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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
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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 802.3 az)– Focuses on saving network energy for Ethernet
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
• Networks are provisioned for peak-load– phone network needs to work on 1st JAN, at 12AM
• Average utilization is low:
Opportunity
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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
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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 reducesidle energy
Slowing downreduces both
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Outline
1.Key questions and method2.Sleeping3.Rate adaptation (slowing down)4.Sleep vs. Rate adaptation
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1. Key questions and method
• How much energy can we save without compromising performance?
• Can we realize these savings with practical schemes?
Methodology:1. Model hardware support for sleep and rate adaptation2. Evaluate savings/performance with simulations (ns)
• Abilene and Intel topologies and their traffic workloads
3. Look for (unrealistic) bounds as well as practical schemes
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Model• 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
2. Sleeping states
time
power
pidle
psleep
δ
(sleep)
(idle)
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Packets over a link:
• sleep time depends on:
Buffer and burst:
When can a link sleep?
time
δ Transition time
1 2 3 4 5 6 7
Periods of sleep
δδ δ δ
time
1 2 3 4 5 6 7
Sleep
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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
wake @ t=3 t=B+3 t=2B+3
` 2ms 5ms 20ms
tx @ t=1 t=B+1 t=2B+1
@ t=8 t=B+8 t=2B+8
@ t=28 t=B+28 t=2B+28
R1 R2 R3
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Coordination among ingressesBasic idea: align bursts/gaps on links in networks by adjusting relative timing phase of different ingresses
8ms
3mst+5, t+5+B,…
t, t+B,…
coordinate burst times to align in the network
R
I1
I2
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Potential for savings with sleep (optB&B)
• “perfect” coordination not generally possible
1ms
2ms
15ms
20ms
t1
t2
• Upper bound (optB&B): Global search to find ingress transmission times that maximize network-wide sleep
I1
I1
R1
R2
t1 + 1ms = t2 + 20ms
t1 + 15ms = t2 + 2ms
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Potential benefits of sleeping
A little shaping can get most of the utilization gain
Abilene, transition time=1ms, B=10ms
Upper bound withoutbuffering/shaping
Upper boundfor any scheme
idle (bound)WoA (pareto)WoA (CBR)optB&B(CBR)
Upper bound withbuffering/shaping
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Practical sleeping algorithm (practB&B)
1. Ingress buffers and transmits packets in a bunch every Bms2. Within bunch, packets are organized by egress3. Router interfaces wake to process bursts4. Router interfaces sleep if start of next burst is >2δ ms away
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No coordination (practB&B)
Practical algorithm realizes most of the benefit
Abilene, transition time=1ms, B=10ms
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Impact of sleeping on delay
No added loss; added delay ~ bounded by Bms
Abilene, transition time=1ms98
th p
erce
ntile
del
ay (
ms)
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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%
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Outline
1.Key questions and method2.Sleeping3.Rate adaptation (slowing down)4.Sleep vs. Rate adaptation
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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
time
power
pi+1
pi
δ
(1G)
(100M)
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Using performance states
Optimal algorithm: ideal service curve follows shortest Euclidean distance.
bytes arriving at router
bytes leaving router
service rate
• Basic idea: decrease rate as much as possible without introducing more than than d ms per hop
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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 sized: target maximum queuing delayri : current link operating rate
Rules: 1. increase to ri+1 iff (q/ri > d) OR (δrf +q)/ri+1> (d- δ)
2. decrease to ri-1 iff (q = 0) AND (rf < ri-1 )– duration since last rate change > k δ (k=4)
Leave headroom fortransition time
Avoid frequent changes
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Benefits of rate adaptationAbilene, transition time δ =1ms, d=3ms
Upper boundfor any scheme
Practical rate adaptation close with uniform rates
Far with exponential rates• Added delay < d * (#hops)
• No observed packet loss
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Outline
1.Key questions and method2.Sleeping3.Rate adaptation (slowing down)4.Sleep vs. Rate adaptation
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Models of future power profiles
pactive = C + fn(rate)
pidle = C + β fn(rate)
psleep = μ pidle(rmax)
Fraction of power that doesn’t scale with rate
Idle/Active Workload Ratio
Rate scaling function
fn(rate) ~ ratefrequency scaling
fn(rate) ~ rate3
dynamic voltage scaling
Power reduction using sleep
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ObservationsThe 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.
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ObservationsWhen rate adaptation is done using frequency scaling the authors themselvessay 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.