GREEN IPTV A Resource and Energy Efficient Network

Main objectives of this lecture General Illustrate the usual route taken to make good

Outline • Route to make good computer science research • IPTV today • Research

A typical IPTV network Internet IP Network TV Head End Customer Premises TV. .

More details • All TV channels transmitted always everywhere • IP multicast used –

Why IPTV? • New service on top of an IP network – Still an

Resource inefficiencies • 90% of all TV viewing is restricted to a small selection

Selective pre-fetching of TV channels • Instead of each node pre-fetching all TV channels,

Trace-driven simulation • Using huge dataset from a nationwide IPTV provider: – 255 k

Results – bandwidth savings Number of channels prejoined 160 140 120 50% bandwidth reduction

Results – requests affected 16% 14% < 2% requests affected % of requests 12%

Is it worth it? Conclusion Today probably not, in the future probably yes 1.

Router power consumption model Max power Power consumption Dp Dl Max capacity Load •

3. 0 € 500 Thousands € 450 2. 5 € 400 cost savings (€)

Zapping delay • In IPTV this delay can add up to two seconds or

Several solutions proposed • Video coding and processing techniques • Network level • Problems

Most zapping is linear. . . 1. opportunities 2. problem 3. state of the

IPTV today IPTV NETWORK Requesting channel 3 1. opportunities 2. problem Sending channel 3

IPTV with channel smurfing IPTV NETWORK Requesting channel 3 1. opportunities 2. problem Requesting

Channel smurfing • Besides the channel requested, send N neighbouring channels concurrently for C

Results – requests with no delay % of switching requests with no delay 100%

Results – requests with no delay (zapping periods only) % of switching requests with

Results – average bandwidth Average bandwidth consumption (Mbps) 40 35 30 25 2 neighbours

Is it worth it? • Very simple to implement Distance to ideal predictor –

Take-home message 1. Computer science knowledge can be used in novel, practical, useful ways

Interested in these matters? Any idea what the “G” in GREEN could stand for?

References [Cisco] Cisco visual networking index: Forecast and methodology 2008 -2013, 2008. [Piper] B.

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GREEN IPTV A Resource and Energy Efficient Network for IPTV

Main objectives of this lecture General Illustrate the usual route taken to make good computer science research Specific Exemplify with a resource and energy efficient network for IPTV

Outline • Route to make good computer science research • IPTV today • Research example 1: resource and energy consumption in IPTV • Research example 2: zapping delay in IPTV • Take-home message

1. Look for an opportunity

2. Identify a problem

3. Analyse state of the art

4. Propose solution

5. Evaluate solution

6. Assess relevance

What is IPTV?

A typical IPTV network Internet IP Network TV Head End Customer Premises TV. . . STB DSLAM PC

More details • All TV channels transmitted always everywhere • IP multicast used – PIM-SM – Static multicast trees – No traffic engineering

Why IPTV? • New service on top of an IP network – Still an infant: around 5 years old • The sum of all forms of video (IPTV, Vo. D, P 2 P) will account for over 91% of global consumer traffic by 2013 [Cisco] • In the US there already more than 5 million subscribers, and this number is expected to increase to 15. 5 million by 2013 [Piper] 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Resource inefficiencies • 90% of all TV viewing is restricted to a small selection of channels [Cha et al. ][Qiu et al. ] 160 140 metro/core Number of channels 120 100 80 access 60 Channels with viewers 40 Channels broadcasted 20 0 10 1. opportunities 2. problem 1000 Number of users 3. state of the art 10000 4. solution 100000 5. evaluation 6. relevance

Selective pre-fetching of TV channels • Instead of each node pre-fetching all TV channels, pre-fetch only a selection of channels • Pre-fetch active channels (channels for which there are viewers) + a small number of inactive channels • Room size = number of inactive channels prefetched 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Trace-driven simulation • Using huge dataset from a nationwide IPTV provider: – 255 k users, 6 months, 150 TV channels, 622 DSLAMs, 11 regions 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Results – bandwidth savings Number of channels prejoined 160 140 120 50% bandwidth reduction 33% bandwidth reduction 100 80 60 40 20 0 r=5 r = 10 r = 15 r = 20 r = 25 r=0 All (access) (access) (metro) channels 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Results – requests affected 16% 14% < 2% requests affected % of requests 12% 10% < 0. 1% requests affected 8% 6% 4% 2% 0% r=0 r=5 r = 10 r = 15 r = 20 r = 25 r=0 All (access) (access) (metro) channels 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Is it worth it? Conclusion Today probably not, in the future probably yes 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

What about energy savings?

