View
233
Download
1
Category
Tags:
Preview:
Citation preview
1
Cache Me If You Can
NUS.SOC.CS5248OOI WEI TSANG
2
You Are Here
Network
Encoder
Sender
Middlebox
Receiver
Decoder
NUS.SOC.CS5248OOI WEI TSANG
3
Cache Proxies for Web
A
NUS.SOC.CS5248OOI WEI TSANG
4
Hierarchical Caching
A
B
NUS.SOC.CS5248OOI WEI TSANG
5
Cooperative Caching
A B
NUS.SOC.CS5248OOI WEI TSANG
6
Distributed Caching
A B
NUS.SOC.CS5248OOI WEI TSANG
7
Streaming Media vs. Web Pages
NUS.SOC.CS5248OOI WEI TSANG
8
Video Access Pattern
by S. Acharya and B. Smith
Study at Lulea University, Sweden 55% complete, 45% stop very early High temporal locality
NUS.SOC.CS5248OOI WEI TSANG
9
Bimodal Distribution
10 30 50 70 90
% Played
NUS.SOC.CS5248OOI WEI TSANG
10
Zipf Distribution
log (Frequency)
log (rank)
Frequency = C/rankm
NUS.SOC.CS5248OOI WEI TSANG
11
Zipf Law
Frequency of English Words“the” “of” “to” “a”
City size
12
Benefits of Caching
NUS.SOC.CS5248OOI WEI TSANG
13
Reduce Access Latency
:) :(
NUS.SOC.CS5248OOI WEI TSANG
14
Reduce Server Load
NUS.SOC.CS5248OOI WEI TSANG
15
Buy Some Time
NUS.SOC.CS5248OOI WEI TSANG
16
Reduce Client Buffer Size
17
Type of Cache
NUS.SOC.CS5248OOI WEI TSANG
18
Types of Cache
Static caching
Dynamic cachingpatching buffer
NUS.SOC.CS5248OOI WEI TSANG
19
Static Caching
Cache “all or nothing” is badSplit video into segments of size
S
NUS.SOC.CS5248OOI WEI TSANG
20
If I am writing a survey Introduction
Differences between web and media caching
Video access pattern Benefits of caching
Different type of cache Proxy caching architecture
NUS.SOC.CS5248OOI WEI TSANG
21
Other Caching Issues
How to segment?Which segments to cache?When to fetch data?Who to fetch from?When cache is full, who to kick
out?How to measure popularity?Can cache adapt to popularity?
NUS.SOC.CS5248OOI WEI TSANG
22
Rest of the Lecture
Soccer – cooperative caching proxy
Silo, rainbow and cache token – distributed caching proxy
Mocha – with congestion control
23
SOCCER
Self-organizing cooperative caching architecture
NUS.SOC.CS5248OOI WEI TSANG
24
How to segment?
NUS.SOC.CS5248OOI WEI TSANG
25
Effects of Size S
Large S : Low utilizationSmall S : Lots of gaps
NUS.SOC.CS5248OOI WEI TSANG
26
Prefix Caching Policy
1 Chunk = k segments
NUS.SOC.CS5248OOI WEI TSANG
27
Fetching Cache Data
B
A
Server
Client 2Client 1
NUS.SOC.CS5248OOI WEI TSANG
28
Option 2
B
A
Server
Client 2Client 1
NUS.SOC.CS5248OOI WEI TSANG
29
Option 3
B
A
Server
Client 2Client 1
NUS.SOC.CS5248OOI WEI TSANG
30
Issues
How to advertise?How to choose helper?
NUS.SOC.CS5248OOI WEI TSANG
31
How to Advertise?
Balance betweennetwork loadfreshness of information
NUS.SOC.CS5248OOI WEI TSANG
32
Scalable AdvertisementExpanding Ring Advertisement
163264128
NUS.SOC.CS5248OOI WEI TSANG
33
How to Choose Helper?Consideration for Static Cache
network distance (1,2,3,4)number of streams being served
NUS.SOC.CS5248OOI WEI TSANG
34
Static Cache
Cost for retrieving data from node X to node Y =
treamsCurrNumOfSreamsMaxNumOfSt
reamsMaxNumOfStYXDist
*),(
NUS.SOC.CS5248OOI WEI TSANG
35
Dynamic Caching
Need to consider the size of patching buffer
S
NUS.SOC.CS5248OOI WEI TSANG
36
Dynamic Caching
Nused
Nfetched = min(s, Nused + Δ)
S
NUS.SOC.CS5248OOI WEI TSANG
37
Dynamic Cache
Potential Waste = Nfetched/Nused
Integrate into overall cost
NUS.SOC.CS5248OOI WEI TSANG
38
Helper Selection
Helper ask other helpers to help if segments not found on local cache
Pick lowest cost helper
NUS.SOC.CS5248OOI WEI TSANG
39
If Static Cache
Retrieve to fill current gapMay pick different helpers for
different gaps
Should avoid frequent switching
NUS.SOC.CS5248OOI WEI TSANG
40
If Dynamic Cache
Stick to one helper
NUS.SOC.CS5248OOI WEI TSANG
41
Evaluations
SOCCER
Hierarchical Proxies
Single Proxy
42
Silo, Rainbow and Cache Token
NUS.SOC.CS5248OOI WEI TSANG
43
Distributed Caching
B
A
Server
new clip!
