Huichen Dai Bin Liu Yan Chen Yi Wang Tsinghua University China Northwestern University USA Outline Background of Named Data Networking NDN Pending Interest Table PIT ID: 362780
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Slide1
On Pending Interest Table in Named Data Networking
Huichen
Dai
,
Bin
Liu, Yan
Chen*,
Yi
Wang
Tsinghua University,
China
*
Northwestern University, USASlide2
Outline
Background of Named Data Networking (NDN)
Pending Interest Table (PIT)
Problem StatementChallengesMeasure PIT Size and Access FrequencyNCE to Accelerate PIT Access and Size ShrinkingEvaluationConclusion
2
/33Slide3
Background of NDN (1/3)
Newly
proposed clean-slate
network architecture;Embraces Internet’s function transition from host-to-host communication to content dissemination;Routes and forwards packets by content names;Two kinds of packets: Interest packet and Data packet.Request-driven communication model (pull);3/33Slide4
Background of NDN (2/3)
Content Store (CS):
the router’s buffer memory
that caches Data packets;Pending Interest Table (PIT): each entry records the name of a pended Interest and the interfaces from which the Interest comes in.Forwarding Information Base (FIB): NDN forwarding table.4/33Slide5
Background of NDN (3/3)
Figure: Interest Packet lookup and forwarding process
Figure: Data packet lookup and forwarding process
Content Store
Pending Interest Table
Forwarding Information
Base
5
/33Slide6
Pending Interest Table (1/3)
A special table in NDN and no equivalent in IP
.
Keeps track of the Interest packets that are received but yet un-responded.Brings NDN significant features:communication without the knowledge of host locations;loop and packet loss detection;multipath routing support; etc.6/33Slide7
Pending Interest Table (2/3)
Read and write on PIT.
Content name
Interface list
PIT
com/
google
/maps
1
com/
google
/scholar
3
......
……
com/
google
/maps/data/1/
com/
google
/scholar/data/1/
lookup
Interest Packet
Data Packet
inserted
inserted
deleted
deleted
A Dynamic Process!
7
/33Slide8
Problem Statement (1/2)
A
router inserts every
incoming Interest into PIT, and removes each received Data packet from PIT.High-speed packet arrival rate leads to a large PIT size and extremely high access (lookup, insert and delete) frequency.Two major problems about PIT arise:The size of PIT?The access (lookup, insert and
delete
)
frequency of PIT?
Size?
Frequency?
8
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Problem Statement (2/2)
Senior researchers
(
Lixia Zhang, Van Jacobson, etc.) witness Internet’s function transition;NDN is empirically proposed based on their experience;A gap from experience to practice: can PIT be satisfied by off-the-shelf technologies?No previous study touched the feasibility issue.No statistics support.
9
/33Slide10
Challenges
No NDN deployment;
No data;
No PIT;10/33Slide11
A fresh look at NDN (1/2)
-- from application perspective
NDN better satisfies the requirements of users:browsing web pages, watching videos, sharing files, etc.Users demand the same network applications, but require different implementations in NDN.The PIT entries are consumed by application-triggered requests.11/33Slide12
A fresh look on NDN
(1/2)
-- PIT entry filled by app-triggered InterestsA sample PIT:Content nameInterface list
PIT
com/
google
/maps
1
com/
google
/scholar
3
Avatar
2
parc.com/
email_service
/van/example@gmail.com/123
5
……
……
HTTP Interest
P2P Interest
Email Interest
The applications do not change, solve the problems from the application perspective!
12
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Apps from IP
to
NDN
(1/5)Major Internet applications: HTTP, FTP, P2P, Email, Online games, Streaming media, Instant messaging, etc.Implement these applications using NDN’s request-driven communication model.Afterwards, examine how these applications construct and destruct PIT entries.HTTP/1.1 as an example.13/33Slide14
Apps from IP to NDN -- HTTP (
2/5
)
A request/response protocol;A client sends a request over a connection to a server, The server responds with a message.HTTP/1.1 adopts Persistent Connections:HTTP requests and responses can be pipelined on a connection.Simplest Case:
14
/33Slide15
Apps from IP to NDN -- HTTP(3/5
)
However, the actual case is complicated
.For HTTP Response:HTTP response divided into multiple Data packets due to the MTU,At the corresponding PIT entry is created, and at
the entry is
removed,
Entry Lifetime:
.
15
/33Slide16
Apps from IP to NDN -- HTTP(4/5
)
For HTTP Request:
HTTP requests can be divide into different categories, including GET, HEAD, POST, PUT, DELETE, TRACE;GET, HEAD – pull data from server;POST, PUT – push data to server;DELETE, TRACE – function calls on the server.NDN can only pull data from server.16
/33Slide17
Apps from IP to NDN -- HTTP(5/5
)
GET,
HEAD – implemented by Interest packet directly;Extend the function of Interest packet to accommodate the rest methods; POST, PUT – add additional data block to the Interest packet that contains the content to be pushed to the server;DELETE, TRACE – extend Interest packet to enable function calls.17/33Slide18
Measure PIT Size and Access Freq. (1/3)
So far, we have
So we can:
We get data – we quantify PIT size and access frequency by analyzing the NDN trace.Applications on IP
Applications on NDN
IP trace
NDN trace
translate
18
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Measure PIT Size and Access Freq.
