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SEOUL NATIONAL UNIVERSITY. Ubiquitous Network Laboratory. Problems and challenges – Queue loss ratio. Unbalanced queue loss among nodes with the ...
QU-RPL: Queue Utilization based RPL for Load Balancing in Large Scale Industrial Applications June 24. 2015 Hyung-Sin Kim, Jeongyeup Paek, and Saewoong Bahk School of Electrical and Computer Engineering, Seoul National University School of Computer Information Communication Engineering, Hongik University

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Introduction Low power and lossy network (LLN): Low power/cost/rate Challenges in LLNs: Link dynamics, overhead, and complexity Solution: IETF RPL (Routing Protocol for LLN) [RFC 6550] New challenge: Large scale industrial application (Smart Grid)  Ex) Cisco’s CG-Mesh network (5,000 nodes/network) High rate traffic near the root (10mins/packet 0.12sec/packet)

Big Question RPL and high rate traffic… Are they happy together? 2

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

RPL operation (default RPL on TinyOS) Key metrics  𝑅𝑅𝐴𝐴𝐴𝐴𝐴𝐴 𝑘𝑘 = 𝐻𝐻𝐻𝐻𝐻𝐻 𝑘𝑘 + 1, propagated via DIO message broadcast  𝐸𝐸𝐸𝐸𝐸𝐸 𝑘𝑘, 𝑝𝑝𝑘𝑘 =

# 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡.(𝑘𝑘→𝑝𝑝𝑘𝑘 ) # 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑡𝑡𝑡𝑡.(𝑘𝑘→𝑝𝑝𝑘𝑘 )

, measured by child node 𝑘𝑘

Parent selection mechanism    

Parent candidate condition: 𝑅𝑅𝐴𝐴𝐴𝐴𝐴𝐴 𝑝𝑝𝑘𝑘 < 𝑅𝑅𝐴𝐴𝐴𝐴𝐴𝐴 𝑘𝑘 and 𝐸𝐸𝐸𝐸𝐸𝐸 𝑘𝑘, 𝑝𝑝𝑘𝑘 < 𝛿𝛿 Routing metric: 𝑅𝑅 𝑝𝑝𝑘𝑘 = 𝑅𝑅𝐴𝐴𝐴𝐴𝐴𝐴 𝑝𝑝𝑘𝑘 + 𝐸𝐸𝐸𝐸𝐸𝐸 𝑘𝑘, 𝑝𝑝𝑘𝑘 Best parent candidate: smallest 𝑅𝑅 𝑝𝑝𝑘𝑘 Parent change condition: significantly smaller 𝑅𝑅 𝑝𝑝𝑘𝑘 than current parent

DIO broadcast period – Trickle Timer  Low overhead: Double the period after every DIO transmission  Fast route recovery: Reset the period to the minimum value when inconsistency is detected 3

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges – Environment 30-node indoor testbed in office building  PHY: IEEE 802.15.4, MAC: default CSMA, Routing: RPL, UDP/IP: BLIP  Routing topology: 6 hop with RPL  Traffic load: 30~75 ppm/node (1800~4500 ppm/network) [Routing topology snapshot] 14

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7m

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LBR RPL node

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges

Question 1 Can an RPL-based network deliver high rate traffic? - Does packet loss increase gracefully? -

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges – Packet delivery Worse than anticipated based on bandwidth PRR degradation mainly due to queue loss Queue loss at only few nodes very severely [Arrival rate vs. PRR]

No!

[Arrival rate vs. Loss rate] 80

100

Left: Link loss 80

60

Loss ratio [%]

PRR [%]

[Answer]

60 40

Outliers

Right: Queue loss

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20 20 0

0 30

60 45 Arrival rate [ppm/node]

75

30

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60 45 Arrival rate [ppm/node]

75

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges

Question 2 PRR degradation comes from unbalanced queue loss. Is it inevitable?

