Intelligent Placement of Datacenter for Internet Services

Execution

Environments for

Distributed

Computing

Intelligent Placement of

Datacenter for Internet

Services

EEDC

34

33

0

Master in Computer Architecture,

Networks and Systems - CANS

Homework number: 6

by Arinto Murdopo – arinto@gmail.com

2

Problem Statement

Data Center

where? dónde? di mana? oú? waar?

dove? どこですか? πού? 在哪里？어디?

Response time, availability, cost, environmental concerns

3

Proposed Solution

Framework

Solve optimization Problem

Produce tool to compare efficiency and accuracy

Characterization

4

Framework

Minimize Cost

Efficiently select data center locations

Response Time Consistency Availability

5

Solve Optimization Problem

Problem formulation

Approaches: • Simple Linear Programming (LP0) • Pre-set Linear Programming (LP1) • Brute force (Brute) • Heuristic-based on LP (Heuristic) • Simulated Annealing plus LP1 (SA+LP1) • Optimzed SA + LP1 (OSA + LP1)

6

Placement Tool

Available Inputs:

MaxS

1/ratioServerUser

MAXLAT

MAXDELAY

MINAVAIL

area of interest

Granularity

existing data center

7

Placement Tool

Location-dependent data:

Network backbones: latency data from backbone ISP

Power plants, transmission lines, and CO2 emissions: obtained from DOE

Electricity, land, water and temperature: obtained from DOE as well

Missing data are obtained from neighboring location

8

Placement Tool

Datacenter characteristics:

Cooling : CRACs and Water Chillers for cooling

Connection: It costs $500k/mile of transmission line, and $480k/mile of fiber. Amortization of 12 years

Building: Its costs depends of the maximum power

Land: 6 K square feet per Megawatt

9

Placement Tool

Datacenter characteristics:

Water: 24K gallons of water per MW per day

Server: Each server costs $2000 (4 years amortization), each interconnect switch costs $20K (4 years amortization)

Staff: $0.05 per Watt per month. $100K per year salary for 1K servers

10

Characterization

Characterize 7 locations in US

11

Characterization

Evaluate each location with Placement Tools Parameters

12

Characterization

Evaluate each location with Placement Tools Parameters

13

Broadening The Scope

Distribution of cost assuming 500 potential locations

14

Sample Output

Specifications: 1. 60 K servers 2. Latency <= 60 ms 3. Consistency Delay <= 85 ms 4. Minimum Availability = 5 nines

Results Three locations : 1. Seattle(A, 1789 servers) 2. St. Louis (B, 22712 servers) 3. Oklahoma city(C, 5501 servers)

15

Evaluation of Chosen Approach

Based on this specification:

1. 60 K servers

2. Latency <= 60 ms

3. Consistency Delay <= 85 ms

4. Minimum Availability = 5 nines

16

Overall cost table (in million)

17

Evaluation of Chosen Approach

Running Times of Solution Approaches

18

Evaluation of Chosen Approach

Solution Quality

19

Evaluation of Chosen Approach

Recommended approach:

OSA + LP1, since it provides best tradeoff between running time and search quality

20

Exploring Placement Tradeoff

Latency

Latency of 50 ms strikes the best compromise between latency and cost

21

Exploring Placement Tradeoff

Availability

It is usually cheaper to build networks out of less redundant datacenters Tier II data centers are the best option

22

Exploring Placement Tradeoff

Consistency Delay

Low latency and low consistency are conflicting goals

23

Exploring Placement Tradeoff

Green datacenters

Green network is less than $100k more expensive per month than the cost-optimal network when the maximum latency can be relatively high (> 70ms)

24

Exploring Placement Tradeoff

Chiller-less data center

Avoiding chillers reduces costs by 8% for max latencies >= 70ms

25

Conclusions

• Proposed and implemented optimization framework for automatic data center placement for Internet Services

• Characterized US regions • Evaluated solutions based on the framework

26

Questions and answers