Solution Selling in EMEA - UML

Solution Selling in EMEA - UML

Google and Cloud Computing Google Google Agenda The Internet: From Hardware to Community The Innovation: A Computing Cloud

Breakthroughs for Cloud Computing Google Apps for Cloud Computing Google Infrastructure for Cloud Computing The Internet

From Hardware to Community The Internet: From Hardware to Community MySpace Facebook What Do Todays Users Want? Accessibility Access from anywhere and from multiple devices

Shareability Make sharing as easy as creating and saving Freedom Users dont want their data held hostage Simplicity Easy-to-learn, easy-to-use Security Trust that data will not be lost or seen by unwanted parties The Innovation A Computing Cloud

6 Cloud Computing 7 Attributes of Cloud Computing Data stored on the cloud

Software & services on the cloud - Access via web browser Based on standards and protocols - Linux, AJAX, LAMP, etc. Accessible from any device Personal PC Hardware Centric Client Server Software Centric Cloud Computing Service Centric

8 Breakthroughs for Cloud Computing 9 Breakthroughs for Cloud Computing User-Centric 1 Task-Centric

2 Powerful 3 Intelligent 4 5 6

Affordable Programmable 10 User Centric Data stored in the Cloud Data follows you & your devices Data accessible anywhere Data can be shared with others messages preferences news contacts

calendar investments maps photo mailing lists music e-mails phone numbers Example : GMail

San Francisco, Monday Home, Wednesday Beijing, on travel Just a web browser and your account with password! Once you login, the device is yours. Data stored on remote servers in the cloud (with large capacity) Use Google Docs to Solve a Task Task = Teachers creating a departmental curriculum Changes instantly appear to other collaborators

Access your docs from anywhere Chat with others in real time Communication Task Email, Chat, Contacts, Chat History Task: Collaborate on Spreadsheet Communicate Chat with others editing the spreadsheet

Task: Collaborate on Spreadsheet Collaborate Invite others to collaborate on the spreadsheet Task: Collaborate on Spreadsheet Publish Invite others to view the spreadsheet You can also easily organize all your common tasks

Cloud Computing is Powerful: It can do what no PC can do Example: Google Search Is Google Search faster than search in Windows/Outlook/Word? And Google Search must be much harder. How much storage does it take to store all of the web pages? 100B pages * 10K per page = 1000T disk! Cloud computing has at its disposal Essentially infinite amount of disk Essentially infinite amount of computation (Assuming they can be parallelized)

Web Page Search Universal Search W A B C D E 1st Generation: era of single search not diverse 2nd Generation: era of vertical search too complex 3rd Generation: an era of Universal Search From vertical search to universal search

Integration of user experience A B C D E Universal Search Example Universal Search Example Cloud Computing Infrastructure

Masters GFS Master C2 Chunkserver 1 Chunkserver 2 C5 Client

MSN GFS Master 19% Google 48% C1 Yahoo 33% C3 C5 C0 C1

Replicas GFS Architecture Client Client Client Client C0 C5

C2 Chunkserver N Files broken into chunks (typically 64 MB) Master manages metadata Data transfers happen directly between clients/chunkservers 25 Client Client Client Client

Typical Cluster Lock service GFS master Machine 1 User app1 Machine 2 User app1

User app3 User app2 Scheduler slave GFS chunkserver Linux 26

Scheduling masters Scheduler slave GFS chunkserver Linux Machine N User app3

User app2 Scheduler slave GFS chunkserver Linux MapReduce 27

More specifically Programmer specifies two primary methods: map(k, v) * reduce(k', *) * All v' with same k' are reduced together, in order. Usually also specify: partition(k, total partitions) -> partition for k often a simple hash of the key allows reduce operations for different k to be parallelized 28

BigTable Distributed multi-level map With an interesting data model Fault-tolerant, persistent Scalable Thousands of servers Terabytes of in-memory data Petabyte of disk-based data Millions of reads/writes per second, efficient scans Self-managing

Servers can be added/removed dynamically Servers adjust to load imbalance 29 BigTable: Basic Data Model Distributed multi-dimensional sparse map (row, column, timestamp) cell contents COLUMNS contents ROWS

t1 t2 t3 TIMESTAMPS Good match for most of our applications 30 BigTable: System Architecture

Bigtable cell Bigtable master performs metadata ops, load balancing Bigtable client Bigtable client library Open() Bigtable tablet server Bigtable tablet server

Bigtable tablet server serves data serves data serves data Cluster Scheduling Master GFS Lock service

handles failover, monitoring holds tablet data, logs holds metadata, handles master-election Thanks Q&A

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