Storage Optimization for a
Peer-to-Peer Video-OnDemand Network
Jagadeesh M. Dyaberi, Vijay S. Pai, and
Karthik Kannan
Purdue University
Introduction



Video-on-Demand
(VoD) is commercially
important (video
anytime, anywhere)
Huge growth in VoD
subscribers and
requests
HDTV-quality streams
enabled by increasing
downlink bandwidth
Clients
VoD Server
2
IPTV definition

The official definition approved by the
International Telecommunication Union focus
group on IPTV (ITU-T FG IPTV) is as
follows: "IPTV is defined as multimedia services
such as television/video/audio/text/graphics/data
delivered over IP based networks managed to
provide the required level of quality of service
and experience, security, interactivity and
reliability." (e.g., Amazon VoD; Hulu; YouTube)
3
VoD Growth
(a) Subscriber growth

(b) # of VoD requests
Data analysis of logs from a nationwide IPTV service provider (Amazon
VoD 7.5% per month; 75% more requests per day July 2009; spike on
weekend)
4
IPTV Architecture
National Server
Constrained
Servers
Regional Servers
Constrained
Link
Community Switch/
DSLAM
STB
STB
5
Network trends


Client-server model alone
is not scalable
Inter-client (peer to peer)
data transfers can help
 Especially
Peer-to-Peer (P2P) System
under ISP-level
control!
(e.g., cable operator)

Demand varies cyclically
during day
6
Contributions



Present mathematical model to
optimize data allocation and retrieval
Pre-populate data during low-load
phase
Simulation results show reduction in
server load up to 50%
7
Outline
Introduction and Contributions
 Background
 Optimization Formulation
 Results and Conclusion

8
Background: IPTV Characteristics





Customers are provided with a Set-top
Box(STB) to access IPTV service
Built in hard-drive for DVR purposes
Hard-drive capacity of STBs currently
exceeds 100 GB
STBs are always on (no “churn”)
STBs centrally controlled
9
Background: IPTV Data Characteristics
Viewing behavior influenced by content
recency and external factors among others
 Nearly 47% of content overlapped
between consecutive weeks
 Recently added content more popular

 Six
of the top ten popular movies for the week
were added at the beginning of the week
10
Background: BitTorrent (BT)



Popular P2P file distribution protocol on the
Internet
Splits file into many pieces and downloads
concurrently from multiple peers
Toast added VoD Server to BT P2P network
 Server

serves any piece upon request
Modified Client
 Tracks
current location in file stream
 Requests upcoming missing pieces from server

Toast allows BitTorrent to operate normally, but
still ensures clients have uninterrupted streams
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Improving Toast with Pre-seeding
Toast deals with a single stream
 Multiple streams result in fewer peers to
download data from
 Pre-seeding: distribute data ahead of time
to improve data availability in the network
 Pre-seeding done at low load cycle
 Centralized control obviates need for
BitTorrent incentive mechanisms

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Analysis: VoD system load
Request Distribution by Hour
2500
1500
1000
500
9:
00
10
:0
0
11
:0
0
12
:0
0
13
:0
0
14
:0
0
15
:0
0
16
:0
0
17
:0
0
18
:0
0
19
:0
0
20
:0
0
21
:0
0
22
:0
0
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:0
0
8:
00
7:
00
6:
00
5:
00
4:
00
3:
00
2:
00
1:
00
0
0:
00
# Of Requests
2000
Time
 Low load between 2 am to 8 am
 Exploit this low load cycle for pre-seeding
(Source: Zebroid: using IPTV data to support peer-assisted VoD content delivery,
NOSSDAV 2009)
13
Challenges in pre-seeding

Peers must be providercontrolled to avoid churn


(e.g., cable STBs)
BitTorrent Peers
System must consider
capacity and bandwidth
constraints of nodes, as well
as object popularity


Heavily replicate most popular
objects
Pre-seed less popular objects
only if capacity remains
Tracker
14
Pre-seeding strategies
Random seeding
 Popularity-weighted random
 Optimal strategy

 Optimization
framework with objective
function (minimize server load) and
constraints (bandwidth and capacity utilization
at nodes)
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Outline
Introduction and Contributions
 Background
 Optimization Formulation
 Results and Conclusion

16
Optimization Formulation
17
Optimization Formulation
Objective function
Object i is present
at node j
Probability of request
for object i
Probability that node j is
serving object i
Function tries to minimize the probability a request is served by the server
Constraint
Node j is free to
serve an object
Probability there are n
requests
Node is free if it does not server any object over all possible requests
Optimization Formulation
Constraint
Probability that node j is
serving object i
Node j will serve object i if
• object i is present at node j
• node j is free
• no lower-numbered node can serve the object
Constraint
Capacity of node
Formulation Simplification

Poisson request arrival (rate of arrival λ and
service time t) and Taylor series expansion

Simplify Equation (2) to

Linearize Equation(3) to

Unlike real BitTorrent, self-service keeps a node
20
busy
Outline
Introduction and Contributions
 Background
 Optimization Formulation
 Results and Conclusion

21
Experimental Evaluation

Optimization problem solved using
GAMS/BARON
 Time
limit leads to good but not always
optimal solution
Extended BitTorrent simulator developed
by Bharambe et.al., from Microsoft
Research
 No tit-for-tat, choking/unchoking
 “In-order” piece selection
 Pre-seeding capacity of 2 streams per
STB

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Experimental Evaluation
Upload rate of 1 Mbps
 Download rate of 22 Mbps
 Bit rate of 2 Mbps
 Movie stream size of 1GB
 40 clients and 120 streams
 Movie popularity Zipf curve with α = 1
 Three pre-seeding strategies
 Solver solution for different time limit – 4
and 2.5 hours

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Solver allocation for 4 hour limit
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Full Load
25
Varying load level in the network
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Load Balance – Optimized


Standard Deviation of 30 chunks
Max to Min Ratio: 2.8
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Load Balance – Weighted Random


Standard Deviation of 38 chunks
Max to Min Ratio : 6
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System Robustness

Reduced problem size (25 nodes and 50
objects) for fully optimal solution
29
Summary
Provider-controlled pre-seeding can help
to reduce server load beyond basic P2P
 For best impact, must consider node
capacity and bandwidth constraints, as
well as object popularity
 Constrained optimization framework
shown to outperform heuristics
 Reduction in server load up to 50%
 System robust to external events

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Questions?