COMPARATIVE STUDY OF VARIOUS PLATFORM AS A SERVICE FRAMEWORKS

International Journal on Cloud Computing: Services and Architecture (IJCCSA) Vol. 6, No. 1, February 2016
DOI : 10.5121/ijccsa.2016.6103 23
COMPARATIVE STUDY OF VARIOUS PLATFORM AS A
SERVICE FRAMEWORKS
Mohan Krishna Varma and Eunmi Choi*
DISC Lab, Graduate School of Business IT, Kookmin University, Seoul, South Korea
*Corresponding Author, School of Business IT, Kookmin University, Seoul, Korea
ABSTRACT
Cloud computing is an emerging paradigm with three basic service models such as Software as a Service
(SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). This paper focuses on
different kinds of PaaS frameworks. PaaS model provides choice of cloud, developer framework and
application service. In this paper, detailed study of four open PaaS frameworks like AppScale, Cloud
Foundry, Cloudify, and OpenShift are explained with the architectural components. We also explained
more PaaS packages like Stratos, mOSAIC, BlueMix, Heroku, Amazon Elastic Beanstalk, Microsoft Azure,
Google App Engine and Stakato briefly. In this paper we present the comparative study of PaaS
frameworks.
KEYWORDS
Cloud Computing, AppScale, Cloud Foundry, Cloudify, OpenShift, Stratos, BlueMix, Heroku & Stackato
1. INTRODUCTION
Cloud computing [2] is a widely used technology in which computers are networked to provide
storage and compute services using virtualization technology. Cloud computing must satisfy five
essential characteristics. They are on demand service, access network, resource pooling, elasticity
and measured services. To achieve these five essential characteristics, cloud computing provides
three kinds of service models: Software as a Service (SaaS), Platform as a Service (PaaS) [8] and
Infrastructure as a Service (IaaS) [9]. Cloud computing service models are shown in Figure 1.
Customer Relationship Management (CRM) applications are widely used services in the SaaS.
Application platform delivered as a service is described as PaaS and it is used to deploy the user
code. AppScale [3], Cloud Foundry, Cloudify and OpenShift open-source environments can be
used as PaaS. IaaS is used to build their private infrastructure, which reduces the setup cost. IaaS
can provide virtualized resources such as computation, storage and communication. Eucalyptus
[1], open stack and cloud stack open-sources can be used to provide IaaS.
Figure 1. Cloud computing service models

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This paper will focus on the PaaS service model. It is easy to deploy, run and scale application
using PaaS. Some of the PaaS have limited language and framework support. They do not deliver
key application services needed for cloud applications. They sometime restrict deployment to a
single cloud. Whereas open PaaS provides choice of cloud like private, public or hybrid, choice
of developer framework like spring, ruby, or java and application services like mongoDB,
MySQL, or PostgreSQL for running the applications. This paper deals with the architectural
components of major open PaaS frameworks like AppScale, Cloud Foundry, Cloudify and
OpenShift. And also given some explanation about PaaS frameworks such as Stratos, mOSAIC,
BlueMix, Heroku, Amazon Elastic Beanstalk, Microsoft Azure, Google App Engine and Stakato.
The paper is organized as follows. Section 2 introduces AppScale and its components, Cloud
Foundry architecture and component explanation given in Section 3, Cloudify open PaaS is
explained in Section 4, Section 5 deals with OpenShift, other PaaS frameworks are introduced in
Section 6, comparison of PaaS frameworks are given in Section 7 and finally Section 8 concludes
the paper.
2. APPSCALE
AppScale [4] is a scalable, distributed, and fault-tolerant cloud runtime system that executes over
cluster resources. It can be deployed on Xen [5], Kernel-based Virtual Machine (KVM), Amazon
EC2 or Eucalyptus. AppScale initial design utilizes the standard three-tier web deployment model
in the design. In the later design cycles more components are added to the AppScale. Table 1
shows the AppScale components, language used to design the component and their functionality.
Table 1. AppScale Components
Component Language Functionality
AppController Ruby Executes on every node and starts automatically when
the guest virtual machine boots
AppLoadBalancer Ruby on Rails Processes arriving requests from users and forwards
them to the application server
AppServer Python Running through a number of distant hosts to support
automated execution of applications
Database Master Python Offers persistent storage for applications, processes
protocol buffers from apps and makes requests on its
behalf to read and write data to the data store
Database Slave Python Facilitate distributed, scalable, and fault tolerant data
management
AppScale Tools Ruby Starts an AppScale system, deploys and tear down
applications, queries the state and performance of
AppScale deployment or application, and manipulates
AppScale configuration and state
3. CLOUD FOUNDRY
Cloud Foundry [13] is an open PaaS, which provides choice of clouds, developer frameworks and
application services. Cloud Foundry makes application development faster and easier. We can
build, test, deploy and scale applications with help of Cloud Foundry. It is an open-source project
available through a variety of private cloud distributions and public cloud instances. Cloud
Foundry started as a platform to deploy Java Spring applications on Amazon Web Services.
VMware acquired the Cloud Foundry and made it into an open-source, multi-language and multi-
framework PaaS. Cloud Foundry supports multiple languages and multiple runtimes such as Java,