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges – Queue loss ratio Unbalanced queue loss among nodes with the same hop Most congested node with hop distance 2 Queue loss comes from inefficient parent selection of RPL There is room for improvement!! [Hop distance vs. Queue loss]

Queue loss ratio [%]

50

2 hop!

40 30 20

30 ppm/node 60 ppm/node

[Answer] No!

Large gap!

10 0 1

2

3 4 Hop distance

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5

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges

Question 3 Does ETX reflect the congestion?

𝐸𝐸𝐸𝐸𝐸𝐸 𝑘𝑘, 𝑝𝑝𝑘𝑘

# 𝑜𝑜𝑜𝑜 𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑡𝑡𝑡𝑡. (𝑘𝑘 → 𝑝𝑝𝑘𝑘 ) = # 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑡𝑡𝑡𝑡. (𝑘𝑘 → 𝑝𝑝𝑘𝑘 ) 9

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Problems and challenges - ETX ETX does not reflect congestion Small queue size of a low cost device Queue capacity ≪ Link capacity

[Answer]

[Arrival rate vs. Link layer ETX]

No!

3

Link ETX

2.5 2 1.5 1 0.5 0

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45 60 Arrival rate [ppm/node]

75

We need to redesign RPL for load balancing! 10

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

QU-RPL design – Basic structure Queue utilization ratio (QU)  𝑄𝑄 𝑘𝑘 =

# 𝑜𝑜𝑜𝑜 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝 𝑖𝑖𝑖𝑖 𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞 𝑜𝑜𝑜𝑜 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑘𝑘 𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇𝑇 𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞𝑞 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑜𝑜𝑜𝑜 𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛 𝑘𝑘

Propagation of QU – New RANK  𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑄𝑄𝑄𝑄 𝑘𝑘 = 𝛽𝛽 ℎ 𝑘𝑘 + 1 + 𝛽𝛽 − 1 𝑸𝑸(𝒌𝒌)

 Each node can extract both ℎ 𝑘𝑘 and 𝑄𝑄 𝑘𝑘 from 𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑄𝑄𝑄𝑄 𝑘𝑘

Fast propagation of QU – Trickle Timer

 Trickle Timer re-initialization when a node experiences consecutive losses

Routing metric  𝑅𝑅𝑄𝑄𝑄𝑄 𝑝𝑝𝑘𝑘 = ℎ 𝑝𝑝𝑘𝑘 + 1 + 𝐸𝐸𝐸𝐸𝐸𝐸 𝑘𝑘, 𝑝𝑝𝑘𝑘 + 𝛼𝛼𝑸𝑸(𝒑𝒑𝒌𝒌 ) Original RANK

Original 𝑅𝑅(𝑝𝑝𝑘𝑘 )

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

QU-RPL design – Additional challenge

Problem: Herding effect!

Congestion!

Vicious Cycle

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Congestion!

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

QU-RPL design – Probabilistic parent change Congestion indicator µ𝑘𝑘

 1) When µ𝑘𝑘 > γ (congested environments)

 Parent changes with a probability of 𝑚𝑚𝑚𝑚𝑚𝑚{𝜿𝜿(𝑄𝑄 𝑃𝑃𝑘𝑘 − 𝑄𝑄(𝑃𝑃�𝑘𝑘 )), 0}

 2) Otherwise (non-congested environments),  Fall back to default RPL

Congestion!

Load balance

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Performance evaluation – Routing topology 2

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[RPL]

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[QU-RPL]

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Balanced! 8 vs. 11 nodes

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Congestion! 2 vs. 17 nodes 14

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Performance evaluation – Packet delivery Balanced and reduced queue loss ratio Significant PRR improvement [Arrival rate vs. Queue loss ratio]

[Arrival rate vs. PRR] 100

RPL (worst node) QU-RPL (worst node) RPL (average) QU-RPL (average)

60

40

78% reduction!

20

0

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PRR [%]

Queue loss ratio [%]

80

60 40 20

30

60 45 Arrival rate [ppm/node]

0

75

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145% improvement!

