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Intelligent Placement of Datacenters for Internet Services
Íñigo Goiri, Kien Le, Jordi Guitart,Jordi Torres, and Ricardo Bianchini
1
Motivation
• Internet services require thousands of servers• Use multiple “mirror” datacenters
– High availability and fault tolerance– Low response time
• Spend millions building and operating datacenters• Consume enormous amounts of brown energy
2
Datacenter construction costs
• Each datacenter costs >$100M to construct– The smaller datacenters are rated at ~25MW
• Examples:– Microsoft DCs in Virginia & Chicago: $500M each
3
Energy costs and carbon emissions
Company #Servers Energy/year (MWh)
Energy cost/year
CO2/year (Metric tons)
eBay 16K 0.6 x 105 $3.7M 0.4 x 105
Akamai 40K 1.7 x 105 $10M 1.0 x 105
Rackspace 50K 2 x 105 $12M 1.2 x 105
Microsoft >200K >6 x 105 >$36M >3.6 x 105
Google >500K >6.3 x 105 >$38M >3.8 x 105
Sources: [Qureshi’09], EPA
4
Intelligent Placement of Datacenters
Goal: Manage the monetary and environmental costs
• Define framework• Model costs and datacenter characteristics• Define optimization problem• Create solution approaches
• Collect cost and location-related data• Create placement tool
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Outline
• Motivation• Placing datacenters• Evaluation• Conclusion
6
Selecting datacenter locations
• Model datacenter placement– Network latencies– Availability
7
Selecting datacenter locations
• Model datacenter placement– Network latencies– Availability
• CAPEX costs– Distance to electricity and networking infrastructure– Land and construction (maximum PUE)– Power delivery, cooling, backup equipment– Servers and networking equipment
8
Selecting datacenter locations
• Model datacenter placement– Network latencies– Availability
• CAPEX costs– Distance to electricity and networking infrastructure– Land and construction (maximum PUE)– Power delivery, cooling, backup equipment– Servers and networking equipment
• OPEX costs– Maintenance and administration– Electricity and water prices (average PUE)
9
Selecting datacenter locations
• Model datacenter placement– Network latencies– Availability
• CAPEX costs– Distance to electricity and networking infrastructure– Land and construction (maximum PUE)– Power delivery, cooling, backup equipment– Servers and networking equipment
• OPEX costs– Maintenance and administration– Electricity and water prices (average PUE)
• Incentives (taxes)
10
Selecting datacenter locations
• Model datacenter placement– Network latencies– Availability
• CAPEX costs– Distance to electricity and networking infrastructure– Land and construction (maximum PUE)– Power delivery, cooling, backup equipment– Servers and networking equipment
• OPEX costs– Maintenance and administration– Electricity and water prices (average PUE)
• Incentives (taxes)
11
Formulating the problem• Goal
– Minimize CAPEX and OPEX
• Constraints– Response times < MAX LATENCY for all users– Min consistency delay between 2 DCs < MAX DELAY– Min system availability > MIN AVAILABILITY
• Output– Number of servers at each location– Minimum cost
12
Solving the (non-linear) problem
• Linear Programming– Does not support non-linear costs
• Brute force– Too slow
• Simple heuristics– May not produce accurate results efficiently
13
Our approach for solving the problem
• Evaluate each potential solution– Quickly via Linear Programming (LP)
• Consider neighboring configurations– Simulated annealing (SA)
• Cost optimization process– Combine SA and LP
14Current solution Near neighbor
LP
SA
LP
Our approach for solving the problem
15
LP
SA
LP
LP
SA
LP
SA
$13.8M/month
$9.2M/month $10.7M/month
$10.3M/month
Summary of our approach
• Generate a grid of tentative locations• Collect data about each location• Define datacenter characteristics• Instantiate optimization problem• Solve optimization problem
16
Tool demo
• We built a tool that– Embodies the problem– Input data for the US– Multiple solution approaches
Short video at:http://www.darklab.rutgers.edu/DCL/dcl.html
17
Outline
• Motivation• Placing datacenters• Evaluation• Conclusion
18
Comparing locations for60k-server DC
0100020003000400050006000700080009000
Austin Bismarck Los Angeles
New York Orlando Seattle St. LouisCost
(tho
usan
d do
llars
per
mon
th)
Servers Land Building Connection Energy Water Staff Networking
19
Interesting questions
• How much does…… lower latency cost?… higher availability cost?… faster consistency cost?… a green DC network cost?… a chiller-less DC network cost?
20
Cost of 60k-servergreen DC network
21Green DC network costs $100k/month more, except when latency <70ms
Cost of a 60k-serverchiller-less DC network
0
2
4
6
8
10
12
14
30 50 70 90 110
Cost
(in
mill
ion
dolla
rs)
Maximum latency (milliseconds)
Chiller-less
Traditional
22Chiller-less DC network is cheaper but it cannot achieve low latencies
Conclusions
• First scientific work on smart datacenter placement– Proposed framework and optimization problem– Proposed solution approach– Characterized many locations across the US– Built a tool to automate the process– Answered many interesting questions
• Results show that smart placement can save millions• Work enables smaller companies to reap the benefits
23
Intelligent Placement of Datacenters for Internet Services
Íñigo Goiri, Kien Le, Jordi Guitart,Jordi Torres, and Ricardo Bianchini
24
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