The document discusses a framework for intelligently placing datacenters to minimize costs while meeting performance objectives. It considers factors like location costs, power availability, latency, and environmental impact. The framework models it as an optimization problem. It evaluates solutions like heuristics and simulated annealing and finds heuristics provide good results within a few days. Case studies show tradeoffs between latency, availability, consistency and costs. Intelligently placing datacenters can lower costs significantly.

1. Intelligent placement of
datacenters for Internet Services
Inigo Goiriyz, Kien Lez, Jordi Guitart,
Jordi Torres, and Ricardo Bianchini
Presenter: Zafar Gilani

2. Introduction
• Selection of suitable datacenter locations is
very important.
• Why?
– Running and maintenance costs.
– Network latency.
– Environmental factors (renewable energy vs
carbon-intensive).

3. Important considerations for location
selection
• Proximity to
– population centers,
– power plants, and
– network backbones.
• Source of electricity in the region.
• Electricity, land and water prices.
• Average temperatures of the location.

4. Framework

5. Framework for placement
• Goal:
– Minimize overall cost, while respecting response
time, consistency and availability.
• Objectives:
– Formalize the process as a non-linear cost
optimization problem.
– Automated datacenter location selection process.

6. Framework: Parameters
• Capital costs: investments made upfront.
Type of capital cost Description
Independent of Electricity, external networking.
number of servers
Maximum number of Land acquisition, datacenter construction,
servers power delivery, backup, cooling systems.
Actual number of Purchase of servers, internal networking.
servers

7. Framework: Parameters
• Operational costs: incurred during operation.
Type of operational Description
cost
Actual number of Maintenance of equipment, external
servers bandwidth usage.
Utilization of hosted Electricity and water costs.
servers

8. Framework: Parameters
• Response time.
• Consistency delay.
• Availability.
• CO2 emissions.

9. Framework: Optimization problem
Placement of a Maximum
number of Number of servers that
datacenter at service population center c
locaton d, either servers at
location d. at location d.
1 or 0.

10. Framework: Optimization problem
Placement of a Maximum
number of Number of servers that
datacenter at service population center c
locaton d, either servers at
location d. at location d.
1 or 0.

11. Framework: Solution approaches
• Make it linear.
Remove Sd and Pd,c.
Use linear version of
PBd,c is use of servers at location d to CAP_max.
serve population center c, either 1 or 0.
This is actual number of servers at each
location d.

12. Framework: Solution approaches
• Using Heuristics:
1. Use simple linear program to generate M1
datacenter networks for 1 to D datacenters. We
have M1 * D configurations.
2. Use SBd (placement) and PBd,c (use to meet
demand) to derive pre-set linear program.
3. Select most popular locations and run brute
force.

13. Framework: Solution approaches
• Simulated Annealing:
– For each candidate solution we have values for
each location d and population center c.
– Optimization starts with a configuration and
datacenter at each location.
– Each iteration evaluates a neighboring
configuration.
– Iterate until no more cost reductions observed for
n iterations.

14. Input data and datacenter
characteristics for placement tool

15. Input data
SE BI
SL NY
LA AU
OR

16. Input data

17. Input data

18. Datacenter characteristics
• Datacenter size, cooling and PUEs.
– 8% power delivery losses.
• Connection costs.
– $500K/mile for transmission.
– $480K/mile for fiber optic.
– $1 per Mbps. 1Mbps per server.
• Building costs.
– As a function of maximum power: $15 per watt (small),
$12 per watt (large).
– Availability: 99.827%

19. Datacenter characteristics
• Land cost.
– 6K sq. ft. per MW
• Water cost.
– 24K gallons of water per MW per day.
• Server and internal networking hardware.
– $2K per server.
– $20K per switch.
• Staff costs.
– An admin can manage 1K servers for an average salary
of $100K/year.

20. Results from the tool, a few
characterizations

21. Location characteristics

22. Location characteristics: observations
City PUE/Temp Land/Water Network CO2
cost cost emissions
Austin H L L L
Bismarck L L H H
Los Angeles H H L L
New York H H L L
Orlando H H L L
Seattle L H L L
St. Louis H L H H

23. A case study: placing a datacenter
network

24. Evaluation

25. Evaluating solution approaches
Heuristic was run for 3 days and
then forcefully terminated, results
were extrapolated.
OSA+LP1 is:
•2x faster than Heuristic.
•5x faster than Brute.

26. Datacenter placement tradeoffs:
Latency
2x difference in
price between
desired latency of
33ms and 50ms
$7.8M/month for
latency 70ms or more

27. Datacenter placement tradeoffs:
Cheaper to have 3 Tier
Availability Overall Tier II
II than 2 Tier IV datacenters are the
datacenters. best option.

28. Datacenter placement tradeoffs:
Consistency delay
Consistency delay and
latency are conflicting
goals.
Acceptable ranges for
consistency delay and
latency.

29. Datacenter placement tradeoffs:
Green datacenters
A network of 8
datacenters with 60K
servers produces 8K tons
of CO2/month.
Will cost a lot more
for lower latencies.
With relatively higher latency of
70ms, it will cost $100K/month
more for green energy.

30. Datacenter placement tradeoffs:
chiller-less datacenters
Avoiding chillers can
reduce costs by 8% for
latencies > 70ms.

31. Conclusion

32. In a nutshell
• Intelligent placement of datacenters can save
millions of $/€ .
• Cost of networks of datacenters doubles when
maximum acceptable response time is reduce
from 50ms to 35ms.

33. Intelligent placement of
datacenters for Internet Services
Inigo Goiriyz, Kien Lez, Jordi Guitart,
Jordi Torres, and Ricardo Bianchini
Presenter: Zafar Gilani