Transcript Lecture #1 - Electrical and Computer Engineering

Cloud Computing
ECE7650
Cloud Computing Case Study
 At one extreme, sharing basic IT infrastructure
 E.g Amazon’s EC2: an EC2 instance appears physical HW,
users can control nearly the entire sw stack, from the
kernel upwards
 At the other extreme, sharing application domain-
specific platforms

E.g. Google AppEngine, a platform for building and
hosting web applications on Google Web servers. Limited
range of applications are supported,
 In the middle, sharing programming environments
 Microsoft’s Azure written using .NET libraries, and
compiled to the Common Langurage Runtime (CLR).
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Amazon Web Services
 AWS is a collection of remote computing services that together
make up a cloud computing platform, offered over the Internet by
Amazon.com.
 List of AWS products
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Amazon Elastic Compute Cloud (EC2) provides scalable virtual private
servers using Xen.
Amazon Simple Storage Service (S3) provides Web Service based
storage.
Amazon SimpleDB allows developers to run queries on structured data. It
provide "the core functionality of a database."
Amazon Elastic MapReduce allows developers to easily and cheaply
process vast amounts of data. It uses a hosted Hadoop framework
running on the web-scale infrastructure of EC2 and Amazon S3.
Amazon Virtual Private Cloud (VPC) creates a logically isolated set of
Amazon EC2 instances which can be connected to an existing network
using a VPN connection.
And More…
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Amazon’s EC2
 Amazon Elastic Compute Cloud (EC2)
 Web service that provides resizable compute capacity in
the cloud
 An EC2 instance appears physical HW, provides
users complete control over nearly entire sw
stack, from the kernel upwards
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Load Variety of operating system
Install Custom applications
Manage network access permission
Run image using as many/few systems as you desire
C. Xu @ Wayne State
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Amazon’s EC2 features
 Elastic capacity
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Elastic resource config/reconfig; Elastic num of instances
 Completely Control
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Root access/access to console output/data store/ reboot
 Reliable
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Multiple locations
Elastic IP addresses
 Secure
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Firewall config
Virtual Private Cloud
 Performance
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Auto Scaling
Auto local balancing
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Amazon’s EC2 Instances
 On-Demand Instances
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Pay for capacity without long-term commitment
 Reserved Instances
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Standard Instances
Micro Instances
High-Memory Instances
High-CPU Instances
High-I/O instance
High Storage Instances
 Spot Instances
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Bit on unused Amazon EC2 capacity, run those instances for as
long as their bid exceeds the current Spot Prices
C. Xu @ Wayne State
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Amazon’s EC2 Usages
 Create an Amazon Machine Image (AMI) containing your
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applications, libraries, data and associated configuration
settings. Or use pre-configured, templated images to get up
and running immediately.
Choose the types of instances and OS, then start, terminate,
and monitor as many instances of your AMI as needed, using
the web service APIs or the variety of management tools
provided.
Determine whether you want to run in multiple locations,
utilize static IP endpoints, or attach persistent block
storage to your instances.
Pay only for the resources that you actually consume, like
instance-hours or data transfer.
http://www.youtube.com/watch?v=JPFoDnjR8e8
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Amazon’s S3
 Amazon Simple Storage Service (S3)
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Storage for the Internet.
 Features
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Unlimited Storage
Highly scalable
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in terms of storage, request rate and concurrent users
Reliable
• Store redundant data in multiple facilities and on multiple devices
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Secure
• Flexibility to control who/how/when/where to access the data
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Performance
• Choose region to optimize for latency/minimize costs
 Work with other AWS products
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EC2/Elastic MR/Amazon Import/Export…
C. Xu @ Wayne State
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Example : online photo processing service
 Photo operation
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red eye reduction/cropping/customization/re-coloring/teeth
whitening, etc
 Procedure
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Web server receive request
Put request message in the queue
Pictures stored in S3
Multiple EC2 instances run photo processing
Put back in the queue
Return
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Google’s AppEngine
 A platform for building and hosting web
applications on Google data servers.
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support Python/Java
Upload and ready to run
No need to maintain your own servers, easy to scale as
your traffic and storage grow
 Kevin Gibbs, App Engine Tech Lead
“AppEngine is a system that exposes various
pieces of Google’s scalable infrastructure so that
you can write server-side applications on top”
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AppEngine Does One Thing Well
 AppEngine handles HTTP(S) requests, nothing else
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Think RPC: request in, processing, response out
Dynamic web serving, with full support for common web
technologies
 App configuration is dead simple
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No performance tuning needed
 Everything is built to scale
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“infinite” number of apps, requests/sec, storage capacity
Automatic scaling
 Automatic management
 Load balancing/monitoring/scaling
 Local development Environment: simulation
 Google AppEngine Software Development Kit
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AppEngine Service
 URLFetch
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fetch web resources/services
Images
 manipulate images: resize, rotate, flip, crop
Google Accounts
 Allow users to sign in their product by using G account
Mail
 Send message using Google infrastructure.
XMPP
 send instant messages
Datastore
 managing data objects
Memcache
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Distributed in-memory data cache in front of constant storage
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App Enginer Sandbox
 Appl run in a secure environment that
provides limited access to the underlying
OS.
