Real-Time Systems Prof. Chenyang Lu TAs: Ruixuan Dai, Jiangnan Liu - PowerPoint PPT Presentation

CSE 520S Real-Time Systems Prof. Chenyang Lu TAs: Ruixuan Dai, Jiangnan Liu

Real-Time Systems Ø Systems operating under timing constraints Ø Safety-critical systems q Automobiles. q Airplanes. q Mars rovers. q Factory automation. q Air traffic control. Ø Time-sensitive systems q Game console, Google Stadia. q Stock trading. Ø >95% of microprocessors are used for embedded systems. 2

Embedding a Computer analog analog output actuators analog analog sensors input CPU mem embedded computer 3

Anti-lock Brake System Ø Pumps brakes to reduce skidding: real-time à safety sensor sensor brake brake hydraulic ABS pump brake brake sensor sensor 4

GM Super Cruise 5

A Distributed Real-Time System ECU A Microcontroller 1 Brake Controller Core 1 Core 2 Radar Radar Camera Microcontroller 2 CAN Bus Steering Core 1 Core 2 FlexRay #1 Controller Channel B CAN Bus Engine ECU B FlexRay #2 Controller Channel A Microcontroller 1 Core 1 Core 2 Transmission Radar Radar Camera Controller Microcontroller 2 Core 1 Core 2 Courtesy: GM 6

More on a Car ~100 microprocessors: Ø 4-bit microcontroller checks seat belt; Ø microcontrollers run dashboard devices; Ø 16/32-bit microprocessor controls engine; Ø In-Vehicle Infotainment (IVI): audio/video, navigation, communication… 7

Real-Time Applications in a Car Ø Soft real-time: Infotainment on Linux or Android Ø Hard real-time: Safety-critical control on AUTOSAR Source: http://www.edn.com/design/automotive/4399434/Multicore-and-virtualization-in-automotive-environments 1/14/20 8

Smart Civil Infrastructure Cyber-Physical Boundary WU/Purdue Real-Time Hybrid Simulation - Enabled by real-time parallel computing - Expand to larger-scale, multi-specimen experiments (bridge spanning a river, different ground motions on each end) - Towards cloud-based multi-site experiments 9

Internet of Things Ø Convergence of q Miniaturized devices: processor+sensors+radio, embedded OS. q Low-power wireless: connect millions of devices to the Internet. q Data analytics: make sense of sensor data. q Cloud and edge computing: scalable real-time data processing. Ø Large-scale IoT -driven control q Smart manufacturing, transportation, power grid, healthcare… q Closed-loop control requires real-time performance!

Clinical Warning Rapid Response R. Dor, G. Hackmann, Z. Yang, C. Lu, Y. Chen, M. Kollef and T.C. Bailey, Experiences with an End-To-End Wireless Clinical Monitoring System, Conference on Wireless Health (WH'12), October 2012. 11

IoT-driven Control Ø Smart manufacturing, transportation, grid, healthcare… Ø Closed-loop control à latency bounds Ø End-to-end latency: devices–wireless– edge –internet– cloud Senso r Actuato r sensor data control command WirelessHART in Process Industries Controlle [Courtesy: Emerson Process Management] r

Real-Time IoT End-to-End Real-Time Performance Ø Miniaturized devices à real-time embedded systems Ø Low-power wireless à real-time wireless Ø Data analytics à real-time analytics Ø Cloud à real-time data processing 13

Real-Time Cloud Ø IoT à large-scale sensing and control of physical world q Smart manufacturing, smart transportation, smart grid… q Feedback control demands real-time performance guarantees. Ø Example: Intelligent Transportation q Cloud collects data from cameras and roadside detectors. q Control the traffic signals and message signs in real-time. q Transportation information feed to drivers. q SCATS @ Sydney: controlling 3,400 signals at 1s round-trip latency. Ø Cloud needs to be real-time and predictable! q URL: https://youtu.be/CluvnRaVhqA 14

Real-Time Cloud Ø Support real-time applications in the cloud. q Latency guarantees for tasks running in virtual machines (VMs). q Real-time performance isolation between VMs. q Resource sharing between real-time and non-real-time VMs. Ø Real-time cloud stack. q RT -Xen à real-time VM scheduling q VATC à real-time network I/O on a virtualized host. q RT -OpenStack à real-time cloud resource management. Cyber-Physical RT Cilk Event Processing Plus -OpenStack VATC: RT Network I/O Latency guarantees RT 15

