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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

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Cloud computing case study
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).

Cloud Computing


Amazon web services
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

    • 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…

Cloud Computing


Amazon s ec2
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

    • Load Variety of operating system

    • Install Custom applications

    • Manage network access permission

    • Run image using as many/few systems as you desire

Cloud Computing


Amazon s ec2 features
Amazon’s EC2 features

  • Elastic capacity

    • Elastic resource config/reconfig; Elastic num of instances

  • Completely Control

    • Root access/access to console output/data store/ reboot

  • Reliable

    • Multiple locations

    • Elastic IP addresses

  • Secure

    • Firewall config

    • Virtual Private Cloud

  • Performance

    • Auto Scaling

    • Auto local balancing

Cloud Computing


Amazon s ec2 instances
Amazon’s EC2 Instances

  • On-Demand Instances

    • Pay for capacity without long-term commitment

  • Reserved Instances

    • Standard Instances

    • Micro Instances

    • High-Memory Instances

    • High-CPU Instances

    • High-I/O instance

    • High Storage Instances

  • Spot Instances

    • Bit on unused Amazon EC2 capacity, run those instances for as long as their bid exceeds the current Spot Prices

Cloud Computing


Amazon s ec2 usages
Amazon’s EC2 Usages

  • Create an Amazon Machine Image (AMI) containing your 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

Cloud Computing


Amazon s s3
Amazon’s S3

  • Amazon Simple Storage Service (S3)

    • Storage for the Internet.

  • Features

    • Unlimited Storage

    • Highly scalable

      • in terms of storage, request rate and concurrent users

    • Reliable

      • Store redundant data in multiple facilities and on multiple devices

    • Secure

      • Flexibility to control who/how/when/where to access the data

    • Performance

      • Choose region to optimize for latency/minimize costs

  • Work with other AWS products

    • EC2/Elastic MR/Amazon Import/Export…

Cloud Computing


Example online photo processing service
Example : online photo processing service

  • Photo operation

    • red eye reduction/cropping/customization/re-coloring/teeth whitening, etc

  • Procedure

    • 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

Cloud Computing


Google s appengine
Google’s AppEngine

  • A platform for building and hosting web applications on Google data servers.

    • 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”

Cloud Computing


Appengine does one thing well
AppEngine Does One Thing Well

  • AppEngine handles HTTP(S) requests, nothing else

    • Think RPC: request in, processing, response out

    • Dynamic web serving, with full support for common web technologies

  • App configuration is dead simple

    • No performance tuning needed

  • Everything is built to scale

    • “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

Cloud Computing


Appengine service
AppEngine Service

  • URLFetch

    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

    • Distributed in-memory data cache in front of constant storage

Cloud Computing


App enginer sandbox
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

Cloud Computing


App enginer sandbox1
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.

Cloud Computing


App engine architecture python
App Engine Architecture (python)

req/resp

stateless APIs

urlfech

Python

VM

process

stdlib

mail

app

images

datastore

stateful

APIs

memcache

Cloud Computing


App engine architecture
App Engine Architecture

Cloud Computing


Why not lamp
Why Not LAMP?

  • Linux, Apache, MySQL/PostgreSQL, Python/Perl/PHP/Ruby

  • LAMP is the industry standard

  • But management is a hassle:

    • 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…

Cloud Computing


Automatic scaling
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

Cloud Computing


Automatic scaling1
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

Cloud Computing


Appengine s resource limit
AppEngine’s Resource Limit

Cloud Computing


Appengine development demo
AppEngine Development Demo

http://www.youtube.com/watch?v=tcbpTQXNwac

Cloud Computing


Microsoft s azure
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

    • Window Azure: serving as the development, service hosting, data storage and service management environment

Cloud Computing


Components of azure
Components of Azure

Compute,Storage ,Fabric Controller,Content Delivery Network (CDN),Connect

Cloud Computing


Compute
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.

Cloud Computing


Compute1
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

Cloud Computing


Storage blob table and queue
Storage: Blob, Table and Queue

  • Blob

    • 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

Cloud Computing


Storage blob table and queue1
Storage: Blob, Table and Queue

  • Tables

    • 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.

Cloud Computing


Storage blob table and queue2
Storage: Blob, Table and Queue

Three replications for all kinds

  • Queues

    • Provide a way for communication between instances

Cloud Computing


Fabric controller
Fabric Controller

All Azure application and data reside in Microsoft data center

All Azure components are managed by the fabric controller.

Cloud Computing


Fabric controller1
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

    • Monitoring the system status

    • Optimizing hardware utilization

    • Create VMs and intelligent deployment

    • Update instances without shutdown

    • Managing the OS for Web and Work roles

Cloud Computing


Content delivery network
Content Delivery Network

The CDN stores copies of a blob at sites closer to the clients that use it

Servers store videos, Flash, HTML5…

Cloud Computing


Connect
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.

Cloud Computing


Case study 1
Case Study 1

  • Large scale web applications with occasional huge spikes and background processing.

    • Video sharing site

  • Deployment

    • A number of web instances

      based on demand

    • Table storage

      for information

    • Many works for

      processing

    • Blobs storage for

      large data set

Cloud Computing


Case study 2
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

Cloud Computing


Case study 3
Case Study 3

  • Using storage from an on-premises or hosted application

    • Archive old email

    • User log file

  • Deployment

    • Connect on-premises

      application with Azure

Cloud Computing


Case study
Case Study

More Ideas for Cloud App?

Cloud Computing


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