Juno Instrument Data Pipeline Monitoring a.k.a. Where s My Data?"
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1 Juno Instrument Data Pipeline Monitoring a.k.a. Where s My Data?" Maddalena Jackson Marla Thornton Jet Propulsion Laboratory, California Institute of Technology 2012 by JPL/Caltech. Published by The Aerospace Corporation with permission.
2 Overview Who are we? What was the problem? What did we need? (Situational Awareness) What did we do? (Monitoring Service) How did we do it? (Rapid System Development, Model Based Systems Engineering) What did we learn? Recommendations
3 Mission Background Salient Features Juno is a Category 1, Class B mission First solar powered mission to the outer planets Eightinstrumentpayload instrument to conduct gravity, magnetic and atmospheric investigations plus an E/PO camera Polar orbiter spacecraft launches in August year cruise to Jupiter, JOI in July year operations, EOM via de orbit into Jupiter in 2017 Elliptical 11 day orbit swings below radiation belts to minimize radiation exposure Key Juno partners: SwRI, JPL, ASI, LM Denver and GSFC Instruments GRAVITY SCIENCE & MAGNETOMETERS (ASC, FGM) Study Jupiter s deep structure by mapping the planet s gravity field & magnetic field MICROWAVE RADIOMETER (MWR) Probe Jupiter s deep atmosphere and measure how much water (and hence oxygen) is there JEDI, JADE & WAVES Sample particles, electric fields and radio waves around Jupiter to determine how the magnetic field inside the planet is connected to the atmosphere and magnetosphere especially the auroras UVS & JIRAM Tk Take images of the atmosphere and auroras, along with ihthe chemical lfingerprints i of gases there, with ihultraviolet l & infrared cameras JUNOCAM Take spectacular close up, color images 3
4 Background CCSDS File Delivery Protocol (CFDP) Push data to Instrument Teams Instrument Data Pipeline From ground receipt to delivery to Instrument Teams Multi mission Components Mission Data Delivery Latency Requirements Testing revealed: Mission Data Delivery Latency Requirements not met
5 Problem Great until there s a problem No visibility Different results! (Testbeds) Is this going to happen again? How do I know when something s wrong? Where is the data supposed to be? Where is it really? Why isn t it there? When was it supposed to be there? Unix detectives! Overhead! Giant spreadsheets! Is there a trend? Torrents! Phone tag!???? We need to know status in real time! (Situational Awareness)
6 Situational Awareness Need monitoring (eyes) at every point in the pipeline Multiple pipelines (testbed vs. ops) Analyze performance (throughput, latency) Compare products Detect problems Let s automate this and do it before we need it!
7 Monitoring Service What do need the service to do? Function: Collect data Modular (one monitor per IDP node) Distributed (runs at the node) Read only (no effect on IDP) Function: Display data and analysis Display by current test, instrument, IDP component Remote access (website) Automated performance analysis Implies Standard agent web server interface Automation (point is to save time) Extensible (transition from ATLO to Operations) Test Like You Fly (TLYF)
8 Monitoring Parameters What should the software monitor? Size, name, arrival time, completion time Provides knowledge oflocation, enables comparison, and determination of bottlenecks
9 Services Product Status Data Plug and play monitoring agents Standard HTTP interface Open source web server / database framework Register of products in real time Accessible to team members from any location Shows latest location of products and product data Shows latencies and bottlenecks
10 Rapid System Development Needed functional system ASAP Ready to support Juno Thermal Vacuum Testing Rapid development cycle Development Customer Requests Testing Team: Customer Review Senior Programmer (Developer, Tester) Mission i Expert t(developer, Tester) Mission Personnel (Users & Customers)
11 Impact of Rapid System Development Knowledge Transfer Software experience Mission experience Test Like You Fly Constant testing in operations environment Quick customer feedback Iteration, refining needs > different software design Refactoring, re design induces some overhead Agile development Final product highly optimized for customer Accommodates evolving knowledge of pipeline Reverse engineer some requirements, test cases, and documentation Notime for full waterfall systems engineering process at each iteration
12 Product delivered what next? Institution requires capture of: Requirements Design Implementation Testing 5 year cruise information archive Who will operate it when we get to Jupiter? Model based dlb dsystems Engineering i (in SysML) Standard views Supports description/design of those factors Visualize dependencies Gold source / centralized SE capture Canbe used for future design
13 MBSE Process Requirements spreadsheet Captured components Captured component logical/functional design Discovered areas of weakness Discovered redundancy Discovered inconsistencies Captured Requirements in Model Analyzed discrepancies between requirements and design Improved requirements Captured implementation Link to requirements Design Document Captured testing Test Plans Analyze testing completeness Operating Procedures Link to requirements Implementation Descriptions Link to functions Requirements Documents Generated Artifacts
14 Conclusions Situational Awareness Tedious by hand Easy with software Service Orientation Simple (for distributed monitoring) Flexible Extensible Robust (modular) MBSE Standard artifacts Central gold source of systems engineering Reveals weaknesses Analysis of Systems Engineering dependencies Rapid System Development Knowledge Transfer Leverage open source frameworks and libraries TLYF
15 Recommendations Situational Awareness Analyze current (tedious) processes Define metrics Service Orientation Standardize external interfaces (with GDS systems) Rapid System Development Clear roles/expertise Codify development/delivery cycle Integrate functional design Document all feature/functionality requests Invest time in documenting use cases /concept of operations MBSE Involve earlier in the process (functional) Capture requirements and rationale in real time Keep an up to date capability / function / subsystem mapping Document range of TLYF events as test cases Create/maintain procedures in real time
16 Backup Slides
17 Solution How do we do it by hand? Unix Detectives Can we find the last known location of the file? Call people if we don t have access to parts of the pipeline Did you see this product go through? Write things in a notebook Product ID, arrival time, file size, build time, etc. (add to the torrent) Here s a report of the problem, file it away somewhere! Get vague impressions over time I think it gets slower when this instrument is turned on hmm! TEDIUM Put things in a giant spreadsheet and try to find a trend? Repeat the time consuming process
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