Left: RPL Right: QU-RPL 30

60 45 Arrival rate [ppm/node]

75

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Performance evaluation – Route quality Similar hop distance Similar link ETX [Arrival rate vs. Hop distance]

[Arrival rate vs. Link layer ETX] 3

RPL (farthest node) QU-RPL (farthest node) RPL (average) QU-RPL (average)

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Left: RPL 2.5

Link ETX

Hop distance from LBR

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Right: QU-RPL

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60 45 Arrival rate [ppm/node]

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60 45 Arrival rate [ppm/node]

75

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Performance evaluation – Routing overhead More overhead by reinitializing Trickle Timer more frequently  But, we get much better PRR despite more DIO transmission

Less parent changes due to more strict condition when network is balanced [Arrival rate vs. # of parent changes] Average parent changes / node / hour

[Arrival rate vs. DIO overhead] Average DIOs / node / hour

60 RPL QU-RPL

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46.4%

40 20.8% 30

27.9%

1.2%

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45 60 Arrival rate [ppm/node]

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10 RPL QU-RPL

8 6

-32.7% 4 2 0

-20%

30

+12%

-43.3%

45 60 Arrival rate [ppm/node]

75

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Performance evaluation – Effect of parameters 𝜶𝜶 ∶ Effect of QU on routing metric

 𝑅𝑅 𝑝𝑝𝑘𝑘 = 𝐻𝐻𝐻𝐻𝐻𝐻 𝑝𝑝𝑘𝑘 + 1 + 𝐸𝐸𝐸𝐸𝐸𝐸 𝑘𝑘, 𝑝𝑝𝑘𝑘 + 𝜶𝜶𝑄𝑄(𝑝𝑝𝑘𝑘 )

𝜿𝜿 ∶ Aggressiveness of parent changes (avoid herding effect)  𝑃𝑃𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑘𝑘 = max{𝜿𝜿(𝑄𝑄 𝑃𝑃𝑘𝑘 − 𝑄𝑄 𝑃𝑃�𝑘𝑘 ), 0}

[𝜿𝜿 vs. PRR] (𝜶𝜶 =2)

100

100

80

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PRR [%]

PRR [%]

[𝜶𝜶 vs. PRR] (𝜿𝜿 =0.5)

60 40

40 20

20 0

60

-1

0

1

α

2

0

3

18

0

0.125 0.25

0.5

κ

0.75

1

0

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Conclusions Large scale industrial applications require delivery of heavy traffic over RPL-based LLN We found out that default RPL does not consider load balancing We design QU-RPL, lightweight mechanism based on queue utilization ratio, for load balancing to support heavy traffic Performance evaluation on an indoor testbed shows significant improvement

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Thanks! Email: [email protected] Homepage: netlab.snu.ac.kr/~hskim

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SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory

Appendix – Congestion indicator µ𝑘𝑘 First candidate: My QU ( 𝑄𝑄 𝑘𝑘 )

 Can be small even when my parent suffers from frequent queue losses

Second candidate: My parent’s QU ( 𝑄𝑄 𝑃𝑃𝑘𝑘 )  Can be small after traffic load is balanced

Third candidate: Maximum QU among my parent candidates ( 𝑄𝑄𝑘𝑘,𝑚𝑚𝑚𝑚𝑚𝑚 = max 𝑄𝑄𝑘𝑘,𝑚𝑚𝑚𝑚𝑚𝑚 , 𝑚𝑚𝑚𝑚𝑚𝑚𝑝𝑝𝑘𝑘 ∈𝐏𝐏𝑘𝑘 𝑄𝑄 𝑝𝑝𝑘𝑘 )  Remain high after traffic load is balanced (memory)

𝝁𝝁𝒌𝒌 = 𝑸𝑸𝒌𝒌,𝒎𝒎𝒎𝒎𝒎𝒎 21

SEOUL NATIONAL UNIVERSITY Ubiquitous Network Laboratory