Allow AppEngine to distribute web requests for
the application across multiple servers; Start
and stop servers to meet traffic demands
 Isolates your appl in its own secure, reliable
environment that is independent of the hw, OS,
and physical location of the web server
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App Enginer Sandbox
 Limited Access:
Appl can only access other computers on the
Internet through provided URL fetch and email
API
 Cannot write to file system, can read file
uploaded by the appl; The app must use the App
Engine datastore for all data that persists
between requests
 Run in response to a web request, and must
return response data within 30 seconds. A
request handler cannot spawn a sub-process or
execute code afte the response has been sent.
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App Engine Architecture (python)
req/resp
stateless APIs
urlfech
mail
Python
VM
process
stdlib
app
images
stateful
APIs
memcache
datastore
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App Engine Architecture
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Why Not LAMP?
 Linux, Apache, MySQL/PostgreSQL,
Python/Perl/PHP/Ruby
 LAMP is the industry standard
 But management is a hassle:
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Configuration, tuning
Backup and recovery, disk space management
Hardware failures, system crashes
Software updates, security patches
Log rotation, cron jobs, and much more
More Important…
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Automatic Scaling
 Low-usage apps: many apps per physical host
 High-usage apps: multiple physical hosts per app
 Stateless APIs are trivial to replicate
 Datastore built on top of Bigtable; designed to
scale well
 Abstraction on top of Bigtable
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Automatic Scaling
 You don’t need to configure your resource needs
 One CPU can handle many requests per second
 Apps are hashed onto CPUs:
One process per app, many apps per CPU
 Creating a new process is a matter of cloning a generic “model”
process and then loading the application code (in fact the clones
are pre-created and sit in a queue)
 The process hangs around to handle more requests (reuse)
 Eventually old processes are killed (recycle)
 Busy apps (many QPS query per sec) get assigned to multiple CPUs
 This automatically adapts to the need
• as long as CPUs are available
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AppEngine’s Resource Limit
Quota
Limit
Apps per developer
10
Time per request
30 sec
Files per app
1,000
HTTP response size
Quota
Free Quota
Emails per day
2,000
10 MB
BW in per day
10,000 MB
Datastore item size
1 MB
BW out per day
10,000 MB
Appl code size
150 MB
Secure BW in per day
2,000 MB
Secure BW out per day
2,000 MB
CPU megacycles per day
200,000,000
HTTP Requests per Day
1,333,328*
Datastore API calls per day
10,368,000*
Data stored
1 GB
URLFetch API calls per day
657,084*
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AppEngine Development Demo
 http://www.youtube.com/watch?v=tcbpTQ
XNwac
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Microsoft’s Azure
 Cloud service platform hosted in MS data centers,
including an OS, Windows Azure, and a set of
developer services like .NET services, Live
services, and SQL data services
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Window Azure: serving as the development, service
hosting, data storage and service management
environment
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Components of Azure
Compute，Storage ，Fabric Controller， Content
Delivery Network (CDN)， Connect
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Compute
 Web role: web-based application
 Worker role: various windows-based code
 VM role: Windows Server image, customized software
installations on OS will be kept.
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Compute
 Submit an application with configuration of type
and number of instances
 Fabric controller creates VM and runs the code
 Requests through HTTP/HTTPS/TCP are load
balanced across all instances without affinity.
 Exposed API that allows
 automatically scale up and down.
 application logging
 Monitoring resource usage: CPU/storage
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Storage: Blob, Table and Queue
 Blob
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Simple unstructured hierarchy: each container holds one or
more blobs, which is up to terabyte
Access from URI:
http://<StorageAccount>.queue.core.windows.net/<QueueName>
Basic storage
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Storage: Blob, Table and Queue
 Tables
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Structured storage.
Stored in a group of entities that contain properties.
Entity with different num of properties in various types.
Not relational
tables, support
massively scalable
applications.
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Storage: Blob, Table and Queue
 Queues
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Provide a way for communication between instances
Three replications for all kinds
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Fabric Controller
 All Azure application and data reside in Microsoft
data center
 All Azure components are managed by the fabric
controller.
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Fabric Controller
 Distributed application that’s replicated
across machines.
 It owns all of the resources: computer,
switches, load balancers and is aware of
each azure application
 Autonomic management
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Monitoring the system status
Optimizing hardware utilization
Create VMs and intelligent deployment
Update instances without shutdown
Managing the OS for Web and Work roles
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Content Delivery Network
 The CDN stores copies of a blob at sites closer to
the clients that use it
 Servers store videos, Flash, HTML5…
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Connect
 Effectively connecting on-premises environments with
Windows Azure
 Installing a Endpoint Agent on each on-premises machines
and communication through IP-level connection
 Azure applications appears to be on the same IP network as
the on-premises machines.
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Case Study 1
 Large scale web applications with occasional huge
spikes and background processing.
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Video sharing site
 Deployment
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A number of web instances
based on demand
Table storage
for information
Many works for
processing
Blobs storage for
large data set
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Case Study 2
 Parallel processing applications
 Financial modeling at a bank
 New drug testing simulations in a pharmaceutical company
 Deployment
 Web role for access interface
 Many workers
for processing
 Large data set
stored in blobs
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Case Study 3
 Using storage from an on-premises or
hosted application
Archive old email
 User log file
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 Deployment
 Connect on-premises
application with Azure
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Case Study
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