Reducing Network Energy Consumption via Sleeping and Rate Adaptation

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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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Impact of sleep transition time

Quick transitions (preferably < 1ms) needed

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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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Sleeping and rate adaptation (DVS-r3)

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Sleeping and rate adaptation (Frequency Scaling -r)

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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.

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Thank you. Questions?