End-to-end Real-time Guarantees in Wireless Cyber-physical ...

End-to-end Real-time Guarantees in Wireless Cyber-physical Systems

Romain JacobPengcheng Huang Lothar Thiele

RTSS 16 - IoT and Networking SessionDecember 1, 2016

Marco ZimmerlingJan Beutel

Predictability is key!

2

A Cyber-physical System goes beyond a real-time wireless network

Real-time guarantees > Deadlines are met

3

Wireless network Low-power Multi-hop

Network deadline

Predictability


Real-time guarantees > End-to-end deadlines are met

Buffer management > No buffer overflow

4


Controller

Actuator

Sensor

End-to-end deadline

Network deadline

Predictability


Real-time guarantees > End-to-end deadlines are met

Buffer management > No buffer overflow

5

Predictability

Local interference

Application

Communication

Predicatability

Design goals

Real-time guarantees

Buffer management

Adaptability

Efficiency

Composability

6

Network

Blink

Device

?

Bolt

System

?

?

?

?

DRP

First A predictable and adaptivepiece wireless network

7


Controller

Actuator

Sensor

First A predictable and adaptivepiece wireless network

8


Controller

Actuator

Sensor

State-of-the-art wireless protocolsare predictable or adaptive

Splash, RAP EfficientAdaptive> not Predictable

WirelessHART PredictableEfficient> not Adaptive

9

Blink A real-time, reliable and[1] adaptive wireless protocol

Adaptive Based on Glossy> Flooding primitive

Reliable Average 99.97% reception rate> Multiple testbeds> Tested up to 94 nodes

Real-time Online scheduling> EDF-based Lazy Scheduling

10

[1] Zimmerling M. et al., Adaptive Real-time Communication for Wireless Cyber-physical SystemsTo appear in ACM Transactions on Cyber-Physical Systems, 2016


Abstraction Low-power Wireless Bus (LWB)> MAC protocol

Communication TDMA-basedin rounds > Sleep between rounds

> Wireless yields sync!

11

𝑇𝑛𝑒𝑡

𝐶𝑛𝑒𝑡

𝑡

𝑡

𝑡

Node 1

Node 2

Node N

…

M slots

……

…


Variable!

Sched(n+1)


Abstraction Low-power Wireless Bus (LWB)> MAC protocol

Communication TDMA-basedin rounds > Sleep between rounds

> Wireless yields sync!

12

𝑇𝑛𝑒𝑡

𝐶𝑛𝑒𝑡

𝑡

𝑡

𝑡

Node 1

Node 2

Node N

…

M slots

……

…


Variable!

Design goals


Buffer management

Adaptability

Efficiency

Composability

13

Network Device System

na

na

?

?

?

?

?

Blink Bolt DRP

Second A dual-processor architecturepiece to mitigate local interference

14


Controller

Actuator

Sensor

Second A dual-processor architecturepiece to mitigate local interference

15


Controller

Actuator

Sensor

Bolt A dual-processor architecture [2] to mitigate local interference

16

BOLTBOLT

APIBOLT

API

…

…Receive Buffer

flush

read

write

…

Wireless Comm. Tasks

ApplicationTasks

Communication Processor (CP)

…

Receive Buffer

flush

read

write

Application Processor (AP)

Incoming

Outgoing

Real-time Formally verifiedbehavior Implemented, tested, deployed

Efficient 𝜇𝑊 > sleep𝑚𝑊 > active

Composable Hardware/Softwarefree composition

[2] Sutton F. et al., Bolt: A Stateful Processor Interconnect, SenSys’15, 2015

Design goals


Buffer management

Adaptability

Efficiency

Composability

17


?

?

?

?

?

Blink Bolt DRP

na

na

Design goals → How can we fill the blanks?


Buffer management

Adaptability

Efficiency

Composability

18


?

?

?

?

?

Blink Bolt DRP

? na

na

Third Distributed Real-time Protocol (DRP)piece


Buffer management

Adaptability

Efficiency

Composability

19


?

?

?

?

?

?

