Presentation is loading. Please wait.

Presentation is loading. Please wait.

M. Waldvogel, G. Varghese, J. Turner, B. Plattner Presenter: Shulin You UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering.

Published byAbril Brich Modified over 10 years ago

Similar presentations

Presentation on theme: "M. Waldvogel, G. Varghese, J. Turner, B. Plattner Presenter: Shulin You UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering."— Presentation transcript:

1 M. Waldvogel, G. Varghese, J. Turner, B. Plattner Presenter: Shulin You UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 1 Scalable High Speed IP Routing Lookups

2 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 2 Outline  Routing and the BMP (Best Matching Prefix) problem  Basic scheme: binary search of hash tables  Refinements: asymmetric trees, mutated binary search and ropes  Implementation: building the tree  Performance and comparison study

3 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 3 Introduction Increases in Internet traffic demands: link speed, router data throughput, packet forward rate Packet forwarding rate becomes bottleneck Address lookup: main step in packet forwarding Need efficient Best Prefix Match algorithm

4 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 4 Lookup in an IP Router Dstn Addr ---- Dstn-prefixNext Hop Forwarding Table Next Hop Computation Forwarding Engine Incoming Packet HEADERHEADER Next Hop

5 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 5 More about BMP Classless Inter-Domain Routing (CIDR) - cope with routing table growth Aggregation results in address prefix list in routing tables Packets forwarded according to Best Matching Prefix Goal: use hashing to find BMP

6 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 6 Radix Trie P2 P3 P4 P1 A B C G D F H E 1 0 0 1 1 1 1 Lookup 10111 Add P5=1110* I 0 P5 next-hop-ptr (if prefix) left-ptr right-ptr Trie node P1111*H1 P210*H2 P31010*H3 P410101H4

7 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 7 Radix Trie W-bit prefixes: O(W) lookup, O(NW) storage and O(W) update complexity Advantages  Simplicity  Extensible to wider fields Disadvantages  Worst case lookup slow  Wastage of storage space in chains

8 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 8 Outline  Routing and the BMP (Best Matching Prefix) problem  Basic scheme: binary search of hash tables  Refinements: asymmetric trees, mutated binary search and ropes  Implementation: building the tree  Performance and comparison study

9 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 9 Basic Scheme Hash table organized by prefix length Markers needed

10 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 10 Marker Management Reducing marker storage: markers only needed in levels visited by binary search when looking for P E. g. : In table of 8 levels: a prefix with length of 7 [111] needs markers in level 4 and 6 a prefix with length of 3 [11] needs markers in level 2

11 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 11 False Markers What if table entries are: P1=1, P2=00, P3=110 in the previous example? Solution: to avoid backtracking, record current best matching prefix of marker M in M.bmp

12 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 12 Basic Algorithm Initialize range R = array L Initialize BMP to be null string While R not empty I = middle level in range R D’ = D(L[i].length) M = Search(D’, L[i].hash) If M is nil Then set R = upper R Elseif M is a prefix & not a marker Then BMP = M.bmp; break Else { BMP = M.bmp; R = lower R} Endif Endwhile

13 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 13 Outline  Routing and the BMP (Best Matching Prefix) problem  Basic scheme: binary search of hash tables  Refinements: asymmetric trees, mutated binary search and ropes  Implementation: building the tree  Performance and comparison study

14 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 14 Algorithm Refinements To optimize, need to exploit deeper structure in routing table Simplest improvement: Search only non-empty tries

15 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 15 Asymmetric Binary Search Try to search the most promising trie first Example: Maximize table entries while keeping tree balance

16 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 16 Mutating Binary Search The number of distinct prefix lengths rarely exceeds 12 Every match with some marker X means that we need only search among the set of prefixes for which X is a prefix

17 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 17 Mutating Binary Search (example)

18 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 18 Potential Disadvantages Pre-computing optimal trees increases insertion time Storage of binary trees for each prefix enormous Storage problem solved with “rope” data structure Rope maximum length: log 2 W pointers

