cloud computing alcances e implementacion

Implementacion de Cloud Computing:
Alcances y Tecnologia
Lic. Jorge Guerra Guerra
Universidad Nacional Mayor de San Marcos

XVII Congreso Nacional de Estudiantes de Ingeniería de
Sistemas y Computación
6 Agosto 2010
http://sites.google.com/site/jguerra91/home/
/

Agenda

• Definiciones
• Taxonomía
• Costos
• Implementaciones

Lic. Jorge Guerra 2

“No es nada nuevo”
“... hemos redefinido launa trampa”
“Es
computación en nube para
“Es la peor estupidez: es una
incluir todo bola del marketing. Alguien está
lo que ya hacemos
... No entiendo que podriamos
diciendo que es inevitable-y cada
Que es cloud computing?
de otra maneraque oigo eso, es muy
vez ... que no sea
cambiar la probable que algunoscampaña de
redacción de sea un
de nuestros anuncios.” hacerlo realidad.”
negocios para
Larry Ellison, CEO, Stallman, Founder, Free
Richard Oracle (Wall
Street Journal, Sept.Foundation (The
Software 26, 2008)
Guardian, Sept. 29, 2008)
No hay una respuesta consistente…

Todo el mundo tiene un montón de
datos para procesar!
• Wayback Machine tiene 2 PB + 20 TB/mes (2006)
• Google procesa 20 PB por dia (2008)
• “Todas las palabras que han hablado alguna vez
los seres humanos” ~ 5 EB
• NOAA tiene ~1 PB datos del clima (2007)
• CERN’s LHC genera 15 PB al año(2008)

Some material adapted from slides by Jimmy Lin, Christophe
Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet, Google
Distributed Computing Seminar, 2007 (licensed under Lic. Jorge Guerra 4
Creation Commons Attribution 3.0 License) Maximilien Brice, © CERN

Evolucion hacia el Cloud

Source: http://news.cnet.com
Lic. Jorge Guerra 5

Que es Cloud Computing?
• Viejas ideas:
– Grids, supercomputadoras vectoriales
– Software como Servicio (SaaS)
• Def: desarrollando aplicaciones sobre la Internet
• Recientemente: “[Hardware, Infraestructura,
Plataforma] como un servicio”
– Pobremente definido por lo que hay que evitar “X es un
servicio”
• Utility Computing: computacion paga-como-tu-vas
– Ilusion de infinitos recursos
– No hay costo por adelantado
– Facturacion de grano fino(ejm. por hora)
Lic. Jorge Guerra 6

Definiciones formales

• Un estilo de computación donde capacidades
basadas en TI masivamente escalables en
forma masiva se proporcionan "como un
servicio" en la red (IBM)

Lic. Jorge Guerra 7

Características
• Virtual – Ubicación física y detalles sobre los
infraestructura son transparentes para los usuarios
• Escalable – Capaz de dividir en partes cargas de
trabajo complejas para ser atendidos, a través de una
infraestructura ampliable de forma incremental
• Eficiente – Arquitectura Orientada a Servicios para la
provisión dinámica de compartir los recursos
informáticos
• Flexible – Puede servir una variedad de tipos de carga
de trabajo - tanto de cliente o de empresa
Lic. Jorge Guerra 8

Percepción del usuario

Lic. Jorge Guerra 9

Como lo ven al Cloud Computing

• “Sólo me interesa resultados, no
cómo se implementan las
capacidades de TI”

• " Quiero pagar por lo que yo uso,
como una utilidad mas“

• " Puedo acceder a los servicios
desde cualquier lugar, desde
cualquier dispositivo”

• “Puedo escalar hacia arriba o
abajo de la capacidad, según sea
necesario""
Lic. Jorge Guerra 10

Mapa Cloud/Saas de Laird


Curva de evolución Cloud de Gartner


Implementaciones Cloud


Tipos de implementacion


SAAS


Mapa Saas de Wolosky 2008


Tipos de Cloud Computing


Tipos


Enabling Technology:
Virtualization

App App App

App App App OS OS OS

Operating System Hypervisor

Hardware Hardware

Traditional Stack Virtualized Stack

Some material adapted from slides by Jimmy Lin, Christophe
Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet, Google
Distributed Computing Seminar, 2007 (licensed under Lic. Jorge Guerra 19
Creation Commons Attribution 3.0 License)

