1. Reducing Network Energy Consumption via Sleeping and Rate Adaptation

2. 2 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. 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 802.3 az) – Focuses on saving network energy for Ethernet

4. NetworkUtilization 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 1 st JAN, at 12AM Average utilization is low: Opportunity

5. 5 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. 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 ~= p idle T idle + p active T active 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. 7 Outline 1.Key questions and method 2.Sleeping 3.Rate adaptation (slowing down) 4.Sleep vs. Rate adaptation

8. 8 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

9. 9 Model Single sleep state with power p sleep << p idle 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 p idle p sleep δ (sleep) (idle)

10. 10 Packets over a link: sleep time depends on: Buffer and burst: When can a link sleep? time δ Transition time 1 234 5 6 7 Periods of sleep δδ δ δ time 1 234 5 6 7 Sleep

11. 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 wake @ t=3 t=B+3 t=2B+3 2 ms 5 ms 20 ms 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 R2R3

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

13. 13 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 R1 R2 t1 + 1ms = t2 + 20ms t1 + 15ms = t2 + 2ms

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

15. 15 Practical sleeping algorithm (practB&B) 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 >2δ ms away

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

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

18. 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 Abilene, network utilization=5% SchemeUtilization Default41% B = 10ms38% B = 25ms36%

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

20. 20 Outline 1.Key questions and method 2.Sleeping 3.Rate adaptation (slowing down) 4.Sleep vs. Rate adaptation

21. 21 3. Rate adaptation states Model N performance states Rates r 1, …, r n and p i < p i+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 p i+1 pipi δ (1G) (100M)

22. 22 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

23. 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: r f : estimated arrival rate as average (EWMA) of past arrivals q: current queue size d: target maximum queuing delay r i : current link operating rate Rules: 1.increase to r i+1 iff (q/r i > d) OR (δr f +q)/r i+1 > (d- δ) 2.decrease to r i-1 iff (q = 0) AND (r f < r i-1 ) –duration since last rate change > k δ (k=4) Leave headroom for transition time Avoid frequent changes

24. 24 Benefits of rate adaptation Abilene, transition time δ =1 ms, d =3 ms 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. 25 Outline 1.Key questions and method 2.Sleeping 3.Rate adaptation (slowing down) 4.Sleep vs. Rate adaptation

26. 26 Models of future power profiles p active = C + fn(rate) p idle = C + β fn(rate) p sleep = μ p idle (r max ) Fraction of power that doesn’t scale with rate Idle/Active Workload Ratio Rate scaling function fn(rate) ~ rate frequency scaling fn(rate) ~ rate 3 dynamic voltage scaling Power reduction using sleep

27. 27 Sleeping and rate adaptation (DVS-r 3 )

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

29. 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. 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. 31 Thank you. Questions?