umit hw6
TRANSCRIPT
Execution Environments for Distributed Computing
Intelligent Placement of Datacenters for Internet
Services
EEDC
343
30
Master in Computer Architecture, Networks and Systems - CANS
Homework number: 6Umit Cavus Buyuksahin
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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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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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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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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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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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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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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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Propose
Selection and automation of palcement of data centers.. Datacenter selection and automation, efficiently !!
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Propose – Defining Framework
Parameters Costs
• CAPEX (Capital)bringing electricity and networkland and constructionpower, backup, cooling equipment• OPEX (Operational)maintaince and administorelectrcicity 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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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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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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Tool
http://www.darklab.rutgers.edu/DCL/dcl.html
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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