1. Cloud Computing : Capabilities and limitations
5/16/2019
Cloud Computing : Capabilities and limitations
CUBRC/Calspan, Buffalo, NY. Ramamurthy
Bina Ramamurthy
Computer Science and Engineering Department
University at Buffalo (UB)
This work is partially funded by grants
NSF-DUE-CCLI-Phase II: and NSF-CI-TEAM:

2. Cloud: The next Generation Computer?
5/16/2019
Vacuum tube computer
Transistors based computer
Integrated circuits
Main frame
Minicomputer
Microcomputer
Desk tops, PCs, Laptops, Palmtops
Internet and the web, web services
Mobile devices (cell phone and PDA)
Cloud computing
CUBRC/Calspan, Buffalo, NY. Ramamurthy

3. Evolution of Internet Computing
5/16/2019
deep web
scale
web
Data-intensive
HPC, cloud
Semantic
discovery
??????
Automate (discovery)
CUBRC/Calspan, Buffalo, NY. Ramamurthy
Discover (intelligence)
Transact
Integrate
Interact
Inform
Publish
time

4. Evolution Business Computing Automation Remote operations
5/16/2019
Business
Automation
Remote operations
Heterogeneity
Scale
Integration (application, data)
E-commerce, …
Computing
Programming languages
RISC vs. CISC architectures
Memory capacity
Computing power
Simple programObject-orientationComponent…
CUBRC/Calspan, Buffalo, NY. Ramamurthy

5. Evolution (contd.) Information technology
Internet, World-wide web & Grid
Mobile and wireless
Devices
Software, platforms
Search engines
Tremendous advances in application: it is an app-world
Environment and Society
Accessibility
Globalization (outsourcing, markets)
IT users not exclusive to Computer Science
Digital media
ipod, iphone, idog, ipad, …
Youtube, myspace, social networking
Blogs,wikies, podcasts
Facebook, orkut, twitter
5/16/2019
CUBRC/Calspan, Buffalo, NY. Ramamurthy

6. Beyond Search Engines: Enabling Information Technology and Scientific Applications
5/16/2019
Simple Search (stateless)
TV/Remote
Financial: Build Portfolio
Intelligence Community:
Data-mining
CUBRC/Calspan, Buffalo, NY. Ramamurthy
Medicine: plan treatment
Environment: Plan Forestation
Wireless device
Biotech: drug discovery
Complex multi-organizational applications

7. Background Problem Space: explosion of data
5/16/2019
Problem Space: explosion of data
Inability of traditional storage models to handle the data deluge
Solution space: emergence of multi-core, virtualization, web services, cloud computing
The Big-data computing Model
MapReduce Programming Model (Algorithm)
Google File System;
Apache Hadoop Distributed File System (Data Structures)
Microsoft Dryad
This talk is about Cloud Computing, its capabilities and limitations, benefits and broader impact
CUBRC/Calspan, Buffalo, NY. Ramamurthy

8. Problem SPACE Data scale Compute scale Payroll Kilo Mega Giga Tera
5/16/2019
Data scale
Compute scale
Payroll
Kilo
Mega
Giga
Tera
MFLOPS
GFLOPS
TFLOPS
PFLOPS
Peta
Digital
Signal
Processing
Weblog
Mining
Business
Analytics
Realtime
Systems
Massively Multiplayer
Online Game (MMOG)
Other parameters:
Communication
Bandwidth, ?
Exa
CUBRC/Calspan, Buffalo, NY. Ramamurthy

9. Top Ten Largest Databases
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CUBRC/Calspan, Buffalo, NY. Ramamurthy
Ref:
02/28/09 9 9

10. Challenges
5/16/2019
Need transformative solutions such as the Internet and the Search
Alignment with the needs of the business / user / non- computer specialists / community and society
Need to address the scalability issue: large scale data, high performance computing, automation, response time, rapid prototyping, and rapid time to production
Need to effectively address (i) ever shortening cycle of obsolescence, (ii) heterogeneity and (iii) rapid changes in requirements
Transform data from diverse sources into intelligence and deliver intelligence to right people/user/systems
CUBRC/Calspan, Buffalo, NY. Ramamurthy

11. Enter the cloud
5/16/2019
Cloud computing is Internet-based computing, whereby shared resources, software and information are provided to computers and other devices on-demand, like the electricity grid.
The cloud computing is a culmination of numerous attempts at large scale computing with seamless access to virtually limitless resources.
on-demand computing, utility computing, ubiquitous computing, autonomic computing, platform computing, edge computing, elastic computing, grid computing, …
CUBRC/Calspan, Buffalo, NY. Ramamurthy

