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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