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
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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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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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