Intelligent Placement of Datacenters for Internet Services. Íñigo Goiri, Kien Le, Jordi Guitart, Jordi Torres, and Ricardo Bianchini

Transcription

1 Intelligent Placement of Datacenters for Internet Services Íñigo Goiri, Kien Le, Jordi Guitart, Jordi Torres, and Ricardo Bianchini 1

2 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

3 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

4 Energy costs and carbon emissions Company #Servers Energy/year (MWh) Energy cost/year CO 2 /year (Metric tons) ebay 16K 0.6 x 10 5 $3.7M 0.4 x 10 5 Akamai 40K 1.7 x 10 5 $10M 1.0 x 10 5 Rackspace 50K 2 x 10 5 $12M 1.2 x 10 5 Microsoft >200K >6 x 10 5 >$36M >3.6 x 10 5 Google >500K >6.3 x 10 5 >$38M >3.8 x 10 5 Sources: [Qureshi 09], EPA 4

5 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

6 Outline Motivation Placing datacenters Evaluation Conclusion 6

7 Selecting datacenter locations Model datacenter placement Network latencies Availability 7

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 8

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) 9

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) 10

11 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

12 Goal Formulating the problem 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

13 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

14 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 SA LP Current solution LP Near neighbor 14

15 Our approach for solving the problem SA LP $13.8M/month LP SA $10.3M/month SA LP $9.2M/month LP $10.7M/month 15

16 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

17 Tool demo We built a tool that Embodies the problem Input data for the US Multiple solution approaches Short video at: 17

18 Outline Motivation Placing datacenters Evaluation Conclusion 18

19 Cost (thousand dollars per month) Comparing locations for 60k-server DC Servers Land Building Connection Energy Water Staff Networking Austin Bismarck Los Angeles New York Orlando Seattle St. Louis 19

20 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

21 Cost of 60k-server green DC network Green DC network costs $100k/month more, except when latency <70ms 21

22 Cost (in million dollars) Cost of a 60k-server chiller-less DC network Chiller-less Traditional Maximum latency (milliseconds) Chiller-less DC network is cheaper but it cannot achieve low latencies 22

23 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

24 Intelligent Placement of Datacenters for Internet Services Íñigo Goiri, Kien Le, Jordi Guitart, Jordi Torres, and Ricardo Bianchini 24