Updating Designed for Fast IP Lookup Author : Natasa Maksic, Zoran Chicha and Aleksandra Smiljani´c...

Updating Designed for Fast IP Lookup

Author : Natasa Maksic, Zoran Chicha and Aleksandra Smiljani´cConference: IEEE High Performance Switching and Routing (HPSR), 2012Presenter: Kuan-Chieh FengDate: 2015/9/23

Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.

Outline

Introduction BPFL (Balanced parallelized frugal lookup) POLP (Parallel optimized linear pipeline) Memory requirement Execution time Conclusion

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Introduction

In this paper, we propose implement and analyze lookup table updating for POLP lookup algorithm and BPFL.• Compare POLP and BPFL update algorithm in

terms of real-world routing tables.• Measure the memory requirement for both

lookup algorithm.• Observe the number of memory access when

lookup table are updated.

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Outline

Introduction BPFL (Balanced parallelized frugal lookup) POLP (Parallel optimized linear pipeline) Memory requirement Execution time Conclusion

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BPFL Structure(1/12)

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BPFL Structure(2/12)

Number of levels : : address length : subtree depth

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BPFL Structure(3/12)

IP Lookup example:• = 32 , = 8 , L = 4• IP = 10000000100000001111000011110000

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Level 1

Level 2

Level 3

Level 4Highest

level match

BPFL Structure(4/12)

Each module at level i contains two parts : • Subtree search engine• Prefix search engine

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BPFL Structure (5/12)

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Subtree search engine Prefix search engine

BPFL Structure (6/12)

Subtree search engine• Balanced Tree Selector• Balanced Trees

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BPFL Structure (7/12)

Subtree search engine• Balanced Tree Selector gives the address of the

root of the subtree.

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Address ofthe root of selected

balanced tree

BPFL Structure (8/12)

Subtree search engine• The selected balanced tree is traversed based on

the comparisons of its node entries to the given IP address.

• In order to frugally use the on-chip memory, balanced tree nodes do not store pointers to their children.

• The left child address is obtained by adding ‘0’ before the parent’s address, and the right child address is obtained by adding ‘1’.

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BPFL Structure (9/12)

Subtree search engine

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BPFL Structure (10/12)

Prefix search engine• Bitmap processor• Internal memory block (subtree bitmap memory)

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BPFL Structure (11/12)

Prefix search engine• If the number of non-empty nodes is below the

threshold, their indices are kept in the internal memory.

• Otherwise, the complete bitmap vector describing the subtree structure is stored in the internal memory.

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BPFL Structure (12/12)

Prefix search engine

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BPFL advantage

BPFL frugally uses the memory resources so the large lookup tables can fit the on-chip memory.

Since the pipelining is used, one IP lookup can be performed per a clock cycle.

Using memory frugally makes BPFL support IPv6.

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Memory saving

Fast lookup

Support IPv6

BPFL Update

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Outline

Introduction BPFL (Balanced parallelized frugal lookup) POLP (Parallel optimized linear pipeline) Memory requirement Execution time Conclusion

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POLP Structure (1/7)

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POLP Structure (2/7)

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POLP Search engine contains:• Pipeline Selector • Pipeline 1 ~ P (Stages)• Final Selector

POLP Structure (3/7)

Pipeline Selector Select with first I bits of input IP address Pass remaining 32-I bits to pipeline

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POLP Structure (4/7)

Pipeline• Each pipeline consists of F stages• Children nodes of the given node do not have to

be in the next stage

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POLP Structure (5/7)

Pipeline

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POLP Structure (6/7)

Stage• Each stage contains a memory block that holds

the nodes of the subtrees

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POLP Structure (7/7)

Stage memory contains:• Next-hop bit• Left child bit• Right child bit• Children pointer

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POLP advantage

The lookup process is parallelized and pipelined.

Multiple IP addresses can be searched in parallel through distinct pipelines.

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Fast lookup

High throughput

POLP Update

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Outline

Introduction BPFL (Balanced parallelized frugal lookup) POLP (Parallel optimized linear pipeline) Memory requirement Execution time Conclusion

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Memory requirement

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Outline

Introduction BPFL (Balanced parallelized frugal lookup) POLP (Parallel optimized linear pipeline) Memory requirement Execution time Conclusion

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Execution time

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Memory access

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Memory access

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Conclusion

The POLP update algorithm is faster for large routing tables.

The BPFL update algorithm performs better for the smaller routing tables with the number of memory access.

The BPFL algorithm had the smaller memory requirements, and that its memory savings increase with the routing table size.

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Supplement

Three realistic lookup table:• 71K• 143K• 309K

FPGA chip:• Altera’s Stratix II RP2S180F1020C5 chip

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Supplement (BPFL)

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Supplement (POLP)

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