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Power Comparison Power Comparison of Cloud Data of Cloud Data Center Architectures
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Transcript of Power Comparison Power Comparison of Cloud Data of Cloud Data Center Architectures
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Power Comparison of Cloud Data Center Architectures
Pietro Ruiu
Andrea Bianco, Paolo Giaccone
Claudio Fiandrino, Dzmitry Kliazovich
13th Italian Networking Workshop: San Candido, Italy January 13 - 15, 2016
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The quest for green data centers• Data centers are the new polluters of 21st
century– in 2012, accounted for 15% of the global ICT
energy consumption– expected to increase in the next years
• Data center consumption– 75% ICT equipment
• powering and cooling• mostly due to servers
– 25% power distribution and facility operations• Strong interest in designing and operating data
centers with higher energy efficiency– not only to reduce OPEX costs
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Key question
• Given• a data center topology• a power consumption profile for each ICT device
• Define the min-power job allocation policy• Evaluate power consumption as function of the data center
load
Classical questions
• Given• a power consumption profile for each ICT device• a generic job allocation policy
• Compare the power consumption behavior in function of the data center topology
Our question
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Data center model
Data center network (DCN)
Pow
er
Load
Pow
er
Load
Pow
er
Load
Servers
ICT device Power profile
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Local vs global energy proportionality
• consume proportional to the load• consume (and pay) only if really needed
Ideal energy proportionality
• Constant power (CONST)• Full Energy Proportional (FEP)• Linear (LIN)
Local power consumption for single device
• maybe very different from local power consumption
Global power consumption for overall system
CONSTPo
wer
Load
FEP
Pow
er
Load
LIN
Pow
er
Load
Pow
er
Load
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Global power consumption
• N resources/devices to be allocated for a set of requests/VMs• power consumption profile for each resource/device
• allocation policy• consolidate: activate the minimum number of resources• load-balance: distribute load across the resources
Resource allocation
• depends on granularity of the resources (i.e. the value of N)• depends on allocation policy
Overall power consumption
1 2 N
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Global Power ConsumptionLocal power
consumption Consolidate policy Load-balance policy
Normalized power = Power / Load
FEP
Pow
er
Load
CONST
Pow
er
Load Load
Pow
er
• Local energy proportionality implies global energy proportionality• If N is enough large, consolidate policy reaches global energy
proportionality for CONST local power
Local and Global Energy Proportionality
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Our contributions
• Network-aware min-power VM online allocation policy
• Flow-level C++ simulator of data centers • Power comparison of different network
topologies
• Energy profiles for each ICT device (switch, link, server)
• DCN topology• VM arrival process
Flow-level simulator
• Global power consumption
• Load on each ICT device
• VM blocking probabilityVM allocation
policy
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DCN topologies
• traditional Clos-based switch topologies– classical 2, 3 tiers– Jupiter• Google’s disclosed DCN architecture • “Jupiter Rising: A Decade of Clos Topologies and Centralized Control in
Google’s Datacenter Network”, ACM SIGCOMM CCR, Oct. 2015
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• Switches: 10 core sw – 18 TOR sw
• Servers 180
• Total ICT devices: 208 nodes
CORE
TOR
10Gb
ps40
Gbps
2-tiers DCN
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• 3 core sw – 6 aggregation sw – 18 TOR sw
• 180 servers – 27 switches – 207 nodes
CORE
AGGREGATION
TOR
10Gb
ps40
Gbps
40Gb
ps
3-tiers DCN
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• 24 spine sw – 16 aggregation sw – 16 TOR sw
• 192 servers – 44 switches – 236 nodes
= 4p@40Gbps (or 16p@10Gbps)
SPINE
AGGREGATION
TOR
10Gb
ps10
Gbps
40Gb
ps40
Gbps
40Gb
ps
MB MB MB MB
Jupiter-like DCN
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Online VM allocation policy
• for each VM, select a server at random• connect through the minimum incremental DCN
power• load-balance on the servers
RSS (Random Server Selection)
• for each VM, select the server with minimum incremental power (server + DCN)
• consolidate VMs in the same server, in the same rack, in closeby racks, etc
• variant of min-cost Dijkstra algorithm
MNP (Minimum Network Power)
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VM generation
• time is slotted• at each timelot, a new VM arrives and must
communicate B bps to a previously randomly allocated VM – B is randomly chosen– destination VM is chosen with Bernoulli trials
• simulation can run until saturating the data center
VM1 VM2 VM3 VM4 VM5
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RSS (Random Server Selection)• small datacenter (180-192 servers)• Jupiter appears to be the most energy proportional
– due to the larger number of switches (44 vs 27-28)
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MNP (Minimum Network Power)• small datacenter (180-192 servers)• MNP allows to achieve global energy-proportionality• under FEP, power jumps due to abrupt activation of new layers
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Conclusions
• Global energy proportionality of an overall data center depends on• local power profile of each device• topology (number of devices)• VM allocation policy
Take-home message
• consider large topologies with 10,000 servers• compare data center networks given the same bisection
bandwidth• consider the allocation of clusters of VMs
Future works