NUS.SOC.CS5248OOI WEI TSANG
44
Distributed Caching
B
A
Server
NUS.SOC.CS5248OOI WEI TSANG
45
Distributed Caching
B
A
Server
NUS.SOC.CS5248OOI WEI TSANG
46
Distributed Caching
B
A
Server
NUS.SOC.CS5248OOI WEI TSANG
47
Cooperative vs DistributedCooperative caching caches
independentlyDistributed caching caches as a
team
NUS.SOC.CS5248OOI WEI TSANG
48
Problems
Who should cache what?
Which segment to kick out?
How to redistribute data?
NUS.SOC.CS5248OOI WEI TSANG
49
RCache
Np proxies
video of length Lv
divide into Ns equal segments
Each proxy caches each segment with a/Np probability
NUS.SOC.CS5248OOI WEI TSANG
50
Analysis
Probability that whole video is cached
Storage requirement aL
1ln
11
sNa
sN
NUS.SOC.CS5248OOI WEI TSANG
51
Two Parameters
How to divide video into segments?
What is the probability of storing each segment?
NUS.SOC.CS5248OOI WEI TSANG
52
Silo
segment size probabilityof storage
NUS.SOC.CS5248OOI WEI TSANG
53
Problems
Who should cache what?
Which segment to kick out?
How to redistribute data?
NUS.SOC.CS5248OOI WEI TSANG
54
Compute Popularity
For each videoFor each segment
NUS.SOC.CS5248OOI WEI TSANG
55
Rainbow Algorithm
less popular
NUS.SOC.CS5248OOI WEI TSANG
56
Problems
Who should cache what?
Which segment to kick out?
How to redistribute data?
NUS.SOC.CS5248OOI WEI TSANG
57
Data Redistribution
When popularity changes, need to redistribute.
Redistribute “on-demand” (lazy)
NUS.SOC.CS5248OOI WEI TSANG
58
Cache Token
Each segment have two bits(T,C)
T: I am suppose to have the segment C: I have the segment
NUS.SOC.CS5248OOI WEI TSANG
59
Data Redistribution
(T=1,C=1)(T=0,C=0)(T=1,C=0) (T=0,C=1)
NUS.SOC.CS5248OOI WEI TSANG
60
Silo, Rainbow and TokenDistributed Caching ArchitectureFault tolerantScalable
61
Proxy Cache + Congestion Control
NUS.SOC.CS5248OOI WEI TSANG
62
Scenario (10am)
A
Server
Client 2
NUS.SOC.CS5248OOI WEI TSANG
63
Scenario (1am)
A
Server
Client 2
NUS.SOC.CS5248OOI WEI TSANG
64
Problem
Cache interfere with congestion control algorithm
Make cache adapt aware of quality adaptation
NUS.SOC.CS5248OOI WEI TSANG
65
Congestion Control ProtocolRAP: AIMD + Layered Coding
“sender-driven layered unicast”
NUS.SOC.CS5248OOI WEI TSANG
66
Solutions
Making cache “quality-aware”PrefetchReplacement Algorithm
NUS.SOC.CS5248OOI WEI TSANG
67
Cache Miss
A
Server
Client 2
NUS.SOC.CS5248OOI WEI TSANG
68
Cache Hit
A
Server
Client 2
repair +prefetch
NUS.SOC.CS5248OOI WEI TSANG
69
Peeking Inside the Cache
NUS.SOC.CS5248OOI WEI TSANG
70
Cache Hit: Repair
NUS.SOC.CS5248OOI WEI TSANG
71
Cache Hit: Prefetch
NUS.SOC.CS5248OOI WEI TSANG
72
Cache Hit: Prefetch
NUS.SOC.CS5248OOI WEI TSANG
73
Prefetch Algorithm
prefetchwindow
playbackpoint
NUS.SOC.CS5248OOI WEI TSANG
74
Proxy Request to ServerMultiple requests (for different
clients) are batched.
NUS.SOC.CS5248OOI WEI TSANG
75
Server Response
Missing segments are sent in decreasing priority
1
2
3 4
NUS.SOC.CS5248OOI WEI TSANG
76
Server Response
Send as many segments as possible until next prefetch request
NUS.SOC.CS5248OOI WEI TSANG
77
Solutions
Making cache “quality-aware”PrefetchReplacement Algorithm
NUS.SOC.CS5248OOI WEI TSANG
78
Goal of Replacement AlgorithmGoal: converge to efficient state
if a stream is popularaverage quality is highvariation in quality is low
NUS.SOC.CS5248OOI WEI TSANG
79
The Algorithm
NUS.SOC.CS5248OOI WEI TSANG
80
Trashing and Locking
NUS.SOC.CS5248OOI WEI TSANG
81
Choosing Victim
whit (weighted hit) =Tplay/Ttotal
Calculate whit for each layer in a stream over a popularity window
NUS.SOC.CS5248OOI WEI TSANG
82
Example
Popularity Lock? Name Layer
5.9 1 Nemo 0
4.3 1 Nemo 1
4.0 0 Matrix 2 0
3.9 0 Matrix 2 1
1.1 0 Gigli 0
NUS.SOC.CS5248OOI WEI TSANG
83
Evaluations
Two CriteriaCompletenessContinuity
NUS.SOC.CS5248OOI WEI TSANG
84
vs Request Number
NUS.SOC.CS5248OOI WEI TSANG
85
Different Popularity
NUS.SOC.CS5248OOI WEI TSANG
86
Summary
Proxy Caching for videoTechniques for segmentation,
replacement etc. Interactions with other aspect of
streaming
Recommended