Muchos Tipos de Virtualizacion
• Full virtualization
– Instrucciones sensibles (descubrimiento estático o dinámico en tiempo de ejecución) se
sustituyen por la traducción binaria o ejecucion por pasos enhardware en VMM para la
simulacion de SW
– Cualquier SO puede correr en el VM
– Ejemplos: IBM’s CP/CMS, Oracle (Sun) VirtualBox, VMware Workstation
• Virtualizacion asistido por Hardware(IBM S/370, Intel VT, o AMD-V)
– Instrucciones sensibles a traps de CPU– ejecuta sin modificar sistema operativo invitado
– Ejemplos: VMware Workstation, Linux Xen, Linux KVM, Microsoft Hyper-V
• Para-virtualizacion
– Presenta interfaz de SW para las máquinas virtuales similar pero no idéntica a la del HW
subyacente, requiriendo los sistemas operativos invitados que adaptarse
– Examples: early versions of Xen
• Virtualizacion del Sistema Operativo
– kernel del sistema operativo permite instancias de espacio de usuario aislados, en lugar de
un solo espacio
– Instancia look and feel como un servidor real
– Ejemplos: Solaris Zones, QEMU, BSDJorge Guerra
Lic.
Jails, OpenVZ 20

Que hay del Grid?

Hitachi SR8000 – Leibnitz Rechenzentrum
2 TFlop/s (2*1012)

Grid Computing
• Grid Computing Criteria (Ian Foster 2004)
– Coordination: A grid must coordinate resources that are not subject to
centralized control
– Open APIs: A grid must use standard, open, general-purpose protocols
and interfaces
– QoS: A grid must deliver nontrivial qualities of service (e.g., relating to
response time, throughput, availability, and security) for co-allocating
multiple resource types to meet complex user demands
• Promise of ubiquitous grid computing (utility)
– Reality is specialized grids
• TeraGrid, Open Science Grid, LHC Grid
– Grid provides “library level” service customized to HW
• Ensuring consistent libraries across HW is hard!

Cloud Computing vs.
Grid Computing


Datacenter es el nuevo“servidor”
• “Programa” = Web search, email, map/GIS, …
• “Computadora” = 1000’s computadoras, almacenamiento,
redes
• Facilidades y carga de trabajo del tamaño de la
instalacion
• Nuevas ideas de datacenter (2007-2008): camion
container (Sun), flotantes (Google), datacenter-en-tienda
(Microsoft)
• Cómo habilitar la innovación en nuevos servicios sin tener que
construir primero y capitalizar una gran empresa?

24
photos: Sun Microsystems & datacenterknowledge.com

Datacenter Architectures

• Major engineering design challenges in building
datacenters
– One of Google’s biggest secrets and challenges
– Read: https://groups.google.com/group/google-
appengine/browse_thread/thread/a7640a2743922dcf
– Very hard to get everything correct!
• Some issues – Network access, physical security,
power
– And there’s all the software…


Algunos con accesso de fibra muy
seguro …

Source: Build vs. Buy: Internet Datacenter, W. B. Norton and M. Lucking

Algunos con menos que eso


Infraestructura de seguridad

• 24x7 Manned
• Acceso: Biometrics,
card keys
• Video Surveillance

Sliding Glass

Algunos muy seguros…
http://www.thebunker.net


Otros como si hubiera pasado un
huracan…


Datacenter Architectures

• Let’s look at an example from telco
professionals
• Example: AT&T Miami, Florida Tier 1 datacenter
– Redundant dual uplinks to AT&T global backbone
– Minimum N+1 redundancy factor on all critical
infrastructure systems


AT&T Internet Data Center
Security
• Hardened facilities protected by multiple
security measures:
– 24x7x365 on-premise support
– Continuous CCTV surveillance, security breach
alarms, electronic card key access, biometric palm
scan and individual personal access code
– Secured cage and cabinet environment

AT&T Enterprise Hosting Services briefing 10/29/2008

Power
Commercial
Transformer
Power Supply

Paralleling
Switch Gear / Batteries UPS Systems
Manual Switch

Power
Diesel Fuel Tanks Generators Distribution Units

Remote Power
Panels


Power

Commercial Power Feeds
2 Commercial Feed Each At 13,800V
Located Near Substation supplied from 2 different grids
All Cable Routed Underground for Protection