Example: RT-Xen Ø Real-time schedulers in the Xen hypervisor. Ø Provide real-time guarantees to tasks in VMs. Ø Incorporated in Xen 4.5 as the rtds scheduler. RT-Xen https://sites.google.com/site/realtimexen/ S. Xi, M. Xu, C. Lu, L. Phan, C. Gill, O. Sokolsky and I. Lee, Real-Time Multi-Core Virtual Machine Scheduling in Xen, ACM International Conference on Embedded Software (EMSOFT'14), October 2014. 16

Challenges Must meet non-functional constraints Ø Real-time Ø Memory Ø Battery lifetime Ø Reliability, safety and certification Ø Cost Correct output is NOT enough! 17

Real-time Requirements Ø Period: release a job every T sec q Playback 30 video frames per second Ø Deadline: complete a job within D sec q Anti-lock brake must start within 10 ms after skidding starts 18

Hard vs. Soft Real-Time Ø Hard: violating timing constraints à failure q Automobile: active safety features, autonomous driving q Air traffic control Ø Soft: violating timing constraints à inconvenience q Video q Audio ( “ harder ” than video) q Stock trading 19

Topics 1. Real-Time Operating Systems 2. Real-Time Scheduling 3. Real-Time Edge Computing 4. Real-Time Parallel Computing 5. Real-Time Virtualization and Cloud Computing 6. Real-Time End-to-End Scheduling 7. Adaptive Quality of Service Control 8. Industrial Wireless Control 9. Project: Cloud Middleware for IoT q Based on Amazon Web Services (AWS) 20

Grading Ø Projects 60% q Cloud warm-up homework: 1% q Proposal and presentation: 10% q Demo 1: 5% q Demo 2: 5% q Final demo & report: 39% Ø Critiques 35% Ø Participation 5% 21

Critiques Ø 1/2 page critiques of research papers Ø Submit by 10am before class Ø Back-of-envelop comments - NOT whole essays Ø See guidelines on class web site q http://www.cs.wustl.edu/%7Elu/cse521s/critique.html 22

Project Ø Three students per team Ø Build IoT systems based on cloud q Front end: smart watch, wristband, Raspberry Pi q Cloud backend: storage, analytics, Alexa, notification q Write a paper q Demo to the class 23

Smartwatch as a Healthcare Tool Continuous, passive Open, programmable measurements platform activity, heart Wear OS, Research Kit, rate, sleep, onboard analytics location… T wo-way communication ecological momentary assessments 12/19/2019 Chenyang Lu 24

Raspberry Pi https://cse.wustl.edu/Pages/default.aspx 25

Amazon Web Services (AWS) IoT United: Connect + Communication Smart: Other Cloud Service Data Storage Machine Learning Source: https://aws.amazon.com/iot-platform/ 1/14/20 26

Timed Up and Go with Smartwatch Ø Watch app q Remind participants to take the assessment q Automatically upload the data to the cloud for analysis q Analyze gait and motion features q Feedback to physicians and participants https://www.cse.wustl.edu/~lu/TUG.mp4 Ø Assess physical health and fall risk during prehabilitation. q 20 participants undergoing neoadjuvant radiotherapy followed by surgery q Patients will complete TUG at home with the smartwatch for 90 days. Joint work with Matthew Spraker (Radiation Oncology), Ruixuan Dai (CSE) 12/19/2019 Chenyang Lu 27

Voice-based Smart Medicine Dispenser https://www.cse.wustl.edu/~lu/cse521s/Videos/medicine_dispenser.mp4 28

Steps 1. Choose your favorite topic 2. Form a team 3. Propose a plan 4. Implement 5. Measure and analyze 6. Demo: 1, 2, final 7. Write a technical report 29

Start Early and Work Often! Ø Choose topics Ø Put together a team Ø Meet every week to coordinate Ø Lots of development and experiments throughout the semester! 30

Pointers Ø http://www.cse.wustl.edu/~lu/cse520s/ Ø Email for appointment q Chenyang (Jolley 213) q Ruixuan Dai (Jolley 219A): Projects q Jiangnan Liu (Jolley 219A): Critiques 31