19 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 19 Rope: Encoded Trees Only need to store the sequence of levels which binary search on a given subtrie will follow on repeated failures to find a match e.g.: Rope 1 contains 16, 8, 4, 2, 1 Rope algorithm: a prefix not a marker: end of rope a prefix and a marker: X.bmp = X

20 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 20 Rope Search (Example)

21 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 21 Rope Search Initialize Rope R = default search sequence Initialize BMP = null string While R not empty i = first pointer of R D’ = D(L[i].length) M = Search(D’, L[i].hash) If M is success Then BMP = M.bmp; R = M.rope Endif Endwhile

22 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 22 Building Rope Search Pass 1: builds a conventional trie Pass 2: all prefixes inserted into hash tables, starting with shortest prefix Batch insertions and deletions into the table as these operations are expensive.

23 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 23 Outline  Routing and the BMP (Best Matching Prefix) problem  Basic scheme: binary search of hash tables  Refinements: asymmetric trees, mutated binary search and ropes  Implementation: building the tree  Performance and comparison study

24 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 24 Existing Approaches Modification of exact matching:log 2 2N Trie based: BSD radix trie O(W 2 ) Hardware: parallelism, CAM expensive Protocol based: IP/tag switching, still need some IP lookups Caching: CAM (Content Addressable Memory)

25 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 25 Complexity Comparison

26 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 26 Performance Comparison

27 UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering 27 Conclusion Intellectual contribution: markers and pre-computation ensure log time in worst-case Solution scalable and can be implemented in software efficiently Future work: choice of balancing function and faster insertion algorithms

Download ppt "M. Waldvogel, G. Varghese, J. Turner, B. Plattner Presenter: Shulin You UNIVERSITY OF MASSACHUSETTS, AMHERST – Department of Electrical and Computer Engineering."

Similar presentations

IP Router Architectures. Outline Basic IP Router Functionalities IP Router Architectures.

IP Router Architectures. Outline Basic IP Router Functionalities IP Router Architectures.
A Search Memory Substrate for High Throughput and Low Power Packet Processing Sangyeun Cho, Michel Hanna and Rami Melhem Dept. of Computer Science University.

A Search Memory Substrate for High Throughput and Low Power Packet Processing Sangyeun Cho, Michel Hanna and Rami Melhem Dept. of Computer Science University.
August 17, 2000 Hot Interconnects 8 Devavrat Shah and Pankaj Gupta

August 17, 2000 Hot Interconnects 8 Devavrat Shah and Pankaj Gupta
Router/Classifier/Firewall Tables Set of rules—(F,A)  F is a filter Source and destination addresses. Port number and protocol. Time of day.  A is an.

Router/Classifier/Firewall Tables Set of rules—(F,A)  F is a filter Source and destination addresses. Port number and protocol. Time of day.  A is an.
Internet Routers

Internet Routers
Fast Updating Algorithms for TCAMs Devavrat Shah Pankaj Gupta IEEE MICRO, Jan.-Feb. 2001.

Fast Updating Algorithms for TCAMs Devavrat Shah Pankaj Gupta IEEE MICRO, Jan.-Feb
Spring 2006CS 685 Network Algorithmics1 Longest Prefix Matching Trie-based Techniques CS 685 Network Algorithmics Spring 2006.

Spring 2006CS 685 Network Algorithmics1 Longest Prefix Matching Trie-based Techniques CS 685 Network Algorithmics Spring 2006.
1 IP-Lookup and Packet Classification Advanced Algorithms & Data Structures Lecture Theme 08 – Part I Prof. Dr. Th. Ottmann Summer Semester 2006.

1 IP-Lookup and Packet Classification Advanced Algorithms & Data Structures Lecture Theme 08 – Part I Prof. Dr. Th. Ottmann Summer Semester 2006.
A Scalable and Reconfigurable Search Memory Substrate for High Throughput Packet Processing Sangyeun Cho and Rami Melhem Dept. of Computer Science University.