AT&T Internet Data Center Power
• Paralleling Switch Gear
• Automatically Powers Up All
Generators When
Commercial Power is
Interrupted for More Than 7
Seconds
– Generators are Shed to Cover
Load as Needed
– Typical Transition Takes Less
Than 60 Seconds
• Manual Override Available to Emergency Power Switch
Ensure Continuity if
Automatic Start-Up Should
Fail Lic. Jorge Guerra 35

• Four (4) Battery Strings To
Support The UPS Systems
• Battery Strings Contain
Flooded Cell Batteries
• A minimum of Fifteen (15)
Minutes of Battery Backup
Available At Full Load
• Hydrogen Sensors
Monitoring
• Remote Status Monitoring UPS Batteries

of Battery Strings


Uninterruptible Power Supply (UPS)
Eliminate Spikes, Sags, Surges, Transients, And All Other Over/Under Voltage
And Frequency Conditions, Providing Clean Power To Connected Critical Loads
• Four UPS Modules connected in a Ring Bus configuration
• Each Module rated at 1000kVA
• Rotary Type UPS by Piller


Back-up Power – Generators and Diesel Fuel
• Four (4) 2,500 kw Diesel Generators Providing Standby Power,
capable of producing 10 MW of power
• Two (2) 33,000 Gallon Aboveground Diesel Fuel Storage Tanks

Typical Tier-2 One Megawatt Datacenter
Main Supply

Transformer
ATS Generator
Switch
1000 kW
Board

UPS UPS • Reliable Power: Mains + Generator,
STS Dual UPS
PDU
… • Units of Aggregation
STS – Rack (10-80 nodes) → PDU (20-60
200 kW PDU
racks) → Facility/Datacenter
Panel

Panel

50 kW

Circuit
Rack

2.5 kW
X. Fan, W-D Weber, L. Barroso, “Power Provisioning for a
Warehouse-sized Computer,” ISCA’07, San Diego, (June 2007).

Systems & Power Density
• Estimating DC power density hard
– Power is 40% of DC costs
• Power + Mechanical: 55% of cost
– Shell is roughly 15% of DC cost
– Cheaper to waste floor than power
• Typically 100 to 200 W/sq ft
• Rarely as high as 350 to 600 W/sq ft
• Over 20% of entire DC costs is in power
redundancy
– Batteries able to supply 13 megawatt for
12 min
– N+2 generation (11 x 2.5 megawatt)

James Hamilton talk, 1/17/2007

Porque ahora(y no antes)?

• Commoditization of HW & SW
– x86 as universal ISA, plus fast virtualization
– Standard software stack, largely open source (LAMP)
– Bet: Can statistically multiplex multiple instances onto a single
box without interference between instances
• Novel economic model: fine grain billing
– Earlier examples: Sun, Intel Computing Services—longer
commitment, more $$$/hour
• Infrastructure software: eg Google FileSystem
• Operational expertise: failover, DDoS, firewalls...
• More pervasive broadband Internet


Classifying Clouds
App Model for Utility Computing
Amazon EC2 Windows Azure Google AppEngine Something
Close to Physical .NET and CLR… App Specific Traditional New
Hardware ASP.NET Support Web App Model ???
Lower-level,
User Controls More Constraints
Higher-level,
Constrained
Less managed on User Stack More managed ???
Most of Stack Stateless/Stateful Tiers
“flexibility/portability”
Hard to Auto Auto Provisioning “more Auto Scaling and
built-in functionality”
???
Scale and Failover of Stateless App Auto High-Availability

Constraints on App Model Offer Tradeoffs… Lots of Ongoing Innovation…

• Instruction Set VM (Amazon EC2, 3Tera)
• Managed runtime VM (Microsoft Azure)
• Framework VM (Google AppEngine, Force.com)

Aplicaciones web asesinas
• Mobile and web applications
• Extensiones de software de escritorio
– Matlab, Mathematica
• Batch processing / MapReduce
– Oracle at Harvard, Hadoop at NY Times