Blink Bolt DRP

na

na

DRP is based on three main concepts

Communication is constrained within registered flows only

Global requirements are splitedacross distributed components

Interaction between componentsis based on contracts

20





21


Flow 𝑖 𝐹𝑖 = ( 𝑛𝑖𝑠, 𝑛𝑖

𝑑 , 𝑇𝑖 , 𝐽𝑖 , 𝐃𝑖 )

Release model Sporatic with jitter

22

SourceDestinationMin. release intervalJitterEnd-to-end deadline





23


DRP : 𝐷𝑖𝑠 + 𝐷𝑖

𝑛𝑒𝑡 + 𝐷𝑖𝑑 = 𝐃

end-to-end deadline : 𝐃

𝐷𝑖𝑠 𝐷𝑖

𝑛𝑒𝑡 𝐷𝑖𝑑

Wireless protocolSource Destination

24

Real-timeguarantee





25


26

> Node ↔ Network

> Max resource demandMin service delivered

>

Contract

CONSTRAINTS

ON LOCAL

SCHEDULES

BB

AA

Predictability Performance

Example

Scheduling of Communication Processors (CP)

Real-time Participate in rounds guarantees > 𝑇𝑛𝑒𝑡 Blink schedule

Flush before roundsWrite after rounds> 𝑇𝑛𝑒𝑡 Blink schedule

Buffer Flush Bolt regularlymanagement > 𝑇𝑓

𝑠 Comm. Processorschedule

27

Example


How can we guarantee that all tasks are schedulable?

28

𝐶𝑓 𝐶𝑤

Participate in rounds

Flush before rounds

Write after rounds

Flush Bolt regularly

𝑇𝑓𝑠

𝑡

𝐶𝑛𝑒𝑡

𝑇𝑛𝑒𝑡

𝑡

Example


How can we guarantee that all tasks are schedulable?

29

𝐶𝑓 𝐶𝑤


Flush before rounds

Write after rounds


𝑇𝑓𝑠

𝑡

Variable!

Variable!

𝐶𝑛𝑒𝑡

𝑇𝑛𝑒𝑡

𝑡

𝐶𝑛𝑒𝑡

𝑇𝑛𝑒𝑡

Example


How to guarantee that all tasks are schedulable?

30

𝐶𝑓 𝐶𝑤

𝑡


Flush before rounds

Write after rounds


Variable!

Variable! Conflict!𝑇𝑓𝑠

𝑡

Example



31

𝐶𝑓 𝐶𝑤𝐶𝑛𝑒𝑡

𝑇𝑛𝑒𝑡


Flush before rounds

Write after rounds


𝐶𝐶𝑃 ≜ 𝐶𝑓 + 𝐶𝑛𝑒𝑡 + 𝐶𝑤> 𝑇𝑛𝑒𝑡 ≜ 𝑘 ⋅ 𝐶𝐶𝑃

Solution TDMA approach

Variable!

Variable!

𝑡𝑇𝑓𝑠

𝑡

Example



32

𝐶𝑓 𝐶𝑤


Flush before rounds

Write after rounds


𝑡𝑇𝑓𝑠

𝐶𝐶𝑃 ≜ 𝐶𝑓 + 𝐶𝑛𝑒𝑡 + 𝐶𝑤> 𝑇𝑓

𝑠 ≜ 𝐶𝐶𝑃> 𝑇𝑛𝑒𝑡 ≜ 𝑘 ⋅ 𝐶𝐶𝑃

Fixed to min


Variable!

𝐶𝑛𝑒𝑡

𝑇𝑛𝑒𝑡

𝑡

Example



33

𝐶𝑓 𝐶𝑤𝐶𝑛𝑒𝑡

𝑇𝑛𝑒𝑡


Flush before rounds

Write after rounds


𝑡𝑇𝑓𝑠

𝐶𝐶𝑃 ≜ 𝐶𝑓 + 𝐶𝑛𝑒𝑡 + 𝐶𝑤> 𝑇𝑓

𝑠 ≜ 𝐶𝐶𝑃> 𝑇𝑛𝑒𝑡 ≜ 𝑘 ⋅ 𝐶𝐶𝑃

Variable but constrained


𝑡

Fixed to min

+ Predictability of Network + Predictability of Devices + DRP contracts

34

End-to-end latencyBuffer size

Flush interval 𝑇𝑓𝑠

Flush interval 𝑇𝑓𝑑

Network deadline 𝐷𝑖𝑛𝑒𝑡

End-to-end deadlineMemory space≤

= System predictability Worst-Case Analysis

= 𝑓( Local parameters )

Can we set localparameters such that…

If YES System predictabilityby design

Ultimately

Design goals


Buffer management

Adaptability

Efficiency

Composability

35


?

