EEDC                          34330Execution                          Intelligent placement ofEnvironments for            ...
IntroductionPopular Internet companies offer services to millions of users everyday.These services are hosted in geographi...
Introduction               Austin               PUE: 1.39               Land: 0.394 $/SF               Energy: 0.066 kWh  ...
Introduction               Bismark               PUE: 1.20               Land: 0.434 $/SF               Energy: 0.062 kWh ...
Introduction      Los Angeles      PUE: 1.41      Land: 0.638 $/SF      Energy: 0.099 kWh      Water: 0.33 cents/gal      ...
Introduction               New York               PUE: 1.29               Land: 3.460 $/SF               Energy: 0.096 kWh...
Introduction               Orlando               PUE: 1.42               Land: 0.272 $/SF               Energy: 0.081 kWh ...
Introduction      Seattle      PUE: 1.19      Land: 0.987 $/SF      Energy: 0.041 kWh      Water: 0.65 cents/gal      CO2:...
Introduction         St. Louis         PUE: 1.32         Land: 0.264 $/SF         Energy: 0.047 kWh         Water: 0.21 ce...
Framework for placement - ParametersCost Capital Expenses (CAPEX): investments made upfront and  depreciated over the lif...
Framework for placement - ParametersCost Operational Expenses (OPEX): costs incurred during the operation of  the datacen...
Framework for placement - ParametersResponse Time: Latency between a population center and a location.    – Latency(c, d):...
Framework for placement – FormulationInputs:   –   Maximum number of servers   –   Expected average utilization for the se...
Framework for placement – FormulationOutputs:   – Optimal cost   – Maximum number of servers at each location   – Number o...
Framework for placement – Solutions Simple linear programming (LP0)   – Simplifies the equation to check if a datacenter ...
Framework for placement – Solutions Heuristic Based on LP (Heuristics)   – Generates 10 possible datacenter networks for ...
Placement tool User only specifies:   –   Area of interest   –   Granularity of the potentials datacenters   –   Location...
Placement tool                 18
Placement tool           60k servers           Latency <60ms           Delay <=85 ms           Availability >= 0.99999   3...
Exploring datacenter placement tradeoffs Latency   – Latencies > 70 ms have the same cost   – Latency = 50 ms is the best...
Exploring datacenter placement tradeoffs Consistency delay   – Low consistency delays and low latency are conflicting goa...
Questions            22
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Intelligent Datacenter placement

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Intelligent Placement of datacenters for internet Services

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  • The area of interest is fit into an n x n grid (n depends on granularity). Those tiles inside the area of interest and where a datacenter can be build form the possible location. It also takes the main population centers within this area. Using geolocation services, we can instantiate parameters like distance between to datacenters or the population of a center. The set of users is assumed to be a fraction of these populations. Other location-dependent data obtained with Internet services can be: The topology of the ISP backbones and obtain the latencies between to points or the closest Network for a datacenter. The power plants, transmission lines and C O2 emissions and get the closestPower and the emissions of a datacenter Electricity, land, water and temperature If some data is missing then it takes them for the neighboring locations. Taking into account all this information, the toolkit can compute or assume the rest of the parameters it requires: the PUE of a datacenter in that location, the cost to connect into the power supply or to an ISP backbone, the building, land and water costs; servers and internal networking purchasing and operational expenses and staff compensations.
  • Intelligent Datacenter placement