Demanda de Aplicacion Cloud
• Muchas aplicaciones de nubes tienen curvas
cíclicas de demanda

Recursos
– Daily, weekly, monthly, …
Demanda

Tiempo
• Picos de carga de trabajo más frecuentes y significativos
– Muerte de Michael Jackson:
• 22% de tweets, 20% de trafico Wikipedia , Google penso que
encontraba bajo ataque
– Day de toma de posesion de Obama : 5x incremento en
tweets

Economia de usuarioselegir un
Cómo
Cloud
nivel de
• Pago por usar en lugar de aprovisionamiento
capacidad?
para el pico
• Recuerde: los costos de CD > $ 150M y toma
24 + meses para diseñar y construir
Capacidad
Recursos

Recursos
Demanda Capacidad

Demanda
Tiempo Tiempo
Data center estatico Data center en el cloud

Recursos sin usar

Economia de usuarios Cloud
• Riesgo de sobre-provision: baja utilizacion
• enorme costo perdido en infraestructura
Capacidad
Recursos sin usar
Recrsos

Demanda

Tiempo

Static data center


Economia de usuarios Cloud
• Dura penalidad por baja-provision

Resources
Riesgo de bajo uso si Capacity

predicciones de pico Demand
Resources

Capacity Aplicacion 1 2 3
son demasiado Time (days)
Demand Perdida de ingresos
optimistas 2 CapEx
1
– 3

Resources
despericiado
Time (days)
Capacity

Demand
Muy difícil provisión para
1 2 3
cargas de trabajo de punta Time (days)
Perdida de usuarios

Utility Computing Arrives
• Amazon Elastic Compute Cloud (EC2)
• “Compute unit” rental: $0.10-0.80 0.085-0.68/hour
– 1 CU ≈ 1.0-1.2 GHz 2007 AMD Opteron/Intel Xeon core
Platform Units Memory Disk
Small - $0.10 $.085/hour 32-bit 1 1.7GB 160GB
Large - $0.40 $0.35/hour 64-bit 4 7.5GB 850GB – 2 spindles
X Large - $0.80 $0.68/hour 64-bit 8 15GB 1690GB – 4 spindles
High CPU Med - $0.20 $0.17 64-bit 5 1.7GB 350GB
High CPU Large - $0.80 $0.68 64-bit 20 7GB 1690GB
High Mem X Large - $0.50 64-bit 6.5 17.1GB 1690GB
High Mem XXL - $1.20 64-bit 13 34.2GB 1690GB
High Mem XXXL - $2.40 64-bit 26 68.4GB 1690GB
Northern VA cluster
• No up-front cost, no contract, no minimum
• Billing rounded to nearest hour (also regional,spot pricing)
• New paradigm(!) for deployingJorge Guerra
Lic. services?, HPC? 48

Economics of Cloud Providers

• Microsoft and Google race to build next-gen DCs
(Jan’07)
– Microsoft announces a $550 million DC in Texas
– Google confirm plans for a $600 million site in North
Carolina
– Google two more DCs in South Carolina; may cost another
$950 million – about 150,000 computers each
• Power availability drives deployment decisions


Costos ocultos del cloud


Google Oregon Datacenter

Source: Harper’s (Feb, 2008)

Containerized Datacenters

Nortel Steel Enclosure
Containerized telecom equipment Sun Black Box (242 systems in 20’)

Rackable Systems (1,152 Systems in 40’)
Rackable Systems Container Cooling Model
James Hamilton talk, 1/7/2007

Unit of Data Center Growth
• One at a time:
– 1 system
– Racking & networking: 14 hrs ($1,330)
• Rack at a time:
– ~40 systems
– Install & networking: .75 hrs ($60)
• Container at a time:
– ~1,000 systems
– No packaging to remove
– No floor space required
– Power, network, & cooling only
• Weatherproof & easy to transport
• Data center construction takes 24+ months
– Both new build & DC expansion require
regulatory approval


Sun Modular Datacenter
“BlackBox” (GreenBox)
• Delivered June 9th, operational in September
– Significant challenges with cooling reliability
• 7.5 40U racks
– Power and cooling equivalent to all Soda machine rooms


Economics of Cloud Providers
Economies of Scale for Humongous Datacenters
(1,000’s to 10,000’s of commodity computers)

Electricity Network Operations Hardware
Put Datacenters Put Datacenters Standardize and Containerized
at Cheap Power on Main Trunks Automate Ops Low-Cost Servers

5 to 7 Times Reduction in the Cost of Computing…

• Economy of scale vs. provisioning a medium-
sized (100’s machines) facility
– Public (utility) vs. private clouds issue
• Build-out driven by demand growth (more users)

Alimentación y refrigeración es cara!

