The Method of Failure Management through Big Data Flow Management in Platform Service Operation Environment

Song-Ki Baik, Jae-Hyun Lim
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Abstract

Recently, a situation in which a specific content service is impossible worldwide has occurred due to a failure of the platform service and a significant social and economic problem has been caused in the global service market. In order to secure the stability of platform services, intelligent platform operation management is required. In this study, big data flow management(BDFM) and implementation method were proposed to quickly detect to abnormal service status in the platform operation environment. As a result of analyzing, BDFM technique improved the characteristics of abnormal failure detection by more than 30% compared to the traditional NMS. The big data flow management method has the advantage of being able to quickly detect platform system failures and abnormal service conditions, and it is expected that when connected with AI-based technology, platform management is performed intelligently and the ability to prevent and preserve failures can be greatly improved.
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平台服务运行环境下基于大数据流管理的故障管理方法
最近,由于平台服务的失败,在全球范围内出现了无法提供特定内容服务的情况,在全球服务市场造成了重大的社会和经济问题。为了保证平台服务的稳定性,需要对平台运营进行智能化管理。本研究提出了快速检测平台运行环境中异常服务状态的大数据流管理(BDFM)及其实现方法。分析结果表明,BDFM技术与传统网管相比,异常故障检测特性提高了30%以上。大数据流管理方法的优点是能够快速检测平台系统故障和异常服务状态,期望与基于人工智能的技术相结合,实现平台管理的智能化,大大提高故障的预防和保存能力。
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