2017/4/26 rethinking packet classification for global network view of software-defined networking...
DESCRIPTION
2017/4/26 Introduction In software-defined networking, applications are allowed to access a global view of the network. Unlike the conventional classification problem to search for the action taken at a single switch, the global network view requires to identify the network-wide behavior of the packet. Conventional classification methods, however, fail to well support network-wide behaviors, since the search space is complicatedly partitioned due to the combinations. National Cheng Kung University CSIE Computer & Internet Architecture Lab CSIE CIAL LabTRANSCRIPT
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Rethinking Packet Classification for Global Network View of Software-
Defined Networking
Author: Takeru Inoue, Toru Mano, Kimihiro Mizutani, Shin-ichi Minato, Osamu AkashiConference: 2014 IEEE 22nd International Conference on Network ProtocolsPresenter: Chih-Hsun WangDate: 2015/10/07
Department of Computer Science and Information Engineering National Cheng Kung University, Taiwan R.O.C.
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Introduction
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In software-defined networking, applications are allowed to access a global view of the network.
Unlike the conventional classification problem to search for the action taken at a single switch, the global network view requires to identify the network-wide behavior of the packet.
Conventional classification methods, however, fail to well support network-wide behaviors, since the search space is complicatedly partitioned due to the combinations.
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Single switch packet behavior
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Network-wide packet behavior
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Binary Decision Diagram
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A Binary Decision Diagram (BDD) is a finite DAG with an unique initial node, where• all terminal nodes are labeled with 0 or 1.• all non-terminal nodes are labeled with a Boolean
Variable.• Each non-terminal node has exactly two edges from
that node to others; one labeled 0 and one labeled 1; represent them as a dashed line and a solid line respectively.
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Binary Decision Diagram
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Binary decision tree and truth table for the function f = ac + bc
a b c f
0 0 0 0
0 0 1 0
0 1 0 0
0 1 1 1
1 0 0 0
1 0 1 1
1 1 0 0
1 1 1 1
a
b
c c
b
c c
0 0 0 1 0 1 0 1
f
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BDD Reduction Rules -1
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Eliminate duplicate terminals• If a BDD contains more than one terminal 0-node, then
we redirect all edges which point to such a 0-node to just one of them.
• Similarly, we proceed for nodes labeled with 1.a
b
c c
b
c c
0 0 0 1 0 1 0 1
a
b
c c
b
c c
0 1
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BDD Reduction Rules -2
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Eliminate redundant nodes• (with both edges pointing to same node)
a
b b
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BDD Reduction Rules -3
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Merge duplicate nodes• Nodes must be unique
a
b
a
c b
a
c
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BDD Reduction
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a
b
c c
b
c c
0 1
a
b
c
b
c
0 1
a
b
c
0 1
Reduction Rules 3 Reduction Rules 2
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Proposed Method
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Proposed Method
These two incomplete multi-valued functions can be unified into a single one without conflict, by introducing the following unification operation
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Multi-valued Decision Diagram
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x = 010111
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Multi-valued Decision Diagram
An MDD, which is shown in Fig. 3 (right), is also an acyclic directed graph with a single root, but it can have more than two terminal nodes, nil, I, II, · · · , |P| . Each non-terminal node is labeled by aggregated bits, and it can have more than two children arcs, 0, 1, · · · , 2K − 1.
The maximum height is L/K. Other properties are the same as those of BDD.
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In our method, a BDD is used to represent a Boolean function that maps the header space to a single packet behavior, while an MDD is used to express a multi-valued function.
BDD of fi(x) is easily converted to MDD of Fi(x),by replacing - and -terminals with nil- ⊥and i-terminals, respectively.
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Multi-valued Decision Diagram
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MDD Unified with Aggregation
0
2
I
3
4
1
5
3
44
0
1
3 3
4 4
II
0,1
2,3 2,3
4,5 4,5 4,5
00 01
0010
0111
10
01
1100
I II
10 11 0110
0001 00
f1 f2 F(2)
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Bit Aggregation
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Bit Aggregation
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This algorithm assumes that all terminal nodes would have been set to the cache in advance.
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Search
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A packet header is represented as an array of K-bit elements; i.e., i-th K-bit element on the header can be accessed by index i, like pkt[i].
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Experiments Environment
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Language C++
CPU Xeon 3.5 GHz Core i7 1.7 GHz
Cache size 8MB 4MB
Memory 16GB 8GB
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Experiments Result
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Experiments Result
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Fig. 6. Memory usage of MDD. The horizontal line is memory usage of HybridCuts for Internet2.
The memory usage of an MDD is the product of MDD size and node size.
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Experiments Result
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Experiments Result
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Experiments Result
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