Title: IEG5270 Advanced Topics in P2P Networking P2P streaming protocols and analysis
1IEG5270 Advanced Topics in P2P NetworkingP2P
streaming protocols and analysis
- Dah Ming Chiu
- Chinese University of Hong Kong
2Coolstreaming
- XY Zhang, JC Liu, B Li, P Yum, Coolstreaming/DONe
t a data-driven overlay network for efficient
live media streaming, Infocom 2005 - DONet Data-driven Overlay Netwrok
- Coolstreaming Cooperative Overlay Streaming
- Background
- XY Zhang was a CUHK M.Phil student
- Built a prototype p2p streaming system, using
Python (2000 lines) - Experimented on PlanetLab
- Deployed in May 30, 2004 during European soccer
finals - Total 30000 downloads, 4000 simultaneous users
- First large scale p2p streaming experiment
showed feasibility
3System diagram of Coolstreaming software
4Low overhead
5Continuity playback performance
6Performance for different streaming rates
7Compare to tree-based experiment
8Drastic improvement over tree-based
9Peer dynamics
102-3 years later
- P2P streaming of live video (e.g. TV) is becoming
established services (at least in China) - PPlive, PPstream, UUSee
- PPlive claims to have 75m installed base, and 20m
active users - See http//www.sigcomm.org/sigcomm2007/p2p-tv/
- More programs are legitimate content (purchased)
- Major issues
- Managing service quality, QoS integration with
CDN? - Delivering advertisement
- Managing relationships with ISPs
11BiToS BitTorent Streaming
- A Vlavianos, M Iliofotou, M Faloutsos, Bitos
Enhancing bit-torrent for supporting streaming
applications IEEE Infocom 2006 - They ask can BT (with small change to its chunk
selection strategy) be used for streaming? - The modified BT by adapting chunk selection
- Select chunks in a sliding window
- Still use rarest first inside the window
- Also create a priority set of chunks to get
- Their work is based on simulation
- Their work inspired YPs work
12BiToS design
13BiToS result
14A simple p2p streaming model
- YP Zhou, DM Chiu and J Lui, A simple model for
p2p streaming, to appear in ICNP 2007 - One of very few performance models of p2p
streaming system - P2p streaming is different from p2p file
downloading - Rate (throughput) is given
- Need good continuity
- Few freezes
- Few jumps
- Need low start-up delay if possible
15Simple peer model
- M homogeneous peers
- Each has a playback buffer
- In each time slot, the server uploads one peer
with probability 1/M - Without P2P network
- continuity p(n)
- 1/M
server
playback
1/M
1/M
M peers
1/M
16Sliding window
- Each peers buffer is a sliding window
- In each time slot, each peer downloads from a
random neighbor - q(i) the probability Bufi gets filled
p(1)1/M p(n)?
timet
sliding window
t1
p(1)1/M p(i1) p(i) q(i)
- q(i) w(i)h(i)s(i)
- w(i) peer wants to fill Bufi
- h(i) the selected peer has the content for
Bufi - s(i) Bufi determined by chunk selection
strategy
17Chunk selection strategies
- Greedy
- try to fill the empty buffer closest to playback
- Rarest First
- try to fill the empty buffer for the newest
chunk - since p(i) is an increasing function, this means
Rarest First
18Chunk selection strategy - cont
- Greedy
- p(i1) p(i) p(i) (1- p(i)) (1- p(1) - p(n)
p(i1)) - Rarest first
- p(i1) p(i) p(i) (1- p(i))2
- Also studied
- continuous forms for these difference equations
- simulation
19Which strategy is better?
- What do you mean by better?
- Playback continuity p(n) as large as possible
- Start-up latency ? p(i) /R as small as possible,
for given streaming rate R - Given buffer size (n) and large peer population
(M) - Rarest first is better in continuity!
- Greedy is better in start-up latency
20Numerical result
- M1000
- N40
- In simulation,
- neighbors60
- Uploads at most 2 in each time slot
21Mixed strategy
- Partition the buffer into 1,m and m1,n
- Use RF for 1,m first
- If no chunks available for download by RF, use
Greedy for m1,n - Difference equations become
- p(1) 1/M
- p(i1) p(i) p(i) (1- p(i))2
for i 1,,m-1 - p(i1) p(i) p(i) (1- p(i))(1- p(m)- p(n)
p(i1)) for i m, n-1
22Comparison
- For different buffer sizes
- Mixed achieves better continuity than both RF
and Greedy - Mixed has better start-up latency than RF
23Closer look with simulation
- Simulate 2000 time slots
- Continuity is the average of all peers
- Continuity for Mixed is more consistent, as well
highest
24Adapting m to unknown population
- Adjust m so that p(m) achieves a target
probability (e.g. 0.3) - In simulation study, 100 new peers arrive every
100 slots - m adapts to a larger value as population increases