Selected Techniques in Content Distribution Networks Pei Cao Cisco Systems, Inc.
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Transcript of Selected Techniques in Content Distribution Networks Pei Cao Cisco Systems, Inc.
![Page 1: Selected Techniques in Content Distribution Networks Pei Cao Cisco Systems, Inc.](https://reader031.fdocuments.us/reader031/viewer/2022032310/56649d575503460f94a36736/html5/thumbnails/1.jpg)
Selected Techniques in Content Distribution Networks
Pei CaoCisco Systems, Inc.
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Enterprise WAN Today
Data Center
Regional Hubs
Branch Offices
Internet
T1
56Kbps,128kbps,DSL …
. . .
. . . . . .
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Why Enterprise CDN (ECDN)
• Overcome bandwidth limitations for video applications to branches
• Distribute very-large files to branches• Cache and police web contents• Consolidate data storage...
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Components of ECDN
WAN
Edge Content Engine(CE)
Edge CE
Data Center
Web Servers
Content Injection Devices
Content Distribution Manager(CDM)
Branches
IOS Router with WCCPIOS Router with WCCP
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Content Delivery
Internet Or
WAN
Internet or Intranet Web Server
HTTP Proxy & Server
Filtering module
Windows Media Proxy & Server
RealNetwork Proxy
MPEG streaming server
CDM Agent Content Dist. Module
RealNetwork Server
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Content Distribution
WAN
Edge Content Engine(CE)
Edge CE
Data Center
Web Servers
Root CE(s)
Content Distribution Manager(CDM)
Branches
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Challenges in Building CDNs
• Network interoperability• System scalability• Content engine performance• System usability
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Outline
• Protocol highlight:Content-based WCCP
• Algorithm highlight: TPUT: Scalable Top-k Algorithm
• Kernel mechanism highlight: Stream Engine
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Request Interception
• “Web Content Caching Protocol” (WCCP) on port 80
Internet Or
WAN
TCP SYN TCP SYN
TCP SYN_ACK
ACKACK
GET … HTTP/1.1 GET … HTTP/1.1
HTTP/1.1 200 OK …Cache Hit:
Cache Miss
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WCCP Bypass
Internet Or
WAN
TCP SYN TCP SYNTCP SYN
TCP SYN
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Dealing with Client Transparency
Internet Or
WAN
GET … HTTP/1.1
GET … HTTP/1.1
HTTP/1.0 200 OK … <META HTTP-EQUIV=\”REFRESH\” …
Cache Miss
404 Not Found …
XXX
TCP SYN TCP SYNTCP SYN
TCP SYN
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Content-Based Interception
• Problem: how to intercept all HTTP traffic from client browsers?
• Possible solutions:– Send all traffic through content engine (CE)
• Issues with per-packet latency and CE throughput
– Send traffic to CE but CE tells router which flows to bypass• High overhead for short flows
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Algorithm Highlight
Scalable Top-k Algorithm
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Top-k Queries in CDNs
Example queries:• List top 10 URLs accessed most often
among all CEs• List top 10 domains that consume
the most storage among all CEs• etc.
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Definitions
• a network of m nodes, connected to a central manager (CM)
• each node i has a reverse-sorted list of ( x, Vi(x) )
• an object’s sum V(x) = V1(x)+V2(x)+…+Vm(x)
• Problem: find the k objects with highest sums
A generic problem in distributed systems
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Existing Methods
• “Naïve” Algorithm– Each node sends the full list of objects
and their values to the Central Manager
• Threshold Algorithm (TA)– Proposed by multiple groups in the
database research community
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The Threshold Algorithm (TA)
(A, 10)(C, 8)(E, 8)(F, 8)(B, 7)(D, 5)(J, 1)(K, 1)
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(B, 10)(D, 9)(F, 8)(H, 6)(G, 5)(C, 1)(A, 1)
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(C, 10)(A, 9)(G, 8)(J, 7)(F, 6)(D, 4)(B, 1)
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Node1
Node2
Node3
Central Manager (CM)
T = 30; V(A)=20, V(C)=19, V(B)=18 T = 26; V(A)=20, V(C)=19, …
T = 24; V(F)=22, V(A)=20, …T = 21; V(F)=22, V(A)=20, …T = 18; V(F)=22, V(A)=20, …
• Example: find top 2 objects with max sums in three columns
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Adapting TA for Distributed Environments
• Consists of multiple “rounds”, each round having two round trips– Round-trip #1 “sorted access”: CM asks for the
next B objects on the lists and nodes respond– Round-trip #2 “random lookup”: CM sends a list
of object names to nodes and nodes supply values
– B = k