    1. 1. EEDC 34330Execution Intelligent placement ofEnvironments for datacenters for InternetDistributed ServicesComputingMaster in Computer Architecture,Networks and Systems - CANS Homework number: 6 Group number: EEDC-32 Francesc Lordan francesc.lordan@gmail.com
    2. 2. IntroductionPopular Internet companies offer services to millions of users everyday.These services are hosted in geographically distributed datacenters.No public information about how they select the locations 2
    3. 3. Introduction Austin PUE: 1.39 Land: 0.394 $/SF Energy: 0.066 kWh Water: 0.40 cents/gal CO2: 569 g/kWh 3
    4. 4. Introduction Bismark PUE: 1.20 Land: 0.434 $/SF Energy: 0.062 kWh Water: 0.32 cents/gal CO2: 869 g/kWh 4
    5. 5. Introduction Los Angeles PUE: 1.41 Land: 0.638 $/SF Energy: 0.099 kWh Water: 0.33 cents/gal CO2: 286 g/kWh 5
    6. 6. Introduction New York PUE: 1.29 Land: 3.460 $/SF Energy: 0.096 kWh Water: 0.35 cents/gal CO2: 960 g/kWh 6
    7. 7. Introduction Orlando PUE: 1.42 Land: 0.272 $/SF Energy: 0.081 kWh Water: 0.23 cents/gal CO2: 541 g/kWh 7
    8. 8. Introduction Seattle PUE: 1.19 Land: 0.987 $/SF Energy: 0.041 kWh Water: 0.65 cents/gal CO2: 120 g/kWh 8
    9. 9. Introduction St. Louis PUE: 1.32 Land: 0.264 $/SF Energy: 0.047 kWh Water: 0.21 cents/gal CO2: 806 g/kWh 9
    10. 10. Framework for placement - ParametersCost Capital Expenses (CAPEX): investments made upfront and depreciated over the lifetime of the datacenter – CAP_ind: independent of the number of servers. • Bringing the electricity and external networking. – CAP_max: maximum number of servers that can be hosted • Land adquisition • Datacenter construction • Purchasing and installing power delivery infrastructure • Cooling infrastructure • Backup infrastructure – CAP_act: purchasing the servers and internal networking gear 10
    11. 11. Framework for placement - ParametersCost Operational Expenses (OPEX): costs incurred during the operation of the datacenters – OP_act: maintenance and administration of the equipment and external networking bandwith. • Domined by the staff compensation. – OP_utl: electricity and water costs involved in running the servers Lower taxes and incentives 11
    12. 12. Framework for placement - ParametersResponse Time: Latency between a population center and a location. – Latency(c, d): latency between a location d and a center c. – Pcd: Number of servers at a location d that serve request from c – Servers(c): Number of servers required by the center cConsistency Delay: time required for state changes to reach all mirrors – Latency (d1, d2): one-way latency between the locations d1 and d2.Availability: depends on the network avalability of all the datacentersCO2 emissions: determined by the type of electricity consumed – Emissions(d): carbon emissions (g/Kwh) at location d. 12
    13. 13. Framework for placement – FormulationInputs: – Maximum number of servers – Expected average utilization for the servers – Number of user that each server can accomodate – Amount of redundancy – Latencies and availability constraints – CAPEX and OPEX for each location – Latencies between any population center and each location – Latencies between any two locations 13
    14. 14. Framework for placement – FormulationOutputs: – Optimal cost – Maximum number of servers at each location – Number of servers that service a population center at a location 14
    15. 15. Framework for placement – Solutions Simple linear programming (LP0) – Simplifies the equation to check if a datacenter must be placed at a location and which centers it provides. Proportionally assigns the max number of servers and computes the network costs with the original one Pre-set linear programming (LP1) – Presets if a location contains a datacenter and its size and removes the centers which are provided variable. Bruteforce (Brute) – Generates all the possibilities and tests them using the LP1 approach 15
    16. 16. Framework for placement – Solutions Heuristic Based on LP (Heuristics) – Generates 10 possible datacenter networks for each number of datacenters using LP0 applies the LP1 algorithm and sorts the results in increasing order of cost and finally runs the bruteforce method on a small set of solutions to obtain the most efficient. Simualted Annealing plus LP1(SA+LP1) – SA starts with a configuration that fulfills the constraints and evaluates the neighbors obtained using LP1. The solution is selected when there is no cost improvement within an iteration interval. Optimized SA+LP1(OSA+LP1) – Adjusts the results of the LP1: when no servers are assigned to a datacenter, it is removed. 16
    17. 17. Placement tool User only specifies: – Area of interest – Granularity of the potentials datacenters – Location of existing datacenters – Max number of Servers – Ratio of user per server – Max latency between – Max delay – Min availability The toolkit obtains the missing data to compute the best datacenter network in order to fulfill the user constraints. 17
    18. 18. Placement tool 18
    19. 19. Placement tool 60k servers Latency <60ms Delay <=85 ms Availability >= 0.99999 31789 22712 5501 19
    20. 20. Exploring datacenter placement tradeoffs Latency – Latencies > 70 ms have the same cost – Latency = 50 ms is the best tradeoff between latency and cost – Latencies < 35 doubles the cost of 50 ms Availability – Less level Tier datacenters  more datacenters – It’s cheaper to achive an avaiability level with more low-level Tier datacenters than with less high-level datacenters. – TierII datacenters are the best option 20
    21. 21. Exploring datacenter placement tradeoffs Consistency delay – Low consistency delays and low latency are conflicting goals – Low consistency delays implies less datacenters and lower costs Green Datacenters – When latencies can be relatively high, a green datacenter is less expensive than $100K a month. Chiller-less datacenters – Water chillers increases energy consumption by 20% and building costs by 30%. Necessary for locations with an average temperature over 20ºC. – Avoiding chillers is feasable when latencies are over 70 ms. It reduces costs by an 8%. 21
    22. 22. Questions 22

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