intelligent placement of datacenters for internet services Íñigo goiri, kien le, jordi guitart,...

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

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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

5

Outline

• Motivation• Placing datacenters• Evaluation• Conclusion

6

Selecting datacenter locations

• Model datacenter placement– Network latencies– Availability

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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

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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

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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

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Intelligent Placement of Datacenters for Internet Services

Íñigo Goiri, Kien Le, Jordi Guitart,Jordi Torres, and Ricardo Bianchini

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