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A Practical Congestion Control Scheme for Named Data Networking ACM ICN 2016 Klaus Schneider 1 , Cheng Yi 2 , Beichuan Zhang 1 , Lixia Zhang 3 September 27, 2016 1 The University of Arizona, 2 Google, 3 UCLA 1 NDN and IP are different! NDN and

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A Practical Congestion Control Scheme for Named Data Networking

ACM ICN 2016

Klaus Schneider1, Cheng Yi2, Beichuan Zhang1, Lixia Zhang3 September 27, 2016

1The University of Arizona, 2Google, 3UCLA

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NDN and IP are different!

NDN and IP networks are different:

  • 1. Traditional Congestion Control doesn’t work for NDN
  • 2. Related work often makes too strong assumptions

⇒ Developing a more practical solution (PCON)

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Multiple Paths and Endpoints

Mixing RTT measurements from different sources ⇒ Problem: Traditional RTO settings often too short

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Multiple Paths and Endpoints

Mixing RTT measurements from different sources ⇒ Problem: Traditional RTO settings often too short

  • 1. Use Route-labels to know content origin and path [3]
  • Still don’t know where next Interest will go! [7]

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Multiple Paths and Endpoints

Mixing RTT measurements from different sources ⇒ Problem: Traditional RTO settings often too short

  • 1. Use Route-labels to know content origin and path [3]
  • Still don’t know where next Interest will go! [7]
  • 2. Predicting location of future data [12, 1]
  • Routers mark Data to indicate their content
  • Overhead? Reliable?

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Hop-By-Hop Interest Shaping

At each hop: Shape Interests to control returning Data.

Source: Wang et al. - An Improved HBH Interest Shaper for NDN [14]

Much work [2, 14, 11, 4, 17, 16, 10, 5] based on that principle!

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Hop-By-Hop Interest Shaping

HBH Interest Shaping assumes that you

  • know the link capacity
  • know the Data chunk size

Estimation errors cost performance!

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PCON: Design Principles

Remove strong assumptions about the network:

  • Unknown link capacity & Data chunk size
  • No route-labels or prediction of data location

Design Principles:

  • Detect congestion at the bottleneck!
  • Signal it towards consumer

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System Design: Overview

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System Design: Overview

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System Design: Overview

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System Design: Overview

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System Design: Overview

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System Design: 1. Congestion Detection

Based on CoDel AQM [9, 8] (or any other AQM) Monitor both downstream and upstream direction!

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System Design: 2. Congestion Signaling

Signaling = Marking NDN Data packets with congestion bit.

  • Using CoDel’s drop spacing logic

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System Design: 3. Consumer Reaction

AIMD window adaptation

  • We use TCP BIC [15], but any loss-based TCP algorithm

works

  • Window decrease on marked Data, NACK, and timeout.

PCON removes traditional sources of packet drops!

  • Router buffer overflows
  • Drops by AQM mechanism
  • Drops by the “link” (UDP tunnel, Wireless)

⇒ Allows to use higher RTOs!

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System Design: 4. Multipath Forwarding

Adjust the forwarding ratio at each router

  • Related work: based on RTT or Pending Interests
  • PCON: based on congestion marks

Start on shortest path; when link congested, divert traffic! fwPerc(F) − = CHANGE PER MARK fwPerc(¯ F) + = CHANGE PER MARK

NUM FACES−1

When congestion disappears, shift back to shortest path!

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System Design: 4. Multipath Forwarding Example

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System Design: 4. Multipath Forwarding Example

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System Design: 4. Multipath Forwarding Example

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System Design: 4. Multipath Forwarding Example

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System Design: 5. Local Link Loss Detection

Problems in diverse deployment scenarios:

  • Wireless Links: Lose packets unrelated to congestion
  • IP Overlays: UDP tunnels lose packets without notice

Solution: Detect packet loss with a shim layer based on positive ACKs [13]; signal consumer with NACK

  • Unmarked NACK: Only retx, no window adaptation
  • Marked NACK: Both retx and window adaptation

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Evaluation: Caching & Multicast

PCON vs. CoDel dropping queues (traditional RTO timer) Both consumers request same data; C2 starts at 5s.

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Evaluation: Caching & Multicast

CODEL 10 20 30 40 50 5 10 15

TP [Mbit/s] Scenario

consumer1 consumer2 −50 −25 25 50 75 5 10 15

RTO−RTT [ms]

100 200 300 400 5 10 15

Time [s] RTT [ms]

Results: TCP Timers cause spurious retransmissions!

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Evaluation: Caching & Multicast

PCON 10 20 30 40 50 5 10 15

TP [Mbit/s] Scenario

consumer1 consumer2 100 200 300 400 5 10 15

Time [s] RTT [ms]

Results: TCP Timers cause spurious retransmissions!

  • PCON can use a fixed higher RTO!

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Evaluation: Multipath Forwarding

Compare PCON against PI-based forward adaptation

  • PI: Choose face with minimum PI
  • CF [3]: Weighted round-robin, based on PI
  • PCON: Adapt to congestion marks

Equal Split Diff Delay Diff BW

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Evaluation: Multipath Forwarding

Split Ratio at R2:

Equal Diff_Delay Diff_BW 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 257 258 259 257 258 259 257 258 259

faceid

  • Forw. Perc.

5 10 15 20 25 Equal Diff_DelayDiff_BW

Scenario Rate [Mbit/s]

PCON CF PI

  • PI and CF bias against High-Delay and High-BW paths [7]

PCON achieves optimal split, i.e, maximizes throughput!

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Evaluation: PCON vs. ICP

PCON vs. ICP with Route-labeling, RAAQM, and CF [3] Consumers start with path C–R1–R2–P1.