– Metrics (2/3)
Size
: the number of PIT entries at each snapshot by examining the lifetime of each PIT entry.Lookup frequency: the # of Data packets (excluding the last Data of a request/response pair) arrive per second;Insert frequency: the # of emerging biflows (new Interests arrive)Delete frequency: the # of disappearing biflows
(last Data packet of a request/response pair
arrive
).
19
/33Slide20
Measure PIT Size and Access Freq.
– Results (3/3)
A one-hour IP trace
from a 20 Gbps link in China Education and Research Network (CERNET).PIT size: 1.5 millionFrequencies of the first 10 seconds: (1.4 M/s, 0.9 M/s, 0.9 M/s)
Methodology is what matters.
20
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Data Structure for PIT (1/3)
PIT size and access frequency demand a well-organized data structure to implement PIT.
Inspired by the
Trie structure in IP lookup, we organize PIT by a Trie as well.
A sample IP
trie
,
bit-wise.
21
/33Slide22
Data Structure for PIT (2/3)
NDN name is different from IP address;
IP addresses:
Fixed length, short… NDN names: hierarchical, composed of component, enables aggregation;Unbounded # of components, much longer than IP;E.g., /com/parc/bulletin/breakthroughs.htmlTherefore, we adopt Name Prefix Trie, which is of component granularity.22
/33Slide23
Data Structure for PIT (3/3)
Name Prefix
Trie
(NPT), each edge stands for a name component.Name
Ports
/com/yahoo
1
/com/yahoo/news
1
/com/yahoo/maps/
uk
2
/com/
google
2
/com/
google
/maps
1, 2
/
cn
/
google
/maps
3
/
cn
/
sina
2, 3
/
cn
/
baidu
4
/
cn
/
baidu
/map
4
An example of PIT
Name Prefix Tree
1
2
9
3
7
A
D
com
cn
4
5
8
B
E
yahoo
google
baidu
google
maps
map
news
maps
maps
6
uk
level-1
level-5
level-2
level-4
level-3
C
sina
23
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Name Component Encoding (1/4)
The Name Prefix
Trie
(NPT) makes PIT well organized, but matching each component is slow:The length of each component varies;should be an exact match of string patterns.We further propose to use positive integers to encode components – Name Component Encoding (NCE).An integer occupies smaller memory space compared to a component;Matching integers is much faster.
24
/33Slide25
Name Component Encoding (2/4)
Name Prefix
Trie
(NPT) is transferred to Encoded Name Prefix Trie (ENPT).
1
2
9
3
7
A
D
com
cn
4
5
8
B
E
yahoo
google
baidu
google
maps
map
news
maps
maps
6
uk
level-1
level-5
level-2
level-4
level-3
C
sina
1
2
9
3
7
A
D
1
2
4
5
8
B
E
1
2
1
3
1
1
1
2
1
6
1
level-1
level-5
level-2
level-4
level-3
C
2
Name Prefix Tree
Encode Name Prefix Tree
CAS
25
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Name Component Encoding (3/4)
Preparation: define the edges in the NPT leaving from the same node as a
Code Allocation Set (CAS)
(dotted ellipse on the NPT);The rules to assign codes to components:Assign each name component in a CAS a unique code;The code should be as small as possible (ensures the codes are continuous).The ENPT is a logical structure and is eventually implemented by Simplified State Transition Arrays (S2TA).Benefit: a component match on S2
TA
can be implemented by a single memory access with the codes as indexes!
26
/33Slide27
Name Component Encoding (4/4)
The data structure that really implements the ENPT, each transition stands for a node and its outgoing edges. The codes are indexes of each outgoing edge.
1
2
9
3
7
A
D
1
2
4
5
8
B
E
1
2
1
3
1
1
1
2
1
6
1
level-1
level-5
level-2
level-4
level-3
C
2
Encode Name Prefix
Trie
(ENPT)
Simplified
State Transition Array (S
2
TA)
27
/33Slide28
Evaluation
Two name sets to compose two PITs:
Domain names collected
from ALEXA, DMOZ and our web crawler, around 10 Million;URLs extracted from the HTTP requests in the trace, around 8 Million.Evaluation goals:memory consumption of S2TA, access frequency S2TA, Comparison with other methods.
28
/33Slide29
Evaluation Results (1/3)
Memory Consumption
Figure: 10M
Name Set memory consumption–original size, NPT size, ENPT.compression ratio (10M Name Set): 63.66%Figure: 8M
Name Set memory consumption–original size, NPT size, ENPT
.
compression
ratio
(8M
Name
Set):
12.56%
29
/33Slide30
Evaluation Results (2/3)
Access
frequency
Figure: Lookup, insert and delete performance for the 10M Name SetAverage: 3.27 M/s, 2.93 M/s and 2.69 M/s
Figure: Lookup
, insert and delete performance for the 8M Name Set
Average: 2
.
51
M/s, 1
.
81 M/s and 2
.
18 M/s
30
/33Slide31
Evaluation Results (3/3)
Figure: PIT
access frequency speedup based on
S2TA.All these results reveal that PIT can be implemented by off-the-shelf technologies.
31
/33Slide32
Conclusion
Measure the size and access frequency of a specific PIT;
Prove the feasibility of PIT with off-the-shelf
technologies;Propose NCE to accelerate PIT access operations while reducing PIT size.32/33Slide33
Thanks!
Questions please ^_^
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