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Forwarding in a Content-Based Network

Forwarding in a Content-Based Network. SIGCOMM 2003 Antonio Carzaniga, Alexander L.Wolf. 14 th January, 2004 Presented by Sookhyun, Yang. Contents. Introduction Content-Based Networking Content-Based Routing Scheme Problem Statement Contribution Forwarding Algorithm Evaluation

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Forwarding in a Content-Based Network

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  1. Forwarding in a Content-Based Network SIGCOMM 2003 Antonio Carzaniga, Alexander L.Wolf 14th January, 2004 Presented by Sookhyun, Yang

  2. Contents • Introduction • Content-Based Networking • Content-Based Routing Scheme • Problem Statement • Contribution • Forwarding Algorithm • Evaluation • Conclusion LAB Seminar 2004

  3. [Content-based addressing scheme] • M - the universe of messages • P: M {true, false} - the universe of predicates over M • predicate pn advertised by n - content-based address of node n • pn(m) = true – message is implicitly addressed by its content to node n Introduction • Content-based network • Flow of messages is driven by the content of the messages, not by the IP address • Sender simply publish messages • Receiver declare and advertise their interests by means of selection predicates • Service delivers to receivers each messages that matches the selection predicates declared by those receivers • Application-level overlay network consisting of client nodes and router nodes • Routers perform specialized routing and forwarding functions LAB Seminar 2004

  4. source Background • Reverse path forwarding (RPF) algorithm • Transmit the packet on all of its outgoing links only if the packet arrived on the link that is on its own shortest path back to the sender Not forwarded forwarded LAB Seminar 2004

  5. Content-Based Networking • Practical refinement of definitions • Concrete syntax and semantics embodied in the disjunctive normal form of Siena event notification service [7] • Message is a set of typed attributes • Attribute is uniquely identified within the message by a name, and has a type and a value • Predicate is a disjunction of conjunctions of constraints on the values of individual attributes [string carrier= UA; string dest=ORD; int price=300; bool upgradeable=true;] [string dest=ORD Λint price < 400] LAB Seminar 2004

  6. Content-Based Routing scheme (1/4) • Content-based routing [8] • Start from a basis of a broadcast system • Prune branches of the broadcast distribution system using advertised predicates • Limit the propagation of each message to only those node that advertised predicates matching the message LAB Seminar 2004

  7. Push Receiver Sender Pull Content-Based Routing scheme (2/4) • Two types of routing protocols • Broadcast routing protocol • Topological information • Maintain the forwarding state that would be necessary to implement broadcast system • Content-based routing protocol • Predicate advertised by nodes • Maintain the forwarding state that decide (for each router interface) whether a message matches the predicates advertised by any downstream node • Mechanisms for the propagation of routing information • Push based on receiver advertisements (RA) • Pull based on sender requests (SR) and update replies (UR) LAB Seminar 2004

  8. Content-Based Routing scheme (3/4) • Receiver advertisements (RAs) • Issued by nodes periodically and/or when they advertise new content-based addresses • Carry the content-based address (predicate) and identifier of its issuer • Pushrouting information from the issuer (receiver) out to all the potential senders • Propagation of RA • Follow the broadcast tree rooted at the issuer node • Sets up RPFs towards the issuer • Predicateadvertised by an RA is combined in a disjunction to the predicate associated with the interface on the reverse path to the issuer • If this combination generates a new predicate for that interface, then the node continues the propagation of RA • Otherwise, the node simply stop propagating RA LAB Seminar 2004

  9. Content-Based Routing scheme (4/4) • Sender request (SR) • Router uses a SR to collect routing information from other routers • Pull content-based routing information from receivers back to senders • Flow of SR/UR • Follow the broadcast tree rooted at the issuer (sender) • Routers respond to SRs by generating update replies (URs) containing their content-based address • URs are returned back to the issuer of the SR, on the reverse path of the SR, combining content-based addresses with URs along the way • Issuer (sender) of the SR receives one UR per interface, each one carrying the combined content-based address of the nodes reachable through that interface LAB Seminar 2004

  10. Problem Statement • Propose a forwarding process consisting of the combination of broadcast forwarding and content-based forwarding • Focus on the design of the content-based forwarding algorithm • Assumption 1: broadcast forwarding and routing function • Output for message m originating at a node s is a set of output interfaces B • Assumption 2: Content-based routing function • Maintain a content-based forwarding table • Table represent a map between interfaces and predicates • Contention-based forwarding function CBF Fast CBF(m, B, T) = { i : i ∈ B Λ matches(pi, m)} m - message B - a set of broadcast output interfaces T - content-based forwarding table {p1, p2, … ,pI} LAB Seminar 2004

  11. Contribution • Use indexing data structure developed by Yan and Carcia-Molina [20] • Enhance functionality of the matching algorithm • Make it appropriate for use in the forwarding function of a content-based network • Extensions of algorithm • Extend the set of types and operators in predicates • Prefix, suffix, and substring operators for strings • Add the explicit expression of disjunctions to predicates • Optimize using the construction of selectivity table LAB Seminar 2004

