Transcript PRESENTATION NAME - University of Massachusetts Amherst

Reducing Network Energy
Consumption via Sleeping
and Rate Adaptation
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
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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
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
Sleeping reduces
idle energy
Slowing down
reduces both
• 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
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Outline
1.Key questions and method
2.Sleeping
3.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 adaptation
2. 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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2. Sleeping states
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
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power
pidle
(idle)
(sleep)
psleep
δ
time
When can a link sleep?
Packets over a link:
δ δ
2
1
• sleep time depends on:
δ
3
4
Transition time
δ
δ
time
6
5
7
Periods
of sleep
Buffer and burst:
Sleep
time
12 3 4
10
5
6 7
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
5ms
2ms
tx @ t=1
t=B+1
t=2B+1
20ms
R1
R2
R3
wake @ t=3
t=B+3
t=2B+3
@ t=8
t=B+8
t=2B+8
@ t=28
t=B+28
t=2B+28
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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
t+5,
I2
t+5+B,…
3ms
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Potential for savings with sleep (optB&B)
• “perfect” coordination not generally possible
t1
1ms
I1
15ms
R1
t1 + 1ms = t2 + 20ms
20ms
t2
I1
2ms
R2
t1 + 15ms = t2 + 2ms
• Upper bound (optB&B): Global search to find ingress
transmission times that maximize network-wide sleep
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Potential benefits of sleeping
Abilene, transition time=1ms, B=10ms
idle (bound)
WoA (pareto)
WoA (CBR)
optB&B(CBR)
Upper bound without
buffering/shaping
Upper bound
for any scheme
Upper bound with
buffering/shaping
A little shaping can get most of the utilization gain
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Practical sleeping algorithm (practB&B)
1.
2.
3.
4.
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
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No coordination (practB&B)
Abilene, transition time=1ms, B=10ms
Practical algorithm realizes most of the benefit
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98th percentile delay (ms)
Impact of sleeping on delay
Abilene, transition time=1ms
No added loss; added delay ~ bounded by Bms
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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 method
2.Sleeping
3.Rate adaptation (slowing down)
4.Sleep vs. Rate adaptation
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3. Rate adaptation states
Model
•
•
•
•
power
N performance states
pi+1
Rates r1, …, rn and pi < pi+1
(100M)
pi
δ : transition period (ms)
Interfaces can rate-adapt independently
Metrics
• Energy savings in average rate reduction
• Performance in loss and max delay
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(1G)
δ
time
Using performance states
• Basic idea: decrease rate as much as possible
without introducing more than than d ms per hop
service rate
Optimal algorithm: ideal
service curve follows shortest
Euclidean distance.
bytes arriving
at router
bytes leaving
router
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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 size
d: target maximum queuing delay
ri : current link operating rate
Leave headroom for
transition time
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)
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Avoid
frequent
changes
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
(#hops)
exponential rates
• Added delay < d *
• No observed packet loss
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Outline
1.Key questions and method
2.Sleeping
3.Rate adaptation (slowing down)
4.Sleep vs. Rate adaptation
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Models of future power profiles
Fraction of power that
doesn’t scale with rate
pactive = C + fn(rate)
Rate scaling function
fn(rate) ~ rate
frequency scaling
fn(rate) ~ rate3
dynamic voltage scaling
pidle = C + β fn(rate)
psleep = μ pidle(rmax)
Idle/Active
Workload Ratio
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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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.
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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.
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Thank you. Questions?
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