winlab 1 roy yates ece/winlab, rutgers nsf workshop on ultra low latency wireless networks march 26,...

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Roy YatesECE/WINLAB, Rutgers

NSF Workshop on Ultra Low Latency Wireless Networks March 26, 2015

Age of Information Status Updating Systems and Networks

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Motivation

• 50 years of rate maximization– at the expense of delay

• long (coded) packets on wireless channels,• ARQ • video streaming with large delays to absorb

packet jitter• Caching to compensate for network latency

• high throughput “best-effort” networks

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Applications(The Samsung 5G List)

• V2X – timely delivery of critical messages for traffic safety

• Mission-Critical IoT (M2M)– mission-critical systems – process monitoring/detection and

disaster response

• Virtual/Augmented Reality– seamless virtual/real-world interaction

• Real-time remote access (tactile feedback)– long range, real-time control for remote surgery, driving,

etc.

• Everything-on-Cloud– instantaneous cloud-based services/multimedia content

~1 ms

1-10 ms

10-100ms

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Applications(The Samsung 5G List)

• V2X – timely delivery of critical messages for traffic safety

• Mission-Critical IoT (M2M)– mission-critical systems – process monitoring/detection and

disaster response

• Virtual/Augmented Reality– seamless virtual/real-world interaction

• Real-time remote access (tactile feedback)– long range, real-time control for remote surgery, driving,

etc.

• Everything-on-Cloud– instantaneous cloud-based services/multimedia content

~1 ms

1-10 ms

10-100ms

57 mph=

1 inch/ms

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Applications(The Samsung 5G List)

• V2X – timely delivery of critical messages for traffic safety

• Mission-Critical IoT (M2M)– mission-critical systems – process monitoring/detection and

disaster response

• Virtual/Augmented Reality– seamless virtual/real-world interaction

• Real-time remote access (tactile feedback)– long range, real-time control for remote surgery, driving,

etc.

• Everything-on-Cloud– instantaneous cloud-based services/multimedia content

• Wireless Network on Chip – Cloud on Chip?

~1 ms

1-10 ms

10-100ms

?? ms

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Network Delay(H. Viswanathan, Bell Labs)

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Remote Surgery

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Remote Surgery

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PHY

• Wider Channels (but not UWB )– 30+ GHz mmWave

• M2M: reliability is essential

• Practice: Emerging low latency 5G– Channel Estimation, Modulation, Coding,

Framing • (check out Fettweis CTW 2013 talk)

• Theory:– Delay-Limited Capacity, Short blocklength

source/channel coding

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Latency-Sensitive MAC

• Practice:– 2G/3G packet voice MAC– LTE Scheduling– Sleep protocols!

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Latency-Sensitive MAC

• Practice:– 2G/3G packet voice MAC– LTE Scheduling– Sleep protocols!

• What Randy said: “CSMA style random access seems ill-matched to low latency unless the network is very underutilized.”

• Theory: Are rate/delay tradeoffs fundamental?

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Large Networks (Hundreds of cars)

Frequent Updates

Reliability and Timeliness are required

V2V Safety Messaging

Source

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• DSRC standard MAC protocol• Message Scheduling, Forwarding/Piggybacking• Power/rate adaptation, coverage …

• Performance Metrics?

V2V Safety Messaging

Source

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• Car u sends updates to car v• Updates pass through network/service

system• Car v wants latest state information.

• Metric: Age of the latest status update

Network

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Update Age

D(t) UpdateSent

Received

tt1 t2t1’ t2'

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Update Age

D(t)

• Low Update Rate Age gets large between

updates

UpdateArrival

Departure

tt1 t2t1’ t2'

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Update Age

D(t)

• High Update Rate Queueing Delay

t1 t2 t1’ t2't3 t3'

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Average Update Age

D(t)

• Update Rate:• High Queueing delays• Low Infrequent

updates

High Average Age

Average Age

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FCFS Average Update Age

D(t)

𝑋 𝑇

• X= Interarrival Time

• T= System Time

• Weak ergodicity requirements• E[XT ] is tricky, negative correlation

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Average Age

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Average Age

Nothroughput/

delaytradeoff

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Other Age Metrics

Average Peak AgeD1

D(t)

D(t)

D2

D3

D* P[ (D t)>D* ]

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Competing Updates

• How often is too often?

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Multiple Sources

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Multiple Sources

Models for Source 2:• Competing status updater• Other traffic

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Multiple SourcesFCFS Status Age Region

OptimalSharing

Nash Equilibriu

m

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𝜇Source

1

Source2

Monitor

• Queueing delays increases status age

• Reduce/Eliminate the queues?• “Packet Management”

[Costa, Codreanu, Ephremides ISIT’14]

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LCFSPre-emption & Discarding

(No Queueing)

𝜇Source

1

Source2

Monitor

l1

l2

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V2V Safety Messaging

Network

• Multiple Sources• Fast local server interface • Slow Server

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Multiple SourcesFCFS/LCFS Age

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Timely CompressionA Status Updating Problem

• Encoder input symbol = status update • Age = Decoder symbol lag• Block coding Bursty bit arrivals at FIFO buffer

Bit pipe queueing delay Decoding delay

• [Sahai&Cheng ISIT’07]

a1a2a3…

Encoder FIFObuffer

Rate Rbit pipe Decoder

a1a2a3…01 110 11…

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Timely CompressionA Status Updating Problem

Encoder

01 110 11…

FIFObuffer

Rate Rbit pipe Decoder

a1 a2 a3 a4 a5 a6 a7 …

ta1 a2 a3a4 …

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Timely CompressionHuffman Block Coding Example

Channel Rate R

Sta

tus

Ag

e

High rate pipe:

use small blocks

Low rate pipe:

Use large blocks

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Summary

• Information Age Minimization– Match the load to the network/system

• “Rate” is an input for controlling delay – Redesign the system

• Give priority to timely updates• Packet Management

• Ultra Low Latency Networks– Sub 1ms latency applications?

• Better Theory for Network Latency