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A Memory-Efficient Hashing by Multi-Predicate Bloom Filters for Packet Classification. Author: Heeyeol Yu; Mahapatra, R.; Publisher: IEEE INFOCOM 2008 Presenter: Yu-Ping Chiang Date: 2008/12/17. Outline. Related Works – Basic Bloom filter

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a memory efficient hashing by multi predicate bloom filters for packet classification

A Memory-Efficient Hashing by Multi-Predicate Bloom Filters for Packet Classification

Author: Heeyeol Yu; Mahapatra, R.;

Publisher: IEEE INFOCOM 2008

Presenter: Yu-Ping Chiang

Date: 2008/12/17

outline
Outline
  • Related Works – Basic Bloom filter
  • Multi-predicate Bloom-filter Hash Table (MBHT)
    • Benefits
    • Architecture
    • Insert
    • Query
    • Delete
  • Analysis and Simulation
    • On/Off-chip memory usage
    • Average access of search
    • URL switching
related works basic bloom filter
0

1

2

3

m-1

……

……

……

1

1

1

Related Works – Basic Bloom filter
  • set S =
    • n elements.
    • represented in m bits array, initially set to 0.
    • using k independent hash functions mapping.

……

…………………

related works basic bloom filter1
Related Works – Basic Bloom filter
  • The probability that a bit is 0
  • Probability of false-positive
  • In requirement of

by [17] A. Broder and M. Mitzenmacher, “Network Applications of Bloom Filters: A Survey,” pp. 485–509, 2002. [Online]. Available:citeseer.ist.psu.edu/broder02network.html

related works basic bloom filter2
Related Works – Basic Bloom filter
  • Linear property
    • Given f, n is linearly proportionate to m.
  • Reverse Exponential Property
    • Given n, m is exponential effect on f.
outline1
Outline
  • Related Works – Basic Bloom filter
  • Multi-predicate Bloom-filter Hash Table (MBHT)
    • Benefits
    • Architecture
    • Insert
    • Query
    • Delete
  • Analysis and Simulation
    • On/Off-chip memory usage
    • Average access of search
    • URL switching
mbht benefits
MBHT - Benefits
  • On-chip
    • Reduce memory size in base- number system by x times compares to that of base- number system.
    • Insert and delete operations are done in constant time in parallel.
  • Off-chip
    • Saves memory by removing linked list mechanism.
    • Does not save the duplicate items.
mbht insert
MBHT - Insert
  • Partition address space.
    • n elements
    • Base-b number system,

→ digits

    • Address with r digits of x bits :

is covered by

mbht insert2
MBHT - Insert
  • Transform to base-4 number system
    • Fewer columns in each address space.
    • Not affect addressing off-chip memory.
mbht insert3
MBHT - Insert
  • Memory usage :
    • .
mbht insert4
MBHT - Insert
  • Memory change rate with f and n.

→larger base- is advantageous because x times on-chip memory saving.

(hard in real hardware.)

mbht insert5
MBHT - Insert
  • Algorithm :

→ Θ(1)

Execute each column

Set bloom filter

→Θ(1)

mbht query
MBHT - Query
  • Algorithm
    • Consider only on-chip operation.
    • Need to be called twice on l-MBHT and r-MBHT
    • Θ(1)
mbht delete
MBHT - Delete
  • Algorithm
    • Need to be called twice on l-MBHT and r-MBHT
    • Θ(1)
outline2
Outline
  • Related Works – Basic Bloom filter
  • Multi-predicate Bloom-filter Hash Table (MBHT)
    • Benefits
    • Architecture
    • Insert
    • Query
    • Delete
  • Analysis and Simulation
    • On/Off-chip memory usage
    • Average access of search
    • URL switching
on off chip memory usage
On/Off-chip memory usage
  • Memory efficiency ratio :

R = # of layers

B = # of bits in one layer

(in FHT memory consumption, 4 is bits for counter.)

average access of search
Average access of search

The lower successful search rate,

the better access time performance

url switching
URL switching

on-chip memory reduction

1.7 times to LHT

2 times to FHT

AAS* = average access for a successful search

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