12. “Grid Technology: A slide from my presentation to Industry (2005)
5/16/2019
Emerging enabling technology.
Natural evolution of distributed systems and the Internet.
Middleware supporting network of systems to facilitate sharing, standardization and openness.
Infrastructure and application model dealing with sharing of compute cycles, data, storage and other resources.
Publicized by prominent industries as on-demand computing, utility computing, etc.
Move towards delivering “computing” to masses similar to other utilities (electricity and voice communication).”
Now,
CUBRC/Calspan, Buffalo, NY. Ramamurthy
Hmmm…sounds like the definition for cloud computing!!!!!

13. But, It is a changed world now…
5/16/2019
Explosive growth in applications: biomedical informatics, space exploration, business analytics, web 2.0 social networking: YouTube, Facebook
Extreme scale content generation: e-science and e-business data deluge
Extraordinary rate of digital content consumption: digital gluttony: Apple iPhone, iPad, Amazon Kindle
Exponential growth in compute capabilities: multi-core, storage, bandwidth, virtual machines (virtualization)
Very short cycle of obsolescence in technologies: Windows Vista Windows 7; Java versions; CC#; Phython
Newer architectures: web services, persistence models, distributed file systems/repositories (Google, Hadoop), multi-core, wireless and mobile
Diverse knowledge and skill levels of the workforce
You simply cannot manage this complex situation with your traditional IT infrastructure
CUBRC/Calspan, Buffalo, NY. Ramamurthy

14. Answer: The Cloud Computing?
5/16/2019
Typical requirements and models:
Infrastructure as a Service (IaaS): Amazon Web Services (aws.com)
Platform as a Service (PaaS): Windows Azure
Software as a Service (SaaS). Ex: Google docs, Turbo Tax online (Google App Engine)
Services-based application programming interface (API)
A cloud computing environment can provide one or more of these requirements for a cost
Pay as you go model of business
When using a public cloud the model is similar to renting a property than owning one.
An organization can also evolve its own IT infrastructure to a cloud model (private cloud model)
CUBRC/Calspan, Buffalo, NY. Ramamurthy

15. Topics for Discussion Cloud Models Capabilities of cloud (demos)
5/16/2019
Cloud Models
Capabilities of cloud (demos)
Potential benefits
Limitations
Analyzing your organization needs for cloud deployment
UB’s involvement: A State-level Certificate Program in Data-intensive Computing (pending SUNY approval)
(Enabling technologies: WS, Virtualization, Multi-core, open source, public key infrastructure (PKI), ..)
References
Conclusion
CUBRC/Calspan, Buffalo, NY. Ramamurthy

16. Demo details Demo# Details Cloud Model / Technology Demo details 1
Multi-thread fractals
AWS; Window's 64-bit instance
Connect to remote desk top; download .exe file on the local DVD to remote desktop; instal on the AWS cloud deployed machine; Try it out; 2
EC2 Cloud services & Dashboard
AWS; Linux instance
Go over various services offered by AWS on the dash board; deploy a simple Linux instance and connect to it; install tools; tranfer files from local files to the EC2 machine. 3
Google App Engine
GAE
POP!World application and what happended in the introductory Biology course. 4
Storage models: archival storage
AWS S3; EBS; …
S3: data transfer demo using Firefox; AWS dashboard 5
MapReduce data-intensive computing model: local
Hadoop Distributed File System
Single node HDFS/MapReduce wordcount; 6
MapReduce data-intensive computing model: on the cloud
AWS MapReduce
4-20 node Data on S3; MapReduce workflow on EC2 7
Three-tier Enterprise web application
AWS; FileZilla (sftp) transfer of .war file
.war is deployed automatically on the machine instance created
5/16/2019
CUBRC/Calspan, Buffalo, NY. Ramamurthy

17. Cloud Models Azure, EC2 and GAE are complementary models:
5/16/2019
Azure, EC2 and GAE are complementary models:
In EC2 you can work at the low levels and have control over the infrastructure
Azure is abstract in that it provides a fabric of compute cycles and storage and operates at the enterprise level
GAE is a Python-based environment for design, development and deployment of applications
All have well-defined pricing model that is quite reasonable
GoGrid and Rackspace are two other cloud companies that primarily feature cloud data centers
CUBRC/Calspan, Buffalo, NY. Ramamurthy

