Reducing Network Energy Consumption via Sleeping and Rate Adaptation

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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

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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
ATT 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
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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

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Outline

• Key questions and method
• Sleeping
• Rate adaptation (slowing down)
• 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
• 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

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2. Sleeping states

• Model
• Single sleep state with power psleepltlt pidle
• d 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)
(sleep)
psleep
time
d
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When can a link sleep?

• Packets over a link
• sleep time depends on
• Buffer and burst

time
2
3
4
6
1
5
7
Transition time
d
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Making sleep gaps on links with buffer burst
(BB)

• Basic idea use limited buffering at ingress to
  create predictable and useful sleep gaps (gt2d)
  do once, adds bounded delay

5ms
20ms
2ms
R1
R2
R3
_at_ t8 tB8 t2B8
_at_ t28 tB28 t2B28
tx _at_ t1 tB1 t2B1
wake _at_ t3 tB3
t2B3
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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, tB,
I1
8ms
coordinate burst times to align in the network
R
3ms
t5, t5B,
I2
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Potential for savings with sleep (optBB)

• perfect coordination not generally possible

t1
1ms
t1 1ms t2 20ms
I1
R1
15ms
20ms
t2
t1 15ms t2 2ms
I1
R2
2ms

• Upper bound (optBB) Global search to find
  ingress transmission times that maximize
  network-wide sleep

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Potential benefits of sleeping
Abilene, transition time1ms, B10ms
idle (bound) WoA (pareto) WoA (CBR) optBB(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
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Practical sleeping algorithm (practBB)

1. Ingress buffers and transmits packets in a bunch
   every Bms
2. Within bunch, packets are organized by egress
3. Router interfaces wake to process bursts
4. Router interfaces sleep if start of next burst is
   gt2d ms away

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No coordination (practBB)
Abilene, transition time1ms, B10ms
Practical algorithm realizes most of the benefit
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Impact of sleeping on delay
Abilene, transition time1ms
98th percentile delay (ms)
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
Abilene, network utilization5
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Impact of sleep transition time
Quick transitions (preferably lt 1ms) needed
Abilene, network utilization5
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Outline

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

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3. Rate adaptation states

• Model
• N performance states
• Rates r1, , rn and pi lt pi1
• d transition period (ms)
• Interfaces can rate-adapt independently
• Metrics
• Energy savings in average rate reduction
• Performance in loss and max delay

power
(1G)
pi1
(100M)
pi
time
d
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Using performance states

• Basic idea decrease rate as much as possible
  without introducing more than than d ms per hop

• Optimal algorithm ideal service curve
  follows shortest Euclidean distance.

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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
• Rules
• increase to ri1 iff (q/ri gt d) OR (drf q)/ri1gt
  (d- d)
• decrease to ri-1 iff (q 0) AND (rf lt ri-1 )
• duration since last rate change gt k d (k4)

Leave headroom for transition time
Avoid frequent changes
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Benefits of rate adaptation
Abilene, transition time d 1ms, d3ms

• Added delay lt d (hops)
• 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 doesnt scale with rate

• pactive C fn(rate)
• pidle C ß 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
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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 (C0.3 and ß 0.1) and (C0.3
  and ß 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?