Application and Data Portability in Cloud Computing The Cirrocumulus Way Ajith Ranabahu and Amit...

Application and Data Portability in Cloud Computing

The Cirrocumulus Way

Ajith Ranabahu and Amit ShethOhio Center of Excellence in Knowledge enabled Computing

(Kno.e.sis) http://knoesis.org

Wright State University, Dayton OH, USA

Agenda

• Issues in Cloud computing and its adoption• Vendor lock-in problem

– Understanding cloud heterogeneity• Using Domain Specific Languages to implement

application portability• Using RDFS based modeling to implement data

portability• Two Example applications• Where are the semantics ?

What is Cloud computing ?

CheapNo upfront cost

Utility style Resources

Service oriented provisioning over the Web

APIs enabling programmatic access

Numerous support servicesData access (Amazon RDS, Amazon simple DB, Google Big TableAutomatic scaling (Amazon Elastic Beanstalk)

Did it change the world ? Why not ?

Multiple Issues in Wide Adoption

Almost always heterogeneous platforms

Supported programming languagesData representationResource provisioning and Management Workflow

Writing one application is not enough !

Amazon EC2 accepts almost any language

Windows Azure support .NET (other languages need workarounds)

Google App Engine only supports Java and Python

Vendor Lock-in

Users locked into cloudsproprietary APIs

Limited language supportCustom tools and workflows

We need a better way of doing things !

Write applications in cloud agnostic ways : "Write once - Run on any cloud"

Access cloud resources with a uniform workflow

Move applications and data across cloudswhen the need arises

Understanding Heterogeneity :Where does it exist ?

Vertical and Horizontal

Vertical

Within the same type of clouds - Say Infrastructure service providers

Horizontal

Across different types of clouds - Say Infrastructure clouds and platform clouds

Some examples

Amazon EC2 vs Rackspace

Both are infrastructure providers

Process of starting a VM in EC2 is very differentfrom doing the same in Rackspace

Google App Engine vs Windows Azure

Both are platforms

Supports different languages (Java/Python vs C#/.NET)

Requires using different custom libraries

Requires adhering to different data models(document-oriented vs Relational)

How can Portable Applications be developed ?

DSLs to the rescue !

Domain Specific Languages (DSL) are specialized, mini languages that address problems in a limited domain.

• Matlab (Mathematics)• SQL (Data definitions and manipulation)• Ant / Make (build scripts)

What 'Domain' are we talking about ?

Many !

Each domain has to have its own DSL

Some example domains of Interest (in the Cloud context)

Data driven mobile applications

Enterprise data retrieval applications

Statistical Scientific Workflows

What is the catch ?

DSLs are not universal applicable:Useful only in a supported domain

Forcing a top-down (model driven) development method

The case of the "smallest common set of features"

When using an abstraction over multiple platforms, only the smallest common set of features can be effectively supported.

Its not as serious as it sounds !

The number of unique features across the major platforms is quite small

[quantification needed !]

In case platform specific features are needed

Use the DSLs as boiler-plate code generators

Use Bison-like conditional code additions to insert specific code fragments

Two Examples

MobiCloudGenerating hybrid applications that have

pieces running on clouds and mobile devices

http://mobicloud.knoesis.org

SCALE : Scalable Cloud AppLication gEnerator

Generating statistical workflows for biologists

http://metabolink.knoesis.org/SCALE

What kind of Heterogeneity are we talking about ?

package bean;

import com.google.appengine.api.datastore.Key;

import javax.jdo.annotations.IdGeneratorStrategy;import javax.jdo.annotations.IdentityType;import javax.jdo.annotations.PersistenceCapable;import javax.jdo.annotations.Persistent;import javax.jdo.annotations.PrimaryKey;

@PersistenceCapable(identityType = javax.jdo.annotations.IdentityType.APPLICATION)public class Todoitem { @PrimaryKey @Persistent(valueStrategy = IdGeneratorStrategy.IDENTITY) private Key key; public Key getKey() { return key; } @Persistent private java.lang.String time; public java.lang.String getTime() { return time; }

public void setTime(java.lang.String local_time) { this.time = local_time; } ……..}

package bean;

public class Todoitem { /* PrimaryKey */ private int key; public int getKey() { return key; } public void setKey(int localkey) { this.key = localkey; } private java.lang.String time; public java.lang.String getTime() { return time; }

public void setTime(java.lang.String local_time) { this.time = local_time; } …}

An Example for a simple class [ Code generated by MobiCloud for task manager example ]

Code for Google App Engine version

Code for Local / EC2 version

model(:todoitem,{:time => :string,:location => :string,:description => :string,:name => :string})

The DSL code fragment relevant to the generated code

Taking care of proper annotations and adhering platform specific restrictions Is taken care of by the generators !

Porting data across clouds

Issues caused by the difference in data models

Often more crucial than porting the application code

We are in luck !We already followed a model-driven

development process

We get to define data at a higher level of abstraction

Data transformations can be generated along with the application code

Our Choice for Data Definitions ?

RDF Schema (RDFS)

Developers need not know a lot of RDFS !

Graphical and textual abstractions can be provided todamp the learning curve

Where are the semantics ?

The four types of semanticsfor Clouds

In defining data structures

RDF/RDFS is already considered 'semantic'

Enhancing functional aspects

Add business policies via rules

Add non-functional enhancements

Security and reliability via policies

Describe System configurations

ECML, EDML, EMML byElastra

Inspired by the 4 types of semantics in services by Shivashanmugam, Sheth

http://knoesis.org/library/resource.php?id=00186

Possible Semantic usage in MobiCloud

What is left to do ?

Hassel-free deployment is a problem

Need a comprehensive middleware platform to fix it

Conclusion

Portability (both application and data) in Cloud computing is an important problem

to solve

Using DSLs and semantic abstractions is a viable solution to the portability

problem in many domains

There is long way to go but things seem promising !

Demonstration

Questions ?

Thank you