• Issues– # of rounds unpredictable– O(m2) network traffic on average
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New Algorithm: Three-Phase Uniform Threshold (TPUT)
• Motivation: terminate in a fixed number of round trips regardless of input
• Operates in three phases1. Lower-bound estimation2. Pruning3. Final lookup
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Partial Sums and Upper Bounds
• Partial sum: PS(x) = ∑Vi’(x)
• Upper bound: U(x) = ∑Ui’(x)
Vi’(x) =Vi(x), if x has been reported by node i to CM
0, otherwise
Ui’(x) =Vi(x), if x has been reported by node i to CM
Ti, otherwise
Ti: Node i sends all objects with values > Ti
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Examples
(A, 10)(C, 8)(E, 8)(F, 8)(B, 7)(D, 5)(J, 1)
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(B, 10)(D, 9)(F, 8)(H, 6)(G, 5)(C, 1)(A, 1)
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(C, 10)(A, 9)(G, 8)(J, 7)(F, 6)(D, 4)(B, 1)
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Node1
Node2
Node3
CM
PS(A) = 10+ 0 + 9 = 19U(A) = 10 + 9 + 9 = 28PS(B) = 0 + 10 + 0 = 10U(B) = 8 + 10 + 9 = 27…
For any object O, PS(O) ≤ V(O) ≤ U(O)
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Steps in TPUTPhase 1:• Manager gets top k objects from each node• Manager:
– Calculate partial sums of all objects – Take the k’th partial sum E1 (E1 ≤E); set t = E1/m
Phase 2:• Manager gets all objects with value ≥ t from each node• Manager:
– Calculate partial sums again; take the k’th partial sum E2 (E1 ≤ E2 ≤ E)
– Calculate upper bounds of all objects– S = {objects whose upper bounds are ≥ E2}
Phase 3:• Manager Nodes: here is S; send me all objects in S• Nodes Manager: here they are
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Example
(A, 10)(C, 8)(E, 8)(F, 8)(B, 7)(D, 5)(J, 1)
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(B, 10)(D, 9)(F, 8)(H, 6)(G, 5)(C, 1)(A, 1)
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(C, 10)(A, 9)(G, 8)(J, 7)(F, 6)(D, 4)(B, 1)
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Node1
Node2
Node3
CM
PS(A) =19; PS(C) =18; E1 = 18; t = 6;
PS(F) = 22; PS(A) =19; E2 = 19U(H) = 18, U(J) = 19 H and J are out!S = (A, B, C, D, E, F, G)
S(F) = 22; S(A) = 20; S(C) = 19; …Top 2 objects are F and A.
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Improving the Pruning Power
• Set t = (E1/m) * α, where 0<α<1
(x1,...)(x2,…)
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Node1
(y1,…)(y2,…)
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Node2
(z1,...)(z2,...)
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Noden
E2/m
t
. . .
U(o)
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Compression via Hashing
• Problem: reducing traffic in phase 2• Solution: send hashed keys of object IDs
– Node report to CM (hash(o), V(o))– Hashed keys are short
– If hash(o1)==hash(o2), then V = max(V(o1), V(o2))
– Candidate set S is a set of hashed keys
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Evaluating TPUT Algorithm
• Trace-driven simulation• Optimality analysis
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Trace Data for Simulations
NLANR-10 daily web access from 10 NLANR proxies
Worldcup-30
2-hr logs from 30 WorldCup web servers
DEC-64 split 1-day DEC proxy traces into 64 sub-traces by client IP
DEC-128 split 2-day DEC proxy traces into 128 sub-traces by client IP
NLANR-203 split NLANR traces into 203 sub proxy traces by client IP
Berkeley-512
Split one week UCB traces into 512 sub traces by client IP
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Results on Unicast-Bytes
m=10 m=30 m=64 m=128 m=203 m=512
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Number of Objects Looked-Up
Trace K=10: TA K=10: TPUT/0.5
NLANR-10 166 18
WorldCup-30 46 12
DEC-64 3164 31
DEC-128 6928 28
NLANR-203 5576 28
Berkeley-512 47899 41
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Results on Multicast-Bytes
m=10 m=30 m=64 m=128 m=203 m=512
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Optimality Analysis
Main results:• TPUT is instance optimal for data sets with a
log-log slope function C(n)– Zipf distribution: C(n) = n– Zipf distribution: opt-ratio = (m-1)*2m +k*m
• Setting α<1 reduces cost qualitatively. – Zipf distribution: opt-ratio = (m-1)O(√m) +k*m/α
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General Instance Optimality
• Definition: An algorithm R is instance-optimal with
optimality ratio C1, if exists C2, such that for any data series D, and any algorithm A,
cost(R, D) ≤ C1 * cost(A, D) + C2
– cost is amount of network traffic– TA is instance optimal with opt-ratio = O(m2)
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Worst Cases for Fixed Number Round-Trip Algorithms
• TPUT is not general instance optimal
• Nor can any algorithm that terminates in a fixed number of round trips
Node 1(A, 1)(C, 1)(X1, 0.6)(X2, 0.6)...(Xn, 0.6)(B, 0.5)..