18. Windows Azure Enterprise-level on-demand capacity builder
5/16/2019
Enterprise-level on-demand capacity builder
Fabric of cycles and storage available on-request for a cost
You have to use Azure API to work with the infrastructure offered by Microsoft
There is a development environment where you develop and test the application and upload it to the production environment on the cloud.
Special features are blob storage, web roles and worker roles.
CUBRC/Calspan, Buffalo, NY. Ramamurthy

19. Amazon EC2 Amazon EC2 is one large complex web service.
5/16/2019
Amazon EC2 is one large complex web service.
EC2 provided an API for instantiating computing instances with any of the operating systems supported.
It can facilitate computations through Amazon Machine Images (AMIs) for various other models. I will illustrate this with MapReduce
Signature features: S3, Cloud Management Console, MapReduce Cloud, Amazon Machine Image (AMI)
Lets look at some of the features of AWS:
On-demand provisioning (machine instance, storage, applications, …)
Scalability (auto scaling / load balancing)
Availability (through redundancy)
Rapid prototyping and deployment
Elastic resources (including bandwidth)
CUBRC/Calspan, Buffalo, NY. Ramamurthy

20. AWS Features (demos) Elastic Compute Cloud (EC2) AWS Cloud Front
5/16/2019
Elastic Compute Cloud (EC2)
AWS Cloud Front
Amazon Machine Images (AMI)
Linux and Windows images
Simple Storage Service (S3)
Elastic Block Volumes (ESB) (mounted/attached volumes)
Load balancers
Support for Data-intensive computing : Ex: Hadoop MapReduce ; Other applications to come soon
AMI: amazon machine images
CUBRC/Calspan, Buffalo, NY. Ramamurthy

21. Data-intensive Application Architecture
Data nodes
Name node
HDFS
Map
Reduce
MapRed for Appln.2
MapRed
Appln.1
PAYLOAD
Rules
Editor
Dynamic
Web site
Consumer/
user
Admin
Producer/
DATA SOURCES
intelligence
push
Annotations/logs
INTERFACE
analysts
Foreground
applications
GRID
5/16/2019
CUBRC/Calspan, Buffalo, NY. Ramamurthy
JobTracker
TaskTrackers
TaskTrackers

22. Google App Engine
5/16/2019
This more a web interface for a development environment that offers a one stop facility for design, development and deployment Java and Phython- based applications in Java and Phython.
Google offers the same reliability, availability and scalability at par with Google’s own applications
Interface is software programming based
Comprehensive programming platform irrespective of the size (small or large)
We developed an Adobe Flash Evolutionary Biology tool for teaching freshman biology. (NSF CI-TEAM Grant)
This scenario is a serendipitous invaluable experience for us about the capability of the cloud.
CUBRC/Calspan, Buffalo, NY. Ramamurthy

23. Google App Engine Load Monitoring on 10/11
5/16/2019
CUBRC/Calspan, Buffalo, NY. Ramamurthy

24. MemCache on GoogleAppEngine on 10/11
5/16/2019
CUBRC/Calspan, Buffalo, NY. Ramamurthy
Memcache partial unavailability

25. Potential Benefits Scalability Availability
5/16/2019
Scalability
Availability
Automation through web services (SOAP and REST)
Newer programming and application models
Accessibility to masses through commoditization
Organic disaster mitigation and recovery
Green Computing
Rapid prototyping and deployment
Obliterates digital divide
Great concept to draw in and engage the net-generation
Not so steep learning curve (quite intuitive)
Cost saving
CUBRC/Calspan, Buffalo, NY. Ramamurthy

26. Scalability through Elastic Resources
5/16/2019
Move, shift, balance. load, unload, juggle
You can request only as many resources as you want and return them when you no longer need them
Automatic load balancing possible
On-demand provisioning of resources
Most importantly turn-on and turn-off whenever
Economies of scale: Increases use/production leads to increased efficiency and lowers unit cost
Large organization have perfected the management and administration of large scale infrastructure for their own demanding operations; why not sell this expertise?
CUBRC/Calspan, Buffalo, NY. Ramamurthy

