identifying and using energy critical paths nedeljko vasić with dejan novaković, satyam shekhar,...
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Identifying and Using Energy Critical Paths
Nedeljko Vasić
with Dejan Novaković, Satyam Shekhar, Prateek
Bhurat, Marco Canini, and Dejan Kostić
EPFL, SwitzerlandNetworked Systems Laboratory
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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)
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Causes of networking energy consumption
• Network redundancy– Achieving high availability
• Bandwidth overprovisioning– Tolerate traffic variations (address lack of QoS)
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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
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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)
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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
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Make all devices energy-proportional?
7
Utilization (%)
Pow
er (%
of p
eak)
Ideal energy-proportionality
Performance penalties
Always-on components
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Dynamically matchresources to the demand make the networkenergy-proportional
Sleeping saves energy
Network-wide energy-proportionality
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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!
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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]
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How often is recomputation needed?
B
C
AD
Tim
e15
min
.
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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)
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• 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
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Can we precompute routing configurations?
B
C
AD
Tim
e15
min
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Can we precompute routing configurations?
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Too many routing configurations
Fraction of time an energy-optimal routing configuration is used (Geant2 trace)
One routing configuration used
60% of the time
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Energy Critical Paths
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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
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REsPoNse (Responsive Energy-Proportional Networks)
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Energy-AwareTraffic Engineering
Runtime Traffic Measurement
[COMSNETS ‘09]
ComputingEnergy-Critical
PathsTraffic Analysis
Online components
Offline components
Ser
vice
-Lev
el O
bjec
tives
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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
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REsPoNse
REsPoNse-lat
REsPoNse-heuristic
REsPoNse-ospf
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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
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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?
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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
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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
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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