La infraestructura de energía y
enfriamiento cuestan MUCHO
Infrastructure PLUS Energy
> Server Cost Since 2001
Infrastructure Alone
Energy Alone

Cost Effective to Discard Inefficient Servers
Dispuesto a pagar más $ / servidor para
servidores eficientes mas potentes
Belady, C., “In the Data Center, Power and
Ahorro de energía Ahorro en Infraestructura! Cooling Costs More than IT Equipment it
Supports”, Electronics Cooling Magazine
Like Airlines Retiring Fuel-Guzzling Airplanes
Lic. Jorge Guerra (Feb 2007) 56

Public vs. Private Clouds
• Building a Very Large-Scale Datacenter Very Is Expensive
– $100+ Million (Minimum)
• Large Internet Companies Already Building Huge DCs
– Google, Amazon, Microsoft…
• Large Internet Companies Already Building Software
– MapReduce, GoogleFS, BigTable, Dynamo
Technology Cost in Medium-Sized DC Cost in Very Large DC Ratio
Network $95 per Mbit/sec/month $13 per Mbit/sec/Month 7.1
Storage $2.20 per GByte/month $0.40 per Gbyte/month 5.7
Administration ≈ 140 Servers / > 1000 Servers / 7.1
Administrator Administrator

James Hamilton, Internet Scale Service
Efficiency, Large-Scale Distributed Systems Huge DCs 5-7X as Cost Effective
and Middleware (LADIS) Workshop Sept‘08
Lic. Jorge Guerra as Medium-Scale DCs 57

Extra Benefits para Cloud Providers

• Amazon: utiliza capacidad ociosa
• Microsoft: vende herramientas .NET
• Google: reutiliza infraestructura existente


Platform - Amazon Web
Services

Elastic Compute Cloud (EC2)
Rent computing resources by the hour
Basic unit of accounting = instance-hour
Additional costs for bandwidth
Simple Storage Service (S3)
Persistent storage
Charge by the GB/month
Additional costs for bandwidth

Platform - Amazon Web Services(EC2)

• • Infrastructure as a Service provider, and current market
leader.
• • Data centers in USA and Europe
• • Different regions and availability zones
• • Uses Xen hypervisor
• • Users provision instances in classes, with different CPU,
memory and I/O performance.


• Users provision instances with an Amazon Machine Image (AMI),
packaged virtual machines.
– Instances ready in 10-20 seconds.
– Amazon provides a range of AMIs
• Users can upload and share custom AMIs,
– preconfigured for different roles.
– • Supports Windows, OpenSolaris and Linux
• Control interface
– HTTP REST/SOAP API
– Command line tools
• Able to implement external monitoring and scaling using interface.

• Flexible, but low-level (roll-your-own)
• No built-in load balancing or scaling (yet)
• Integrated with services:
– Simple Storage Service (S3)
– Scalable Queue Service (SQS)
– SimpleDB

• Pricing based on instance hours
– + bandwidth charges
– + service charges (S3, SQS etc.)

Platform – Windows Azure
• Platform as a Service (in pre-release)
– “Cloud OS”
– .NET libraries for managed code like C#
– Web and worker roles (w/queues)
• Topology described in metadata
• Live upgrades (w/upgrade zones)

Platform – Google App Engine
• Platform as a Service
• Target: Web applications
• Provides custom Python runtime environment, with a
specialized version of the Django framework.
• Integrated with Google data store (Bigtable), and other
“Internet-scale” infrastucture.
• Actually support Java Technology.