?

?

?

?

?

Blink Bolt DRP

na

na

Predicatability

Design goals


Buffer management

Adaptability

Efficiency

Composability

36


?

?

?

?

?

?

Blink Bolt DRP

na

na

From there, adaptability is one (close) step away

37

End-to-end latencyBuffer size

End-to-end deadlineMemory space≤

Can we set localparameters such that…

ADMISSION

TESTS

From there, adaptability is one (close) step away

38

ADMISSION

TESTS

Depends only on local parameters!

Example

Communication Processor

Adaptability is achieved via a distributed registration scheme

39

LOGICAL LAYER

PHYSICAL LAYER

request : 𝑇𝑖 , 𝐽𝑖𝐷𝑖 , 𝐃i YES Y

APs CPsBOLT APdBOLTCPdBLINKPacket

flow

Bolt buffer andlocal memory OK?

Abort

Register flow

Abort

Register flow

Abort

Register flow and update schedule

YES

Abort

Register flow andupdate schedule

Abort

Register flow and update schedule

Compute 𝐷𝑖

𝑇𝑖 , 𝐽𝑖𝐃i

Networkschedule OK?

Local memory OK?

update : 𝑇𝑓𝑑

YES

nack

ack

end-to-end latency ≤ 𝐃i

YES

REGISTRATION

SCHEMEADMISSION

TESTS

NO NO NO NO

𝑓(𝑇𝑓𝑠, 𝐷𝑖) 𝑔(𝑇𝑓

𝑑)

Bolt buffer

and 𝑇𝑓𝑑 OK?

nack

ack

nack

ack

nack

ack

Predicatability

Design goals


Buffer management

Adaptability

Efficiency

Composability

40


?

?

Blink Bolt DRP

na

na

Adaptability

The detailed system analysis allows for performance optimization

Minimal admissible end-to-end deadline

𝐃𝑚𝑖𝑛 = 𝛿𝑓𝑐𝑜𝑛𝑠𝑡 + 𝑇𝑚𝑖𝑛 + 𝑇𝑚𝑖𝑛 + 𝛿𝑔

𝑐𝑜𝑛𝑠𝑡 + 𝑇𝑓,𝑚𝑖𝑛𝑑

41

PacketflowAPs CPsBOLT APdBOLTCPdBLINK

end-to-end latency ≥ 𝐃𝑚𝑖𝑛





42


end-to-end latency ≥ 𝐃𝑚𝑖𝑛Bolt delays

Source delay> Message release by the source> Available for communication





43



Network delay: Waiting time > Message available> Message processed by the network





44



Network delay: Transmission > Message processed by the network> Message transmitted to the destination node





45



Destination delay: Bolt > Message transmitted to the destination node> Message available in the Bolt queue





46



Destination delay: Application > Message available in the Bolt queue> Message retrieved by the destination application





Given Packet size 32 Bytes

𝐶𝑛𝑒𝑡 1 𝑠

𝑇𝑓,𝑚𝑖𝑛𝑑 0.1 𝑠

> 𝐃𝑚𝑖𝑛 3.46 𝑠Max data rate 29.7 𝐵𝑝𝑠 per flow

47

Predicatability

Design goals


Buffer management

Adaptability

Efficiency

Composability

48


?

Blink Bolt DRP

na

na

Using flooding primitives

enables the design of both

adaptive AND predictable

Wireless Cyber-Physical Systems

49

End-to-end Real-time Guarantees in Wireless Cyber-physical Systems

Romain JacobPengcheng Huang Lothar Thiele

RTSS 16 - IoT and Networking SessionDecember 1, 2016

Marco ZimmerlingJan Beutel

Simulation correlates closely with the analysis

Typical simulation trace result

49

End-to-end Real-time Guarantees in Wireless Cyber-physical ...

Documents

Transcript of End-to-end Real-time Guarantees in Wireless Cyber-physical ...