Packet Classification Using Coarse-Grained Tuple Spaces

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Packet Classification Using Coarse-Grained Tuple
Spaces
Haoyu Song, Jon Turner and Sarang
Dharmapurikar www.arl.wustl.edu
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Overview

• Two-dimensional packet classification problem
• in list of 2d filters, find first match for given
  address pair
• (1011,0111) lt101,10gt, lt10,011gt, lt1,01gt
• Limitations of current solutions
• fast algorithmic methods require excessive space
  (50x)
• TCAM has high cost per bit, significant power
  usage
• Combining cross-product and tuple-space search
• hybrid strategy with range of time-space tradeoff
  options
• Improving 1d lookups
• combining tree bitmap and Bloom filters
• Possible extensions

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Cross-Product Method

• Procedure
• do 1d lookup on all fields
• combine results into lookup key in cross-product
  table
• direct lookup table or hash table
• Fast, but space grows as nk for n filters, k
  fields

10100, 01110
S0D1
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2D Tuple Space Search

• Group by prefix length
• hash table per group
• up to 33 x 33 1,089 groups
• in practice 30-100 occupied tuples
• Rectangle search
• markers to guide search
• at most 33 probes, often less
• hard to update
• Pruned tuple space search
• 1d search on src/dest fields
• find prefix lengths that match src/dest fields of
  packet
• search intersecting tuples
• if k matching prefixes, at most k2 probes

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Coarse-Grained Tuple Space

• Select coarse-grained partition of tuple space
• Build cross-product table per sub-space
• Search procedure
• 1d lookups for LPM
• probe each subspace
• terminate early if possible
• Pruning
• identify candidate sub-spaces during 1d lookup
• probe selected sub-spaces
• Space/time tradeoff

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Performance of Basic Algorithm

• Equal size divisions of 2d tuple space
• Ratio of cross-products to filter set size
• 2x2 partition brings space usage to 2x minimum
• maximum of four probes required
• compared to 30-90 for simple tuple space search
• Pruning of limited use for filter sets of size
  lt104

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Performance of Best Configurations
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Alternate Partitioning Approaches

• Arbitrary sub-spaces are possible
• potential for fewer regions with good space
  efficiency
• Preliminary results mixed
• may be useful for smaller filter sets
• More evaluation needed

Note filters of form ltprefix,gt and lt,prefixgt
stored in 1d data structures
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Fast 1d Lookups
Tree Bitmap
Hashing Bloom Filters

• Multibit trie
• Co-located children
• Bitmaps for
• prefix nodes
• subtree presence
• 4 bit stride implies 8 memory accesses

• Expand prefixes to standard lengths
• Off-chip hash table per length
• On-chip Bloom filters to avoid unproductive
  probes
• Large space requirements for good worst-case
  performance

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Fast and Compact 1d Lookups

• Insert tree bitmap subtree roots into off-chip
  hash tables and on-chip Bloom filters
• Lookup prefix of subtree roots in Bloom filters
• if match on length k and all shorter lengths,
  probe off-chip table for length k
• Reduction in on-chip memory for Bloom filters
• shape-shifting trie yields further space reduction

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1d Lookup Performance
200K IPv4 prefixes 5 bit stride for tree bitmap 8
bit on-chip root table
4 Bloom filters 1 BF entry for every 2 prefixes 1
off-chip probes (4 incl. FP)
2 Bloom filters 1 BF entry for every 6 prefixes 2
off-chip probes (4 incl. FP)
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Practical Configuration

• Configure 1d lookups for 1 off-chip probe each
  (excluding false positives)
• about 5 bits per prefix for Bloom filters with
  low FP rate
• Record ltprefix,gt and lt,prefixgt filters in 1d
  lookup data structures
• also proposed in recent paper by Kounavis, et.
  al.
• Divide remaining filters among four subspaces
• approximately 2 off-chip hash table entries per
  filter
• at most four probes
• With single QDR SRAM at 200 MHz, 32 bit word size
  can do 200 million probes per second
• about 33 million packets/second
• 40 byte packets at 10 Gb/s

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Possible Extensions

• More extensive evaluation
• scaling to larger filter sets 100-200K filters
• integrated evaluation of 1d and 2d lookups
• systematic evaluation of alternate partitioning
  strategies
• Alternate representations of filter sub-spaces
• any filter set data structure is candidate
• using decision trees, can skip 1d lookups
• Generalization to more dimensions
• handling fields with ranges (for port numbers)
• coarse-grained grouping of tuple-spaces defined
  on nesting level
• can we beat TCAM?