27. Availability Availability: Amazon.com claims it is 99.99% available
5/16/2019
Availability: Amazon.com claims it is 99.99% available
Redundancy and multiple availability zones
a = (p – (c X d) ))/p where
a is the expected availability
c the % of likelihood that you will encounter a server loss in a given period
d expected downtime from the loss of the server
p the measurement period
If you have 40% chance of your server failing and it takes 24 hours to fix it, availability is:
(8760 –0.40X24)/8760 = or 99.9%
Now consider other points of failures in the system: two cable outage in two hours
(8760 – ((0.4*24)+ (2.0*2)))/8760 = 99.84%
Redundancy mitigates this problem. When you have two or more physical components representing a logical component, the expected downtime of the logical component is the downtime of all the components down simultaneously
c X d now becomes (c X dn )/pn-1 (n-way redundancy)
Applying this formula to a server with a duplicate (n=2) we get 99.99%
CUBRC/Calspan, Buffalo, NY. Ramamurthy

28. Newer Programming and Application Models
5/16/2019
MapReduce programming model
Google File System (GFS), Hadoop File System (HDFS), Collossus, BigTable (stores data in <key,value> pairs)
Dryad from Microsoft
Social networking management frameworks
Many more ideas in the recent Symposium on Cloud Computing (SOCC 2010, USA)
We expect emergence of many more data- intensive computing models
CUBRC/Calspan, Buffalo, NY. Ramamurthy

29. Accessibility to the Masses
5/16/2019
No software to write (at least for the common user); Take a look at the logo of SaleForce.com
Buy cycles using a credit faster than you can go to a supermarket and get bread.
The cloud environment will be usable similar to how gadgets were used
You create AMI for your commonly used applications and let the user instantiate it: uniformity, standardized use, accountability, compliance can be enforced, knowledge sharing, reusability.
CUBRC/Calspan, Buffalo, NY. Ramamurthy

30. Disaster Mitigation
5/16/2019
Iceland’s volcanic ash cloud illustrates the case for cloud computing: con.com/node/
Cloud storage and on-demand services can help in setting up the backup as well as operation capacity to help in mitigating disasters.
After the disaster has moved on, the cloud can be used to recover and restore; once the operation is restored, the cloud capacity is no longer needed and services can be terminated.
CUBRC/Calspan, Buffalo, NY. Ramamurthy

31. Facilitates Green Computing
5/16/2019
The cloud hardware is well located and optimized for efficient operation.
Easy to control ramp up and ramp down the capacity, turn off and on according to green algorithms;
Organization can establish policies and enforce them using green processes such as automatic on/off.
Though on/off is available for individual PC/laptops, power saving is up to the user.
CUBRC/Calspan, Buffalo, NY. Ramamurthy

32. Limitations Security of data and application Privacy of data
5/16/2019
Security of data and application
Privacy of data
Reliability
Other vulnerabilities (crash of a virtual machine vs. crash of a physical machine) : fast fail-over possible
Multi-tenancy (Ex: various organizations renting space on the same cloud provider).
State of flux (frequent updates and addition of features)
Cost computation/pricing
CUBRC/Calspan, Buffalo, NY. Ramamurthy

33. Analyzing your organizational needs for migration to cloud
5/16/2019
CUBRC/Calspan, Buffalo, NY. Ramamurthy
CC – cloud computing
OCC – outsourced cloud computing (ex: CRM by salesforce.com)
CC/local – cloud computing or traditional IT

34. Certificate Program in Data-intensive Computing at UB
5/16/2019
5 courses:
CS2 (CSE250: Algorithms and Data Structures),
Distributed Systems (CSE486),
Data-intensive Computing (CSE487),
Course in any major area of interest (Ex: biomedical engineering)
Capstone project in the area
A limited number of scholarships ($1000 each) available for enrolling and completing this certificate.
These courses are also offered online by Enginet distance learning program.
CUBRC/Calspan, Buffalo, NY. Ramamurthy

35. References
5/16/2019
Cloud Computing: Characteristics, economies and architectures:
t/DeveloperGuide/
Amazon cloud free limited trial:
Google App Engine (free limited trial):
First ACM Symposium on Cloud Computing (SOCC) 2010, Indianapolis Indiana,
CUBRC/Calspan, Buffalo, NY. Ramamurthy

36. Summary
5/16/2019
Cloud computing is indeed a transformational technology that is here to stay.
It will be as impactful (if not more) as the Internet, the web and the e-commerce revolution.
Traditional IT will gradually evolve into the cloud infrastructure.
Most of all, it is poised to socialize computing and make it as widely and easily accessible as the other transformational technologies such as telephones and automobiles.
CUBRC/Calspan, Buffalo, NY. Ramamurthy