Cloud Computing Infrastructure

• Computation model: MapReduce*
• Storage model: HDFS*
• Other computation models: HPC/Grid
Computing
• Network structure

*Some material adapted from slides by Jimmy Lin, Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet,
Google Distributed Computing Seminar, 2007 (licensed under Creation Commons Attribution 3.0 License)

Cloud Computing Computation
Models
• Finding the right level of abstraction
– von Neumann architecture vs cloud environment
• Hide system-level details from the developers
– No more race conditions, lock contention, etc.
• Separating the what from how
– Developer specifies the computation that needs to
be performed
– Execution framework (“runtime”) handles actual
execution


“Big Ideas”

• Scale “out”, not “up”
– Limits of SMP and large shared-memory machines
• Idempotent operations
– Simplifies redo in the presence of failures
• Move processing to the data
– Cluster has limited bandwidth
• Process data sequentially, avoid random access
– Seeks are expensive, disk throughput is reasonable
• Seamless scalability for ordinary programmers
– From the mythical man-month to the tradable
machine-hour

Typical Large-Data Problem

• Iterate over a large number of records
• Extract something of interest from each
• Shuffle and sort intermediate results
• Aggregate intermediate results
• Generate final output
Key idea: provide a functional abstraction for
these two operations – MapReduce

(Dean and Ghemawat, OSDI 2004)

Google MapReduce
Simplified Data Processing on Clusters/Clouds
• http://labs.google.com/papers/mapreduce.html
• This is a dataflow model between services where services can do useful
document oriented data parallel applications including reductions
• The decomposition of services onto cluster engines (clouds) is automated
• The large I/O requirements of datasets changes efficiency analysis in favor
of dataflow
• Services (count words in example) can obviously be extended to general
parallel applications
• There are many alternatives to language expressing either dataflow and/or
parallel operations and/or workflow


Roots in Functional Programming

Map f f f f f

Fold g g g g g


Putting everything together…

namenode job submission node

namenode daemon jobtracker

tasktracker tasktracker tasktracker

datanode daemon datanode daemon datanode daemon

Linux file system Linux file system Linux file system

… … …
slave node slave node slave node


MapReduce/GFS Summary

• Simple, pero poderoso modelo de programación
• Escala a manejar cargas de trabajo de petabyte+
– Google: six hours and two minutes to sort 1PB (10
trillion 100-byte records) on 4,000 computers
– Yahoo!: 16.25 hours to sort 1PB on 3,800 computers
• Incrementa la mejora del rendimiento con más
nodos
• Maneja a la perfección los fallos, pero
posiblemente con penalizaciones en el
rendimiento

Implementacion


Estrategias comerciales

• Microsoft: Software plus Services
– Uso de .NET y Windows
• IBM: Transformation through Customer
Implementations
– Implementacion construida con participacion del
cliente
• Cisco: Evolving Interoperability
– Provee herramientas basadas en Web 2.0


Metodología de implementación


Definir Casos de Uso


Evaluar Infraestructura


Implementar


Problemas a considerar


Buenas practicas


Criterios a considerar


Sumario
• Muchos beneficios de Cloud Computing :
– Desplazar de CapEx aOpEx , escalar OpEx a la demanda
– Startups and prototyping, One-off tasks (Wash. Post)
– Costo asociativo
– Investigacion a escala
• Many Cloud Computing Challenges:
– Disponibilidad
– Datos en la nube pueden ser “pesados” ($$$ para mover)


Referencias
• http://en.wikipedia.org/wiki/Cloud_computing
– Includes references to Amazon, Apple, Dell, Enomalism, Globus,
Google, IBM, KnowledgeTreeLive, Nature, New York Times, Zimdesk
– Others like Microsoft Windows Live Skydrive important
• http://en.wikipedia.org/wiki/Amazon_Elastic_Compute_Cloud
• http://uc.princeton.edu/main/index.php?option=com_conten
t&task=view&id=2589&Itemid=1 Policy Issues
• http://www.cra.org/ccc/home.article.bigdata.html
– Hadoop (MapReduce) and “Data Intensive Computing”
– See Data intensive computing minitrack at HICSS-42 January 2009
• http://ianfoster.typepad.com/blog/2008/01/theres-grid-
in.html
– OGF Thought Leadership blog
• OGF22 talks by Charlie Catlett and Irving Wladawsky-Berger

Gracias!

jguerrag@unmsm.edu.pe
jorgeguerra@uigv.edu.pe

cloud computing alcances e implementacion

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