1. Reducing Network Energy Consumption via Sleeping and Rate Adaptation

2. Reducing Network Energy Consumption via Sleeping and Rate Adaptation
Authors:
Sergiu Nedevschi
UC Berkeley & Intel Research
Lucian Popa (UC Berkeley)
Sylvia Ratnasamy (Intel Research)
Gianluca Iannaccone (Intel Research)
David Wetherall (U Washington & Intel Research)
My Name: Anand Seetharam

3. Motivation Network energy consumption a growing issue
Equipment increasingly power-hungry (power density)
Rising energy costs (significant fraction of TCO)
Environmental concerns
Energy Efficient Ethernet Taskforce (IEEE az)
Focuses on saving network energy for Ethernet

4. Opportunity Networks are provisioned for peak-load
phone network needs to work on 1st JAN, at 12AM
Average utilization is low:
Network
Utilization
AT&T switched voice
33%
Internet Links
15%
Private line networks
3-5%
LANs 1%
“Data networks are lightly utilized, and will stay that way”
A. M. Odlyzko, Review of Network Economics, 2003

5. Opportunity
Energy consumption proportional to capacity, not actual utilization!!
Idle energy consumption is high
For example, a Cisco GSR linecard draws:
[Chabarek etal, INFOCOM08]
~ 80W idle
~ 90W fully loaded
Most energy consumed by networks is wasted!
Goal: Make network energy consumption reflect
utilization levels, not peak provisioning

6. Idea
Key Idea: Let network equipment sleep for brief periods or slow down when lightly loaded to save energy
Inspiration: Use of sleep and performance states in PCs, processors
Rationale: E ~= pidle Tidle + pactive Tactive
Assumptions: We assume support for sleep/performance states in NICs, linecards, switches, and routers and consider how to best use them
Depend on:
Type/extent of hardware support for sleep and performance states
Careful use of these states to protect performance and maximize savings
Sleeping reduces
idle energy
Slowing down
reduces both

7. Outline Key questions and method Sleeping
Rate adaptation (slowing down)
Sleep vs. Rate adaptation

8. 1. Key questions and method
How much energy can we save without compromising performance?
Can we realize these savings with practical schemes?
Methodology:
Model hardware support for sleep and rate adaptation
Evaluate savings/performance with simulations (ns)
Abilene and Intel topologies and their traffic workloads
Look for (unrealistic) bounds as well as practical schemes

9. 2. Sleeping states Model Metrics
Single sleep state with power psleep<< pidle
δ: transition period (ms)
Timer or activity-driven wakeup
Interfaces sleep independently
Metrics
Energy savings in % time asleep
Performance in loss and max delay
power
pidle
(idle)
psleep
(sleep)
time δ

10. When can a link sleep? Packets over a link: Buffer and burst:
Periods of sleep
Packets over a link:
sleep time depends on:
Buffer and burst: δ
time 1 2 3 4 5 6 7
Transition time δ
time 1 2 3 4 5 6 7
Sleep

11. Making sleep gaps on links with buffer & burst (B&B)
Basic idea: use limited buffering at ingress to create predictable and useful sleep gaps (>2δ); do once, adds bounded delay
2ms
5ms
20ms R1 R2 R3
t=1
t=B+1
t=2B+1
@ t=8
t=B+8
t=2B+8
t=3
t=B+3
t=2B+3
@ t=28
t=B+28
t=2B+28

12. Coordination among ingresses
Basic idea: align bursts/gaps on links in networks by adjusting relative timing phase of different ingresses
t, t+B,… I1
8ms
coordinate burst times to align in the network R
3ms
t+5, t+5+B,… I2

13. Potential for savings with sleep (optB&B)
“perfect” coordination not generally possible t1
1ms
t1 + 1ms = t2 + 20ms I1 R1
15ms
20ms t2
t1 + 15ms = t2 + 2ms I1
2ms R2
Upper bound (optB&B): Global search to find ingress transmission times that maximize network-wide sleep

