008) and spring (Apr. 2009) using volume backscattering strength (SV) calculated by the zooplankton collected with north pacific standard (NORPAC) net and the echo intensity measured with ADCP at stations on the study area in the spraying ocean of yellow loess (SOYL), and the non-spraying ocean of yellow ocean (NOYL) by the red tide generating every year. The species number and the individuals per unit volume of the zooplankton collected in NOYL was high and it which was collected in SOYL was low. As a result of comparing the volume backscattering strength (
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.
본고(本稿)시리이즈의 제1보(第一報)에서 우리는 물리(物理), 사회과학(社會科學) 및 공학분야(工學分野)의 12,442명(名)의 과학자(科學者)와 기술자(技術者)에 대한 정보교환활동(情報交換活動)의 78례(例)에 있어서 일반과정(一般過程)과 몇 가지 결과(結果)를 기술(記述)한 바 있다. 4년반(年半) 이상(以上)의 기간(其間)(