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Evaluation: PCON vs. ICP

ICP PCON 10 20 30 40 50 60 10 20 10 20

Rate [Mbit/s] Node

all C1 C2 C3 C4 C5 0.0 0.2 0.4 0.6 0.8 1.0 10 20 10 20

  • FwPerc. Node R2

FaceId

257 258 259 25 50 75 100 10 20 10 20

Time [s] Queue [Pkts]

P1 P2 P3 P4

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Evaluation: PCON vs. ICP

ICP PCON RTT [ms] Rate [Mbps] RTT [ms] Rate [Mbps] C1 128.31 4.48 132.08 5.53 C2 107.83 5.16 112.37 6.77 C3 88.28 6.34 92.26 9.32 C4 68.01 7.86 72.19 12.69 C5 48.21 12.20 52.46 20.52 All 78.08 7.21 79.23 10.96

Table 1: Mean RTT and Rate per Consumer

Trade-off: Throughput vs. Latency

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Evaluation: IP Overlay & Wireless Links

Compare against simplified HBH Interest Shaping [14]

  • Shaper Ideal: The shaper at R1 magically knows the

link capacity in the underlay network (10 Mbps).

  • Shaper Overlay: The shaper uses its local link

bandwidth (20 Mbps) as shaping parameter.

  • PCON: As described earlier.

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Evaluation: IP Overlay & Wireless Links

Shaper_Ideal Shaper_Overlay PCON 5 10 15 20 10 20 30 10 20 30 10 20 30

TP [Mbps]

500 1000 10 20 30 10 20 30 10 20 30

Delay [ms]

100 200 10 20 30 10 20 30 10 20 30

T/Os

50 100 150 200 250 10 20 30 10 20 30 10 20 30

Time [s] Q [KB]

P1 r3

PCON doesn’t drain queues, but still avoids timeouts!

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Conclusions & Future Work

PCON prevents congestion in diverse scenarios (WiFi & IP Overlay) without strong assumptions about the network. Novel forwarding adaptation based on congestion marks. Future Work:

  • 1. Definition of Fairness; handling unresponsive consumers
  • 2. Larger evaluation; parameter setting and dynamics of

congestion reaction

  • 3. Implementation in NFD
  • http://redmine.named-data.net/issues/3636
  • Consumer/Producer API [6]

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The End

Thank you for your attention!

Klaus Schneider

klaus@cs.arizona.edu

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References I

[1] Sebastian Braun, Massimo Monti, Manolis Sifalakis, and Christian Tschudin. An empirical study of receiver-based aimd flow-control for ccn.

In IEEE ICCCN, 2013.

[2] Giovanna Carofiglio, Massimo Gallo, and Luca Muscariello. Joint hop-by-hop and receiver-driven interest control protocol for content-centric networks.

In ACM ICN workshop, 2012.

[3] Giovanna Carofiglio, Massimo Gallo, Luca Muscariello, Michele Papalini, and Sen Wang. Optimal multipath congestion control and request forwarding in information-centric networks.

In ICNP, 2013.

[4] Kai Lei, Chaojun Hou, Lihua Li, and Kuai Xu. A rcp-based congestion control protocol in named data networking.

In CyberC, 2015.

[5] Chengcheng Li, Tao Huang, Renchao Xie, Hengyang Zhang, Jiang Liu, and Yunjie Liu. A novel multi-path traffic control mechanism in ndn.

In IEEE ICT, 2015.

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References II

[6] Ilya Moiseenko, Lijing Wang, and Lixia Zhang. Consumer/ producer communication with application level framing in named data networking.

In ACM ICN, 2015.

[7] Dinh Nguyen, Masaki Fukushima, Kohei Sugiyama, and Atsushi Tagami. Efficient multipath forwarding and congestion control without route-labeling in ccn.

In IEEE ICCW, 2015.

[8] K Nichols, V Jacobson, A McGregor, and J Iyengar. Controlled delay active queue management: draft-ietf-aqm-codel-03.

RFC draft, 2016.

[9] Kathleen Nichols and Van Jacobson. Controlling queue delay.

ACM Communications, 2012.

[10] Heungsoon Park, Hoseok Jang, and Taewook Kwon. Popularity-based congestion control in named data networking.

In IEEE ICUFN, 2014.

[11] Natalya Rozhnova and Serge Fdida. An extended hop-by-hop interest shaping mechanism for ccn.

In IEEE GLOBECOM, 2014.

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References III

[12] Lorenzo Saino, Cosmin Cocora, and George Pavlou. Cctcp: A scalable receiver-driven congestion control protocol for ccn.

In IEEE ICC, 2013.

[13] Satyanarayana Vusirikala, Spyridon Mastorakis, Alexander Afanasyev, and Lixia Zhang. A best effort link layer reliability scheme.

Technical report, NDN TR41, 2016.

[14] Yaogong Wang, Natalya Rozhnova, Ashok Narayanan, David Oran, and Injong Rhee. An improved hop-by-hop interest shaper for congestion control in named data networking.

ACM SIGCOMM CCR, 2013.

[15] Lisong Xu, Khaled Harfoush, and Injong Rhee. Binary increase congestion control (bic) for fast long-distance networks.

In IEEE INFOCOM, 2004.

[16] Feixiong Zhang, Yanyong Zhang, Alex Reznik, Hang Liu, Chen Qian, and Chenren Xu. A transport protocol for content-centric networking with explicit congestion control.

In IEEE ICCCN, 2014.

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References IV

[17] Jianer Zhou, Qinghua Wu, Zhenyu Li, Mohamed Ali Kaafar, and Gaogang Xie. A proactive transport mechanism with explicit congestion notification for ndn.

In IEEE ICC, 2015.

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