14. Potential benefits of sleeping
Abilene, transition time=1ms, B=10ms
idle (bound)
WoA (pareto)
WoA (CBR)
optB&B(CBR)
Upper bound
for any scheme
Upper bound without
buffering/shaping
Upper bound with
buffering/shaping
A little shaping can get most of the utilization gain

15. Practical sleeping algorithm (practB&B)
Ingress buffers and transmits packets in a bunch every Bms
Within bunch, packets are organized by egress
Router interfaces wake to process bursts
Router interfaces sleep if start of next burst is >2δ ms away

16. No coordination (practB&B)
Abilene, transition time=1ms, B=10ms
Practical algorithm realizes most of the benefit

17. Impact of sleeping on delay
Abilene, transition time=1ms
98th percentile delay (ms)
No added loss; added delay ~ bounded by Bms

18. Impact of sleep: Any Losses?
No additional losses are incurred until utilizations come close to saturating some links.
Losses greater than 0.1% occur at
Scheme
Utilization
Default
41%
B = 10ms
38%
B = 25ms
36%
Abilene, network utilization=5%

19. Impact of sleep transition time
Quick transitions (preferably < 1ms) needed
Abilene, network utilization=5%

20. Outline Key questions and method Sleeping
Rate adaptation (slowing down)
Sleep vs. Rate adaptation

21. 3. Rate adaptation states
Model
N performance states
Rates r1, …, rn and pi < pi+1
δ : transition period (ms)
Interfaces can rate-adapt independently
Metrics
Energy savings in average rate reduction
Performance in loss and max delay
power
pi+1
(1G)
(100M) pi δ
time

22. bytes arriving at router
Using performance states
Basic idea: decrease rate as much as possible without introducing more than than d ms per hop
bytes arriving at router
bytes leaving router
service rate
Optimal algorithm: ideal service curve follows shortest Euclidean distance.

23. Practical rate adaptation (practRA)
Idea: lower rate if doing so will maintain minimal queuing delay (of at most d ms); aggressively increase rate to clear buildup
Algorithm:
rf : estimated arrival rate as average (EWMA) of past arrivals
q: current queue size
d: target maximum queuing delay
ri : current link operating rate
Rules:
increase to ri+1 iff (q/ri > d) OR (δrf +q)/ri+1> (d- δ)
decrease to ri-1 iff (q = 0) AND (rf < ri-1 )
duration since last rate change > k δ (k=4)
Leave headroom for
transition time
Avoid
frequent
changes

24. Benefits of rate adaptation
Abilene, transition time δ =1ms, d=3ms
Upper bound
for any scheme
Practical rate
adaptation close
with uniform rates
Far with
exponential rates
Added delay < d * (#hops)
No observed packet loss

25. Outline Key questions and method Sleeping
Rate adaptation (slowing down)
Sleep vs. Rate adaptation

26. Models of future power profiles
Fraction of power that doesn’t scale with rate
pactive = C + fn(rate)
pidle = C + β fn(rate)
psleep = μ pidle(rmax)
Rate scaling function
fn(rate) ~ rate
frequency scaling
fn(rate) ~ rate3
dynamic voltage scaling
Idle/Active Workload Ratio
Power reduction using sleep

27. Sleeping and rate adaptation (DVS-r3)

28. Sleeping and rate adaptation (Frequency Scaling -r)

29. Observations
The authors say
“Hence to avoid complex interactions, we consider that the
whole network , or at least well-defined components of it, run
either rate adaption or sleep”
But both schemes can be combined to give better results.
For eg: In rate adaptation one can try to put the links to sleep
instead of keeping them in the idle state.

30. Observations
When rate adaptation is done using frequency scaling the authors themselves
say that for values (C=0.3 and β =0.1) and (C=0.3 and β =0.8) the savings
obtained are poor and add little additional information.
My observation is that rate adaptation (frequency scaling) gives
no gain in terms of energy.

31. Thank you. Questions?