Node 2(B, 1)(D, 0.2).........
Finding obj with highest sum
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Log-Log Slope Function C(n)
• L(j) is the value at position j in a reverse-sorted list
• The list satisfies log-log slope function C(n), if, for all j≤k, L(j*C(n)) < L(j)/n
• For Zipf-like distribution L(j) ~ 1/jλ, C(n) = n1/λ.
ListPosition 1 . . . . . Position j . . . . . . .Position j*C(n) . . . . . . .
L(j)
< L(j)/n
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Properties of the Two Lower Bounds
• Let E be the “true bottom”
• E1 ≥ E/m
• E2 > E/2
– E2 ≥ E1
– E2 > E – E1*(m-1)/m
E2 > (m/(2m-1))*E
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Restricted Instance Optimality of TPUT (α=1)
• Assume D is a collection of m lists all following log-log slope function C(n), then for any algorithm A,
cost(TPUT,D) ≤ cost(A,D) * ((m-1)*C(2m)+C(m)*k)
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Effect of α<1
• Property: – If object x appears in n nodes in Phase 2 and
U(x)≥ E2, then its average value in those nodes R(x) ≥ E2 * (1-α)/n
• Let li = the num of objects in S that appear in exactly i nodes in Phase 2, then:– 1*l1 + 2*l2 + 3*l3 + … + m*lm ≤ C(m * (1+α)/α) * ∑bi
– For each i, l1 + l2 + … + li ≤ C( i * (1+ α)/(1-α)) * ∑bi
– Size of S is l1 + l2 + … + lm
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Effect of α<1 (Cont’)
• Opt-ratio ≤ (m-1) * C(d*β) + m*k/α, where d isd * C(d*β) - ∑C(i* β) ≤ C(m * (1+α)/α)
For Zipf distribution, TPUT w. α<1 has opt-ratio ≤ (m-1) * c * √m + m*k/α
i=1
d
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Top-k Query Calculation in CDNs
• # of objects small naïve alg.• # of objects large TPUT w. α<1
– Optimal α depends on # of nodes– Limit max # of objects sent in phase 2
• TPUT extends to hierarchical networks easily
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Kernel Mechanism Highlight
Stream Engine
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Building High Performance Internet Streaming Server
• Basic characteristics of streaming protocols– Control channel (TCP): Start/Stop, FF/Rew, Seek, Change bit rate…– Data channel (UDP or TCP): Paced sending of streaming data
• What makes Linux inefficient– Data copies– Context switches
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Observations on Per Stream Flow
Process control command
New Req
Process data
Send data
Exit/ Cleanup
Sleep
Check control channel
Read data
setup
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Process control command
New Req
Process data
Send data
Exit/ Cleanup
Sleep
Check control channel
Read data
setup
Observations on Per Stream Flow < 1%
runtime> 98% code
> 99% runtime
< 2% code
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Where Stream Engine Fits
Process control command
New Req
Process data
Send data
Exit/ Cleanup
Sleep
Check control channel
Read data
Stream Engine
setup
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Streaming File and Data Packets
file header packet1 packet2 packetn indices. . .
ts
Sending time SubBlock1 SubBlock2 Padding
TCP header
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Stream Engine
• In-kernel event driven module to deliver streaming data
• Similar to sendfile() but has streaming logic– Method to assemble data packet– Timed send– Control channel monitoring
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Stream Engine Interface
• client_data_fd• client_control_fd• source_fd & offset• packet_timing_and_assembly
– Example 1: fixed_rate_fixed_block– Example 2: asf_packet_parse
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Performance Comparison
050100150200250300350400
Mbps
DarwinStreamingServer(Eventdriven)
W/OStreamEngine(Processbased)
WithStreamEngine(Processbased)
100Kbps
Based on PC: 1 Xeon 2.8Ghz, 2GB mem, 2 Gigabit interface
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Stream Engine Future
• Put it in hardware– TCP-Offloading Engines– Special blades in Cat6K switches
• To be used by a highly popular Internet radio station
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Summary
• Techniques– Content-based WCCP
• Patent pending
– TPUT as a top-k algorithm• Submitted for publication
– Stream Engine• Published in WCW’2003
• Open research questions