  12. Background • Ternary search trie (TST) m am as be by go me my no so b e n y o a y g s o m e o s LAB Seminar 2004

  13. filter match Forwarding Algorithm (1/6) • Recall of definition • Forwarding table is a one-to-one association of predicates to interfaces • Predicate is a disjunction of conjunctions of elementary constraints • Constraint is a quadruple <type, name, op, value> • Forwarding a message m amounts to computing the set of interfaces associated with a predicate matching m • Counting algorithm • Founded on a particular index structure representing the forwarding table • Matching problem message LAB Seminar 2004

  14. constraint Conjunctions of constraints disjunctions of filters attribute Boolean Forwarding Algorithm (2/6) • High level view of structure of forwarding table Need optimized lookup function!!! LAB Seminar 2004 Left-hand side Right-hand side

  15. Forwarding Algorithm (3/6) • Optimization 1 – “Extended counting algorithm” • Disjunction of filters • Eliminate a lookup in the table of counters for all the filters linked to interface that has already been matched • Termination • Set of matched interfaces = complete set of neighbor interfaces For a given message m, for each attribute a inm{ find constraints matched bya for each constraint c in a{ find the matched filter f } when (counter_filter == total # of constraints of f) add interface to set of matched interfaces } LAB Seminar 2004

  16. Attribute name and type Attribute name and type Forwarding Algorithm (4/6) • Optimization 2 – “Multi-Operator Index” • Speed up the process of finding the constraint • Combine TST (Ternary Search Trie) for attribute name and a simple switch on the type Multi-operator index prefix, suffix, substring op for strings <Integer constraint indexing> LAB Seminar 2004

  17. Forwarding Algorithm (5/6) • Optimization 2 – “Multi-Operator Index” (cont’d) • Extended TST (Ternary search trie) • Capability of matching partial strings (prefix and substring constraints) • Pair of “crown” lists linking the sequence of >, < constraints as leaves in TST • Pair of backtrack functions moving from partial to alphabetically closest complete match (use to jump to the >, < chains) • Lookup function • Start from the first character of the input string • When partial-match node is reached, return prefix constraint and/or the substring constraint • If final node touched is #, return the corresponding =, >, <, and suffix constraints • If final node touched is not a leaf, backtracks to the two closest leaf nodes, one preceding and the other following the final node in alphabetical order • Jump onto <, > chains from matching final node or closest matching nodes • Repeat for each character of the input string • Complexity of complete lookup function = O( l(logN + l) + |result|) LAB Seminar 2004

  18. pf ss < sf = > z LAB Seminar 2004

  19. I1 price I2 I3 stock Forwarding Algorithm (6/6) • Optimization 3 – “Exploiting attribute selectivity” • Eliminate interfaces from consideration as soon as possible • Determinant attribute • Every filter of interface I contains at least one constraint on determinant attribute • If a message does not contain a determinant attribute of interface I, interface I can be ignored during the processing of the message • Selectivity table • Map that associates determinant attribute with the interfaces • Compute the intersection of attribute names of all filters for each interface • Sort in descending order by the cardinality of the set of excluded interfaces • Pre-processing function • Use selectivity table to calculate excluded set of interfaces that will not match the messages • Parameterize pre-processing function by { # of pre-processing rounds} • Round – how far down the selectivity table pre-processing function will traverse LAB Seminar 2004

  20. Evaluation (1/6) • Experiments are intended to provide an initial exploration of the parameter space • Experimental setup • Algorithm in C++ • 950MHz computer with 512Mb of main memory • Auxiliary programs for generating loads of filters and messages • 100 messages, each one between 1 and 19 attributes (avg = 10) • Experimental parameter • Scalability • Total number of constraints C ≈ I ⅹ f ⅹ c (I: interface, f : filter, c: constraint) • Up to five million constraints • Attribute and constraint name • Set of 1000 elements • Select random words out of a common dictionary using a Zipf distribution LAB Seminar 2004

  21. Evaluation (2/6) • Experimental parameter (cont’d) • Attribute and constraint value • Combination of dictionary values for strings and a range for integers • 1000 words, 100 integers • Uniform distribution for selecting values • Attribute and constraint type • 50% strings and 50% integers • Operators in integer constraints: 60% (equality), 20% (less-than), 20% (greater-than) • Operators in string constraints: 35% (equality), 15% (prefix), 15% (substring), 10% (less-than), 10% (greater-than) LAB Seminar 2004

  22. Evaluation (3/6) • Basic results One filter per interface<-worst case Pre-processing round Reduction of matching time up to 40% Centralized Architecture LAB Seminar 2004

  23. Evaluation (4/6) • Basic results (cont’d) Fixed number of interfaces => More closely modeling High ratio of filters per interface Distributed Architecture LAB Seminar 2004

  24. Evaluation (5/6) • Basic results (cont’d) LAB Seminar 2004

  25. Evaluation (6/6) • Sensitivity to the number of pre-processing rounds Performance gain over simple counting algorithm LAB Seminar 2004

  26. Conclusion • In this paper • Present the first algorithm designed specifically for the implementation of the forwarding function of routers in a content-based network • Refine, adapt, and extend earlier work in the area of centralized content filtering • Formulate a variant of the counting algorithm that can handle disjunctive predicates • Develop optimization targeted specifically at the disjunctions that are the semantics of network interfaces in a content-based network LAB Seminar 2004

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