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Service Quality Assessment for NASA's Deep Space Network: No Longer a Luxury

机译:美国宇航局的深度空间网络服务质量评估:不再是奢侈品

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When NASA's Deep Space Network (DSN) was established almost a half century ago, the concept of computer-based service delivery was impractical or infeasible due to the state of information technology As a result, the interface the DSN exposes to its customers tends to be equipment-centric, lacking a clear demarcation between the DSN and the mission operation systems (MOS) of its customers. As the number of customers has continued to increase, the need to improve efficiency and minimize costs has grown. This growth has been the impetus for a DSN transformation from an equipment-for-rent provider to a provider of standard services. Service orientation naturally leads to requirements for service management, including proactive measurement of service quality and service levels as well as the efficiency of internal processes and the performance of service provisioning systems. DSN System Engineering has surveyed industry offerings to determine if commercial successes in decision support and Business Intelligence (BI) solutions can be applied to the DSN. A pilot project was initiated, and subsequently executed to determine the feasibility of repurposing a commercial Business Intelligence platform for engineering analysis in conjunction with the platform's intended business reporting and analysis functions. This paper surveys the challenges, the lessons learned, and highlights the interesting results to date. It will also highlight the technologies applied to achieve these goals including: (1) Business Intelligence Software; (2) Data Warehousing; (3) ETL (Extract, Translate and Load) techniques; (4) Complex Event Processing.
机译:当NASA的深度空间网络(DSN)成立近半个世纪前时,由于信息技术的状态,基于计算机的服务交付的概念是不切实际或不可行的,因此DSN暴露于客户的界面往往是以设备为中心,缺乏DSN和客户的任务操作系统(MOS)之间的明确划分。随着客户的数量持续增加,需要提高效率和最小化成本的需求。这种增长是从租金供应商到标准服务提供商的DSN转换的推动。服务方向自然地导致服务管理的要求,包括服务质量和服务水平的主动测量以及内部流程的效率和服务供应系统的性能。 DSN系统工程有调查的行业产品,以确定决策支持和商业智能(BI)解决方案中的商业取得成功,可应用于DSN。启动了试点项目,随后执行,以确定重新调整商业商业智能平台的可行性与平台的预期业务报告和分析功能结合使用。本文调查了挑战,验证的经验教训,并突出显示迄今为止的有趣结果。它还突出显示应用这些目标的技术,包括:(1)商业智能软件; (2)数据仓库; (3)ETL(提取,翻译和负载)技术; (4)复杂事件处理。

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