A Scalable and Reconfigurable Search Memory Substrate for High Throughput Packet Processing Sangyeun Cho and Rami Melhem Dept. of Computer Science University.
Spring 2006CS 685 Network Algorithmics1 Principles in Practice CS 685 Network Algorithmics Spring 2006.

Spring 2006CS 685 Network Algorithmics1 Principles in Practice CS 685 Network Algorithmics Spring 2006.
Network Algorithms, Lecture 4: Longest Matching Prefix Lookups George Varghese.

Network Algorithms, Lecture 4: Longest Matching Prefix Lookups George Varghese.
1 An Efficient, Hardware-based Multi-Hash Scheme for High Speed IP Lookup Hot Interconnects 2008 Socrates Demetriades, Michel Hanna, Sangyeun Cho and Rami.

1 An Efficient, Hardware-based Multi-Hash Scheme for High Speed IP Lookup Hot Interconnects 2008 Socrates Demetriades, Michel Hanna, Sangyeun Cho and Rami.
Fast Firewall Implementation for Software and Hardware-based Routers Lili Qiu, Microsoft Research George Varghese, UCSD Subhash Suri, UCSB 9 th International.

Fast Firewall Implementation for Software and Hardware-based Routers Lili Qiu, Microsoft Research George Varghese, UCSD Subhash Suri, UCSB 9 th International.
Bio Michel Hanna M.S. in E.E., Cairo University, Egypt B.S. in E.E., Cairo University at Fayoum, Egypt Currently is a Ph.D. Student in Computer Engineering.

Bio Michel Hanna M.S. in E.E., Cairo University, Egypt B.S. in E.E., Cairo University at Fayoum, Egypt Currently is a Ph.D. Student in Computer Engineering.
1 Fast Routing Table Lookup Based on Deterministic Multi- hashing Zhuo Huang, David Lin, Jih-Kwon Peir, Shigang Chen, S. M. Iftekharul Alam Department.

1 Fast Routing Table Lookup Based on Deterministic Multi- hashing Zhuo Huang, David Lin, Jih-Kwon Peir, Shigang Chen, S. M. Iftekharul Alam Department.
IP Routing Lookups Scalable High Speed IP Routing Lookups.

IP Routing Lookups Scalable High Speed IP Routing Lookups.
Survey of Packet Classification Algorithms. Outline Background and problem definition Classification schemes – One dimensional classification – Two dimensional.

Survey of Packet Classification Algorithms. Outline Background and problem definition Classification schemes – One dimensional classification – Two dimensional.
Digital Search Trees & Binary Tries Analog of radix sort to searching. Keys are binary bit strings.  Fixed length – 0110, 0010, 1010, 1011.  Variable.

Digital Search Trees & Binary Tries Analog of radix sort to searching. Keys are binary bit strings.  Fixed length – 0110, 0010, 1010,  Variable.
An Efficient IP Address Lookup Algorithm Using a Priority Trie Authors: Hyesook Lim and Ju Hyoung Mun Presenter: Yi-Sheng, Lin ( 林意勝 ) Date: Mar. 11, 2008.

An Efficient IP Address Lookup Algorithm Using a Priority Trie Authors: Hyesook Lim and Ju Hyoung Mun Presenter: Yi-Sheng, Lin ( 林意勝 ) Date: Mar. 11, 2008.
1 Author: Ioannis Sourdis, Sri Harsha Katamaneni Publisher: IEEE ASAP,2011 Presenter: Jia-Wei Yo Date: 2011/11/16 Longest prefix Match and Updates in Range.

1 Author: Ioannis Sourdis, Sri Harsha Katamaneni Publisher: IEEE ASAP,2011 Presenter: Jia-Wei Yo Date: 2011/11/16 Longest prefix Match and Updates in Range.

Similar presentations

Ads by Google