Identifying and Using Energy Critical Paths

Nedeljko Vasić

with Dejan Novaković, Satyam Shekhar, Prateek

Bhurat, Marco Canini, and Dejan Kostić

EPFL, SwitzerlandNetworked Systems Laboratory

2

NETWORKDatacenter

DATACENTERNETWORK

20% of total serverenergy consumption(3 TWh in US in 2006)

Networking Energy

Several TWh/year for major telcos (Telefonica 4.5 TWh,Verizon 9.9 TWh)

Causes of networking energy consumption

• Network redundancy– Achieving high availability

• Bandwidth overprovisioning– Tolerate traffic variations (address lack of QoS)

3

Typical utilization

levels

Energy-(un)proportionality

4

0

20

40

60

80

100

0 20 40 60 80 100

Pow

er (%

of p

eak)

Utilization (%)

Ideal energy-proportionality

Existing networking hardware

Networking energy outlook

• More demands will result in further increases– Video streaming, Cloud computing

• CMOS reaching a plateau in power-efficiency – Cooling costs of new equipment will increase

• 1 MW for latest Cisco platform, CRS-1

5

Rate of traffic increase > rate in which underlying technologies improve their energy efficiency

Power is not limitless (60 Amps per rack)

Goal: Energy-Proportional Networked Systems

6

0

20

40

60

80

100

0 20 40 60 80 100

Utilization (%)

Goal

Pow

er (%

of p

eak)

Ideal energy-proportionality

Make all devices energy-proportional?

7

Utilization (%)

Pow

er (%

of p

eak)

Ideal energy-proportionality

Performance penalties

Always-on components

8

Dynamically matchresources to the demand make the networkenergy-proportional

Sleeping saves energy

Network-wide energy-proportionality

Routing table computation

• Goal: The minimal set of network elements (w.r.t. power) that will satisfy the current demands

• Routing that minimizes energy consumption– Multi-commodity flow problem, but with additional

constraints for energy objective:Links + routers (switches) powered on/stand by

– Problem is computationally intensive– Heuristics take several minutes for small topologies

When traffic demand changes, optimal routing changes!

9

How often is recomputation needed?

10

0

1

2

3

4

5

6

May-28 Jun-1 Jun-5 Jun-9

Dem

and

[Gbp

s]

Time

traffic demands

[Geant2 - European academic network, 15-day trace]

11

How often is recomputation needed?

B

C

AD

Tim

e15

min

.

How often is recomputation needed?

12

0

1

2

3

4

5

6

May-28 Jun-1 Jun-5 Jun-9

Dem

and

[Gbp

s]

Time

traffic demands

Routing table recomputed 3-4 times per hour!(state-of-the-art)

[Geant2 - European academic network, 15-day trace]

0

1

2

3

4

5

May-28 Jun-1 Jun-5 Jun-9

Rec

ompu

tatio

n ra

te [p

er h

our]

Time

trace granularity (upper bound)

13

• Long computation time might lead to energy waste or congestion

• Adjusting routing upon each change in traffic patterns often leads to oscillations

• Network operators would like to base their designs on longer time scales

Issues with recomputation

14

Can we precompute routing configurations?

B

C

AD

Tim

e15

min

Can we precompute routing configurations?

15

Too many routing configurations

Fraction of time an energy-optimal routing configuration is used (Geant2 trace)

One routing configuration used

60% of the time

Energy Critical Paths

16

1 2 3 4 50

20

40

60

80

100

120

GeantFatTree

Number of alternative paths

CDF

of O

ptim

al p

aths

in

clud

ed

Just a few precomputed paths offer near-optimal energy savings

REsPoNse (Responsive Energy-Proportional Networks)

17

Energy-AwareTraffic Engineering

Runtime Traffic Measurement

[COMSNETS ‘09]

ComputingEnergy-Critical

PathsTraffic Analysis

Online components

Offline components

Ser

vice

-Lev

el O

bjec

tives

REsPoNse

Always-on paths provide a routing that can carry low to medium amounts of traffic at the lowest energy consumption

On-demand paths start carrying traffic when the load is beyond the capacity offered by the always-on paths

Failover paths are designed tominimize the impact of single failures

18

REsPoNse

REsPoNse-lat

REsPoNse-heuristic

REsPoNse-ospf

19

REsPoNseTE in action

DestSrcA B

C D

• Online effort to shift traffic to (in)activate on-demand paths• Intermediate routers mark packets with link load • Edge routers collect load info only on alternative paths

Load(AO) > Tr

TE

Evaluation Questions

• How energy-proportional is REsPoNse?– ISP topologies– Datacenter networks

• How quick is REsPoNseTE in shifting traffic?• What is the impact of traffic aggregation on

application performance?

20

Responsiveness/Energy-Proportionality (Geant)

21

• Replayed a 15-day trace• 2 power models

– Today– Future (static power is

significantly reduced) 0

20

40

60

80

100

120

May-28 Jun-1 Jun-5 Jun-9

Pow

er [%

orig

inal

net

wor

k po

wer

]

Time

ospfREsPoNse

REsPoNse (Alternative HW model)

0

1

2

3

4

5

6

May-28 Jun-1 Jun-5 Jun-9

Dem

and [G

bps]

Time

traffic demands

REsPoNse saves 30% - 45% with adding only 1 carefully precomputed routing table

P

ower

(% o

f orig

inal

)

100

80

60

40

20

Dem

ands

(GBp

s)

5

4

3

2

1

Impact on application performance

80

85

90

95

100

105

epr-lat50 InvCap50 epr-lat100 InvCap100

Per

cent

age

(%)

22

0

0.5

1

1.5

2

epr-lat50 InvCap50

Tim

e (s

)

Application performance and e2e latency under REsPoNse is comparable to OSPF at both low and high utilization levels.

Live Modelnet experiment with P2P-VoD (BulletMedia [IPTV ‘07])

REsPoNse OSPF REsPoNse OSPF low utilization high utilization

REsPoNse OSPF low utilization

P

erce

ntag

e (%

) 100

95

90

85

80

Bl

ock

retr.

late

ncy

(s)

1.5

1

0.5

0

23

Conclusion

• REsPoNse• Key idea: hybrid offline/online approach

based on existence of energy critical paths• Properties

• Stable• Incrementally deployable• Responsive

REsPoNse enables power/cooling for the common case