Towards Eco-Friendly Home Networking Mathias Gibbens, Chris Gniady - - PowerPoint PPT Presentation

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Towards Eco-Friendly Home Networking

Mathias Gibbens, Chris Gniady and Beichuan Zhang Department of Computer Science The University of Arizona Tucson, Arizona

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Home networks are complex

• More and more demand is being placed on router performance
• Routers require more computing power, bandwidth and features

NAS

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Complexity → Power

 Power consumption doubled in 5 years, what about the future?  Always on in 88 million homes, energy footprint of $1 billion

g (2003) n (2009) ac (2013)

Power CPU RAM Dual core! WiFi Speed

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Previous solutions

• Wired: IEEE 802.3az introduced Energy-Efficient Ethernet

▪ First deployed in home networks ▪ Physical connection, easy to detect client

• Large mesh networks: many routers, many clients

▪ Power down redundant access points ▪ Client picks optimal network when more than one is available

• Home networks: one router, many clients

▪ Goma et al: aggregate individual networks ▪ Requires: dense networks, cooperation, client modifications

We need energy management for individual routers

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Our contribution

• Transparent energy optimization of personal networks

▪ Individual routers ▪ No user intervention or modification of clients ▪ No cooperation between networks

• Implementation approach

▪ Discarding unnecessary wireless traffic ▪ Powering down routers when idle ▪ Power cycling with active clients

• Increased energy efficiency of individual home routers

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Outline

 Introduction  Trace collection and categorization  Proposed optimizations  Methodology  Results  Conclusion

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Traffic categorization

 Traffic seen even without clients connected  Lots of idle time when clients are present  Router is in full power mode independent of clients

T1 T2 T3 T4 T5 0% 20% 40% 60% 80% 100% No client broadcast No client Idle client Active client Time

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Eliminating broadcast traffic

 Broadcast traffic, but no clients around to respond  Traffic from wired interface retransmitted over wireless  No one listening → drop broadcast traffic  Safe to perform: clients must be present in order to respond

1 10 100 1000 10000 100000 Client Broadcast

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Powering down when idle

 No clients → power down antennas after a timeout  Periodically check for the arrival of new clients

1 10 100 1000 10000 100000 Client Broadcast Last client departs Power down Power up Power Look for clients

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Optimizing duty cycle: Downtime

• Duty cycle impacts a client's ability to connect
• Need to balance extra delay with potential energy savings
• Infrequent initial associations, which impact only first client

1 2 3 4 5 6 7 8 9 10 11

10 20 30 Antenna down time [s] Connection time [s]

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Optimizing duty cycle: Uptime

• Antennas must be up for at least 4 seconds for clients to connect
• From observed delays, we chose a 5/5 second up/down cycle

4 5 6 7 8 9 10 11

10 20 30 Antenna up time [s] Connection time [s]

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Idle connected clients

• During sufficiently long idle periods, turn off transmit antenna
• Possible because either clients initiate or data arrives on wire
• Router must still periodically announce its presence

1 10 100 1000 10000 100000 Client Broadcast

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Idle connected clients

• WiFi spectrum is inherently error prone

▪ Existing protocols have built in transparent retransmission to compensate

• Clients typically disconnect from network after 7 seconds
• Transparent retransmission gives us opportunity to power up

Time ACK No ACK, try resending Packet lost, inform user

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Idle connected clients

• Additional delay due to power up may be seen by applications

▪ Can be hidden in the time it takes for a response to return to the router

• Transitioning router's state has an energy cost

▪ Only sufficiently long periods should be optimized ▪ Ensures real time data is not interrupted

Router power Client packet Forward packet Begin router power up Complete Response Response to client

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Methodology

 Monitor traffic seen at router: week long traces  Very few initial associations when no other clients present  Detailed power/delay model of ASUS RT-N16 profiled using NI

Trace T1 T2 T3 T4 T5 Average concurrent devices 1 5 4 4 2 Maximum concurrent devices 1 9 7 6 3 Initial associations 13 16 14 31 35 Average time with no client [h] 10.7 0.08 2.07 1.32 0.92 Traffic volume [GB] 5.57 40.21 4.22 3.52 12.97

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Eliminating broadcast traffic

 No client period increased by 10% average in traces 2-5  More opportunities for router to power down

T1 T2 T3 T4 T5 0% 20% 40% 60% 80% 100% No client broadcast No client Idle client Active client Time

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Power cycling no clients

 Power cycling with no clients

has significant impact

 Additional connection delay

for first client paid only

• ccasionally

NB PC NB PC NB PC NB PC NB PC T1 T2 T3 T4 T5 0.0 0.1 0.2 0.3 0.4 0.5 No client Idle client Active client E n e r g y [ M J ]

NB – No Broadcast PC - PowerCycle

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Active client optimizations

 More aggressive power

cycling can reduce energy consumption by an additional 20-30%

 Cumulative energy savings

• bserved to be 12-59%

P C C T C A P C C T C A P C C T C A P C C T C A P C C T C A T1 T2 T3 T4 T5 0.1 0.2 0.3 0.4 0.5 No client Idle client Active client E n e r g y [ M J ]

PC – PowerCycle CT – CycleTransmit CA - CycleAll

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Delay due to state transition

 Active clients see some

delay when initiating activity after a period of idle time

 All delays within

perception threshold, not noticed by user

10 20 30 40 50 60 70 80 0% 20% 40% 60% 80% 100% T1 T2 T3 T4 T5 Additional delay [ms] D e l a y s s e e n

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Conclusion

 Investigated opportunities to reduce energy consumption of

consumer wireless routers

 Collected traces from personal networks  Predicted wireless energy consumption reduced by 12-59%  Changes do not break backwards compatibility

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Thank You Questions?

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Trace collection and analysis

 Five unique week-long traces collected from households  Routers recorded just the wireless traffic seen  Networks used as normal to produce representative traces

1 10 100 1000 10000

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Idle periods with clients

 To save energy with clients,

there must be many idle periods of sufficient length

 Each trace has many long

idle periods

2 4 8 16 32 64 128 256 512 1024 >1024

• 20%

0% 20% 40% 60% 80% 100% T1 T2 T3 T4 T5 Idle time [s] W e i g h t e d i d l e t i m e