This document proposes a framework for intelligently placing datacenters to optimize response time, availability, costs and emissions. It defines relevant parameters like costs, response time, consistency delay and availability. It formulates the placement problem as an optimization problem aiming to minimize costs while meeting constraints. The problem is solved using simulated annealing and linear programming. A tool is developed to automatically select datacenter locations based on this approach. Experimental results demonstrate millions of dollars can be saved through optimized placement.

1. EEDC
34330
Intelligent Placement of
Execution
Datacenters for Internet
Environments for
Services
Distributed
Computing
Master in Computer Architecture,
Networks and Systems - CANS
Homework number: 6
Umit Cavus Buyuksahin
umit.cavus.buyuksahin@ac.upc.edu

2. OUTLINE
1. Introduction
2. Example Datacenter
3. Problem
4. Placement of Datacenters
5. Propose
5.1. Defining Framework
5.2. Formulation
5.3. Solving the problem
6. Conclusion
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3. Introduction

Internet services reach the whole world.

Millions of clients on the world.

Demand high availability
in short response time.

Thus huge datacenters constructed
around the world

They have many servers,
cooling systems, energy power systems..
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4. Example - Datacenter
Facebook - Prineville, Oregon USA
– 147,000-square-foot facility
– $200 million - $215 million.
* http://www.oregonlive.com/business/index.ssf/2010/01/facebook_picks_prineville
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5. Problem

Clients

... widespreaded geographically

... demand high availablity

... in short response time

Many servers requirement.

Supplying Energy

Cooling system

Building and operating datacenters

Green Energy
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6. Problem

Clients

... widespreaded geographically

... demand high availablity

... in short response time

Many servers requirement.

Supplying Energy

Cooling system

Building and operating datacenters

Green Energy

PLACEMENT OF DATACENTER !!
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7. Placement of Datacenter
Direct impact on ...

Response time

High availablity

Mirrored Datacenters

Closest one serves

Capital and Operational Costs

Land acquisition and building

Bring network and electricy

Electricity & Water

Staff

CO2 emmisions (indirect)
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8. OUTLINE
1. Introduction
2. Example Datacenter
3. Problem
4. Placement of Datacenters
5. Propose
5.1. Defining Framework
5.2. Formulation
5.3. Solving the problem
6. Conclusion
8

9. Propose
Datacenter automation of palcement of data centers..

Selection and selection and automation,
efficiently !!
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10. Propose – Defining Framework

Parameters

Costs
•
CAPEX (Capital)
bringing electricity and network
land and construction
power, backup, cooling equipment
•
OPEX (Operational)
maintaince and administor
electrcicity and water price

Response Time
•
Latency & number of servers

Consistency Delay
•
Latency from mirrored datacenters

Availablity
•
#9 changes in each tier

CO2 emissions
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11. Propose – Formulation

Subject to

Minimizing CAPEX and OPEX

Constraints

Response times < MAX LATENCY , ∀ users

Min consistency delay between 2 DCs < MAX DELAY

Min system availability > MIN AVAILABILITY

Output

# of servers at each location

Minimized cost
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12. Propose – Solving

Problem is

... non linear.

... not directly solvable by Linear Programming.

Linear Programming (LP) for potential solution.

Simulated Annealing (SA) for consiring neighborings.

CA + LP for cost optimization.

Quality of results compared with Brute solution.

Tool is built

... automatic dacenter location selection

... new parameters and constraints can be added
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13. Tool
http://www.darklab.rutgers.edu/DCL/dcl.html
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14. Conclusion

No other work for intelligent placement of datacenters.

Contributions:

A framework is proposed by defining parameters

Based on parameters, optimization problem defined

Proposed the most efficient and accurate solution
approach

A tool is built to automate location selection

Experimental results shows

Millions dollar are saved
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