Router power consumption model Max power Power consumption Dp Dl Max capacity Load • We assume the router can turn off ports not in use • Δl and Δp based on real measurements [Chabarek et al. ] • 250 edge routers + 50 core routers 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

3. 0 € 500 Thousands € 450 2. 5 € 400 cost savings (€) € 350 2. 0 energy savings(k. Wh) € 300 1. 5 € 250 € 200 1. 0 € 150 € 100 0. 5 Cost savings per year Energy savings per year Millions Is it worth it? (2) € 50 0. 0 € 0 today 2 -4 years 10 -15 years Conclusion Today not, in the future maybe 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Zapping delay • In IPTV this delay can add up to two seconds or more – should be below 430 ms[Kooji et al. ] 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Several solutions proposed • Video coding and processing techniques • Network level • Problems of existing solutions: – Complexity – Additional video servers needed 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Most zapping is linear. . . 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

IPTV today IPTV NETWORK Requesting channel 3 1. opportunities 2. problem Sending channel 3 3. state of the art 4. solution 5. evaluation 6. relevance

IPTV with channel smurfing IPTV NETWORK Requesting channel 3 1. opportunities 2. problem Requesting channel 2, 3, 4 3. state of the art Sending channel 2, 3, 4 4. solution 5. evaluation 6. relevance

Channel smurfing • Besides the channel requested, send N neighbouring channels concurrently for C seconds. 1. opportunities 2. problem 3. state of the art 4. solution 5. evaluation 6. relevance

Results – requests with no delay % of switching requests with no delay 100% 80% 60% 40% 2 neighbours 4 neighbours 20% 6 neighbours 0% 10 seconds 30 seconds 1 minute 2 minutes Always 8 neighbours 10 neighbours -20% ideal predictor -40% -60% -80% 1. opportunities 2. problem Concurrent channel time 3. state of the art 4. solution 5. evaluation 6. relevance

Results – requests with no delay (zapping periods only) % of switching requests with no delay 100% 80% 60% 40% 2 neighbours 4 neighbours 20% 6 neighbours 0% 10 seconds 30 seconds 1 minute 8 neighbours 10 neighbours -20% ideal predictor -40% -60% -80% 1. opportunities 2. problem Concurrent channel time 3. state of the art 4. solution 5. evaluation 6. relevance

Results – average bandwidth Average bandwidth consumption (Mbps) 40 35 30 25 2 neighbours 4 neighbours 20 6 neighbours 8 neighbours 15 10 neighbours ideal predictor 10 5 0 10 seconds 30 seconds 1. opportunities 2. problem 1 minute 2 minutes Concurrent channel time 3. state of the art 4. solution Always 5. evaluation 6. relevance

Is it worth it? • Very simple to implement Distance to ideal predictor – Small software upgrade – No additional video servers needed – Performance close to optimal predictor 45% 40% 35% 30% 2 neighbours 25% 4 neighbours 20% 6 neighbours 15% 8 neighbours 10% 10 neighbours 5% 0% 10 seconds 1. opportunities 2. problem 30 seconds 1 minute 2 minutes Concorrent channel time 3. state of the art Always 4. solution 5. evaluation 6. relevance

Take-home message 1. Computer science knowledge can be used in novel, practical, useful ways 2. It is important to build realistic scenarios to evaluate our ideas 3. It is fundamental to accept that any technical solution has limitations and that these should not be concealed 4. Be aware that a solution to a problem is only relevant if the benefits clearly outweigh the disadvantages

Interested in these matters? Any idea what the “G” in GREEN could stand for? Feel free to contact me: fernando. ramos@cl. cam. ac. uk

References [Cisco] Cisco visual networking index: Forecast and methodology 2008 -2013, 2008. [Piper] B. Piper. United states IPTV market sizing: 2009 -2013. Technical report, Strategy Analytics, 2009. [Cha et al. ] M. Cha, P. Rodriguez, J. Crowcroft, S. Moon, and X. Amatriain. Watching television over an IP network. In Proc. ACM IMC, 2008. [Qiu et al. ] T. Qiu, Z. Ge, S. Lee, J. Wang, Q. Zhao, and J. Xu. Modeling channel popularity dynamics in a large IPTV system. In Proc. ACM SIGMETRICS, 2009. [Chabarek et al. ] J. Chabarek, J. Sommers, P. Barford, C. Estan, D. Tsiang, and S. Wright. Power awareness in network design and routing. In Proc. IEEE INFOCOM, 2008. [Kooji et al. ] R. Kooij, K. Ahmed, K. Brunnstr¨om, and K. Acreo. Perceived quality of channel zapping. In proceedings of the IASTED , 2006.