Google data centers

Data Centers Architecture Presented By Ali Al Ogaili

About Google Google Products

Background Distributed Computing Layered Architecture & Abstraction

Google Architecture Computing Infrastructure Software Infrastructure

App Engine: Google platform for your Enterprise

A genda

Google Mission: to organize the world’s information and make it universally accessible and useful

Established in (September 4, 1998)

Today Google runs over one million servers in centersaround the world

Processes over one billion search requests[

and twenty petabyte (1015 B) of user-generated data every day

G oogle at glance

Some of the products Google provide Google Search Gmail Maps YouTube GoogleDoc Google Calendar App Engine And many more

Most of their products are web based They serve millions of people and they store user’s data in the

“cloud” How do they do that? What is under the hood?

G oogle products


Background Distributed Parallel Computing Layered Architecture & Abstraction



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One “smart” computer doing the task of summing up the cells of the arrays sequentially.

B ackground: (Distributed Parallel Computing)

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Compute

Five “dummy” distributed computers doing the same task in parallel.

B ackground: (Distributed Parallel Computing)

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Compute

Worke

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Distribute computation power and memory

Division of concern

Structure the system in layers, such as that each layer has a set of problems, tasks and processes decoupled from the other layers.

Abstraction

Each layer abstract a set of functions and concerns to the layer above it

Flexibility

Replace an implementation while maintainingthe interface

B ackground: (Layers & Abstraction)

The trouble with layers of computer software is that sooner or later you loose touch with reality.





A genda

A rchitecture (General Overview)

Computing Platform:- Cost Efficiency- Server Design- Networking - Datacenters Technologies

System Infrastructure:-Google File System (GFS) -MapReduce-BigTable

Google Services Computing PlatformClusters of thousands of commodity-class PC

-Reliable (fault tolerance)-Scalable -Cost Efficient (Low end servers)

System Infrastructure:A layer of software that abstracts the hardware complexity from the developers, it provides features such as:-Scheduling-File access-Fault management-And many more

Google Services:The set of services provided for the users:

-Usability/User friendliness-Simplicity-Performance-Innovation & solving people’s problems

Google Datacenters evolved over time…

Google.standford.edu (circa 1997)

Eric & Sergey (google founders) volunteered to receive a shipments of machines other research groups order, and hold on them for sometime.

C ompuing Platform

C ompuing Platform

Google Datacenters evolved over time…

C ompuing Platform

Google’s software architecture arises from two basic insights *:

o Reliability in software rather than in server-class hardware (thus they can commodity PC)

o Tailor the design for best aggregate request throughput, not peak server response time (manage request time by parallelizing individual request)

* WEB SEARCH FOR A PLANET:THE GOOGLE CLUSTER ARCHITECTURE by Luiz André Barroso , Jeffrey Dean & Urs Hölzle

C ompuing Platform

Dual SATA Disks

RAM

12VDC Sealed Lead-Acid Battery

Dual CPUs

Power Supply

Google custom made servers uses consumers products to get the best economical value per performance..

C ompuing Platform The servers are placed in racks in

a shipment container (Modular design)

Plug & play (or serve) The servers interconnect via a

100-Mbps Ethernet switch that has one or two gigabit uplinks to a core gigabit switch that connects all racks together.

Each shipping container can hold up to 1,160 servers

“power above, water below,” Modular design The Google facility features

a “container hanger” filled with 45 containers,

C ompuing Platform

Some key challenges with Datacenter design:

Powering:

(Google has a backup battery for each server as a oppose to a centralized UPS)

Cooling

(Low tech PC generates more heat, thus the datacenter requires more aggressive cooling)

Cabling and modularity

(Low tech pc are more prone to failure and their life span is shorter; thus, those machines need to be replaced easily)

And much more..

C ompuing Platform

What could go wrong? Many things*..

Overheating (power down most machines)

PDU failure (machines suddenly disappear)

Rack-move (plenty of warnings) Rack-failures (40-80 machines

instantly disappear) Racks go wonky (40-80 machines see

50% pack loss) Network maintenance ( ~ 30 min

random connectivity loss) Individual machine failures Thousands of hard drive failures And much more (slow disk, bad

memory, miss configured machine, etc..)

Thousands of low end machines clustered together is maintenance nightmare !

*Google Seattle Conference on Scalability

C ompuing Platform Google datacenters are more a single upgradable machine

Warehouse Scale Machines– (WSM).

C ompuing Platform “Cloud” computing or back to mainframe computing?

1960s mainframe machines serving thin clients

2005 Google datacenters hosting web applications and serving thin clients

S oftware Platform

A software layer on top of computing platform

If one thinks of Google Datacenter as one single machine (WSM) composted of thousands of individual machines, then the software platforms managing those machines could be thought of as an operating system for this machine

Some of the main custom tools created by Google Google File Systems (GFS) MapReduce BigTable

S oftware Platform (GFS)

Google File System (GFS)

It is designed to provide efficient, reliable access to data using large clusters of commodity hardware. (from Wikipedia)

Abstract the storage on distributed unreliable hardware Master machines that deals with Metadata(Filename, mapping

from filename to chuck locations) 64MB chunks (on the disk 8K file system block on the Operating

System) Every chunk is replicated 3 times on different racks Responsible for managing failures (if machine dies,

then replicate the data in another machine)

S oftware Platform (MapRecude)

MapReduce

Introduced by Google to support distributed computing on large data sets on clusters of computers. (from Wikipedia)

Abstract the computation on distributed unreliable hardware User has to write to functions (Map & Redeuce) and the library

will take care of all the hardware related issues (Assigning tasks to machines, managing machines failures etc)

The library will try to make the computation faster by pushing the logic closer to where the chunk data is located

Deals with scalability

S oftware Platform (MapRecude)

Split the data set into N (mapping) where N is equal to the number of available workers

Wait until all the workers finish their tasks (some processing is done on intermediate results)

Computer the final result (reduce) functions

S oftware Platform (BigTable)

BigTable

A compressed, high performance, and proprietary database system built on Google File System (GFS), Chubby Lock Service, and a few other Google programs (from Wikipedia)

Non-relational distributed database created by Google Built on top of GFS and provides a higher level of abstraction Implements a sub-set of typical DBMS (Database management system) Google Analytics, Google Earth, Personalized Search, App Engine and

many more..





A genda

A pp Engine

* From http://code.google.com/appengine

Google data centers

Technology

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