Upcoming DSM missions will produce unprecedented amounts of data during both I&T and flight, overwhelming current command/telemetry systems. The amount of data, coupled with multiple spacecraft to integrate and test could increase I&T cost and risk in a time when we are trying to reduce overall mission costs. The goal of this research project is to develop a working prototype of a software system that runs on a cluster of commodity hardware and is able to, in real-time, process and visualize 10Gbits of science data per second, and reliably store and retrieve 1TByte of science data per day. Using a real-time analytics platform for system testing will reduce overall project cost and risk by allowing for in-test decision making, interactive data exploration, and most importantly, allowing questions to be asked about our data, and ultimately about our flight system, that we were not able to ask before.
Projects typically select and use a command and telemetry system for four applications: (1) send a stream of commands (control), (2) receive and display a stream of telemetry (status), (3) convert that telemetry into human-readable units (convert), and (4) monitor the converted telemetry for potentially dangerous values (alert). A system designed for these four purposes – control, status, convert, and alert – is not designed to provide meaningful secondary data products needed to understand the performance of the spacecraft -based observatory. Nor is it designed to handle the extremely high data rates that result from the combined data feed of multiple spacecraft present in a distributed spacecraft mission. As a result, these systems are heavily supplemented with offline data processing tools.
Offline data processing typically requires a data archive of all the raw and converted telemetry, a system for requesting the data, and then a diverse set of custom-built tools for analyzing the data. The custom-built tools are often developed in MATLAB, IDL, python, Excel, or other desktop applications. Once the spacecraft are close to being complete, these tools begin a conversion process into a language like C or Java so that data produced “in-flight” can be batched processed at higher rates of efficiency into secondary data products for consumption by the scientific community.
There are three fundamental problems with relying on offline data processing tools for analyzing performance during ground-based system testing: (1) The time it takes to provide performance information disqualifies it from informing any decisions that need to be made while a test is running. (2) The tools typically used in offline data processing are designed for single user desktop applications and therefore do not scale for processing large data sets. (3) The turn-around time required to restructure a data inquiry often prohibits data exploration.
The real-time analytical test system is perfectly suited to address key characteristics of distributed spacecraft missions which are unable to be met by the current technologies. Here are some of the ways that this technology will enable future distributed missions:
|Organizations Performing Work||Role||Type||Location|
|Goddard Space Flight Center (GSFC)||Lead Organization||NASA Center||Greenbelt, MD|