Development of a complex failure prediction system using Hierarchical Attention Network (Hierarchical Attention Network를 이용한 복합 장애 발생 예측 시스템 개발)
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- 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.
In this study, formation background of biodiversity and its changes in the process of geologic history, and effects of climate change on biodiversity and human were discussed and the alternatives to reduce the effects of climate change were suggested. Biodiversity is 'the variety of life' and refers collectively to variation at all levels of biological organization. That is, biodiversity encompasses the genes, species and ecosystems and their interactions. It provides the basis for ecosystems and the services on which all people fundamentally depend. Nevertheless, today, biodiversity is increasingly threatened, usually as the result of human activity. Diverse organisms on earth, which are estimated as 10 to 30 million species, are the result of adaptation and evolution to various environments through long history of four billion years since the birth of life. Countlessly many organisms composing biodiversity have specific characteristics, respectively and are interrelated with each other through diverse relationship. Environment of the earth, on which we live, has also created for long years through extensive relationship and interaction of those organisms. We mankind also live through interrelationship with the other organisms as an organism. The man cannot lives without the other organisms around him. Even though so, human beings accelerate mean extinction rate about 1,000 times compared with that of the past for recent several years. We have to conserve biodiversity for plentiful life of our future generation and are responsible for sustainable use of biodiversity. Korea has achieved faster economic growth than any other countries in the world. On the other hand, Korea had hold originally rich biodiversity as it is not only a peninsula country stretched lengthily from north to south but also three sides are surrounded by sea. But they disappeared increasingly in the process of fast economic growth. Korean people have created specific Korean culture by coexistence with nature through a long history of agriculture, forestry, and fishery. But in recent years, the relationship between Korean and nature became far in the processes of introduction of western culture and development of science and technology and specific natural feature born from harmonious combination between nature and culture disappears more and more. Population of Korea is expected to be reduced as contrasted with world population growing continuously. At this time, we need to restore biodiversity damaged in the processes of rapid population growth and economic development in concert with recovery of natural ecosystem due to population decrease. There were grand extinction events of five times since the birth of life on the earth. Modern extinction is very rapid and human activity is major causal factor. In these respects, it is distinguished from the past one. Climate change is real. Biodiversity is very vulnerable to climate change. If organisms did not find a survival method such as 'adaptation through evolution', 'movement to the other place where they can exist', and so on in the changed environment, they would extinct. In this respect, if climate change is continued, biodiversity should be damaged greatly. Furthermore, climate change would also influence on human life and socio-economic environment through change of biodiversity. Therefore, we need to grasp the effects that climate change influences on biodiversity more actively and further to prepare the alternatives to reduce the damage. Change of phenology, change of distribution range including vegetation shift, disharmony of interaction among organisms, reduction of reproduction and growth rates due to odd food chain, degradation of coral reef, and so on are emerged as the effects of climate change on biodiversity. Expansion of infectious disease, reduction of food production, change of cultivation range of crops, change of fishing ground and time, and so on appear as the effects on human. To solve climate change problem, first of all, we need to mitigate climate change by reducing discharge of warming gases. But even though we now stop discharge of warming gases, climate change is expected to be continued for the time being. In this respect, preparing adaptive strategy of climate change can be more realistic. Continuous monitoring to observe the effects of climate change on biodiversity and establishment of monitoring system have to be preceded over all others. Insurance of diverse ecological spaces where biodiversity can establish, assisted migration, and establishment of horizontal network from south to north and vertical one from lowland to upland ecological networks could be recommended as the alternatives to aid adaptation of biodiversity to the changing climate.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70