25
Ruby, Scala, spring and Node.js. Cloud Foundry can run on anything like laptop, desktop, micro
cloud, private cloud or public cloud. So, it is called as open PaaS as shown in Figure 2. Cloud
Foundry has three dimensions to the platform: choice of frameworks, choice of application
services and the deployment choice. Cloud Foundry supports spring for Java, Rails and Sinatra
for Ruby, Node.js and JVM languages like Groovy, Grails and Scala. It also supports Microsoft
.NET Framework and became the first non-Microsoft platform to support .NET.
Figure 2. Cloud Foundry as Open PaaS
Cloud Foundry supports RabbitMQ for messaging, MongoDB and Redis for NoSQL, relational
databases MySQL and PostgreSQL. Cloud Foundry can be deployed on notebooks through Micro
Cloud Foundry. It is the complete version of Cloud Foundry designed to run in a virtual machine.
It can also be deployed on Private Cloud or Public Cloud. These features made Cloud Foundry as
a flexible PaaS.
Cloud Foundry components perform routing, authentication, messaging, logging, application
storage and execution, provide services and take care of application life cycle. The router routes
incoming traffic to the appropriate component, usually the Cloud Controller or a running
application on a DEA (Droplet Execution Agent) node. The User Account and Authentication
(UAA) server work with Login Server to provide identity and authentication management.
OAuth2 Server is uses as the user account and authentication server. Cloud controller and health
manager components take care of the application lifecycle in the cloud foundry. Cloud controller
is responsible for managing the lifecycle of applications. When a developer pushes an application
to cloud foundry, application is targeting the cloud controller. Cloud controller then stores the raw
application bits, creates a record to track the application metadata, and directs a DEA node to
stage and run the application. Health manager monitor applications to determine their state,
version, and number of instances. Applications state may be running, stopped, or crashed. Health
manager determine applications expected state, version, and number of instances. It reconciles the
actual state of applications with their expected state. Health manager directs the cloud controller
to take action to correct any discrepancies in the state of applications. The Droplet Execution
Agent manages application instances, tracks, started instances, and broadcasts state messages.

26
Application instances live inside warden containers. Containerization ensures that application
instances run in isolation, get their fair share of resources, and are protected from noisy
neighbours. Blob Store holds the application code, build packs, and droplets. Applications
typically depend on services like databases or third-party SaaS providers. When a developer
provisions and binds a service to an application, the service broker for that service is responsible
for providing the service instance. Cloud Foundry uses a lightweight publish-subscribe and
distributed queueing messaging system for internal communication between components. This
internal communication performed via message bus. The metrics collector gathers metrics from
the components. Operators can use this information to monitor an instance of Cloud Foundry. The
application logging aggregator streams the application logs to the corresponding developers.
Cloud Foundry components are shown in Figure 3.
Figure 3. Cloud foundry components
4. CLOUDIFY
Cloudify [14] is another open PaaS cloud application manager. It automates common processes
needed to perform and to manage the applications in a cloud environment. Cloudify composed of
three main components. The components are Command line interface client, Agents, and
Manager. Command line interface client is an executable file which is written in Python. It is
packaged with python and relevant dependencies in an executable file. Command line interface
client can run on Windows, Linux and Mac operating systems. Command line interface client
preform two tasks. First one is manager bootstrapping and another is managing applications.
Bootstrapping is the process of installing the Cloudify manager. Command line interface client
provides the user with the full set of functions for deploying and managing applications including
log and event browsing.

27
Cloudify Agents are responsible for managing the manager’s command execution using a set of
plugins. There is a manager side agent per application deployment and optional agent on each
application Virtual Machine (VM). The manager side agents handle IaaS related tasks, like
creating a VM or a network, and binding a floating IP to a VM. Manager side agents can also be
used with other tools such as REST to remotely execute tasks. The application side agents are
optionally located on application VM’s. The user can state in the blueprint which VM’s will have
an agent installed on them. The application side agents are installed by the manager side agent as
part of the VM creation task. Once running, the application side agent can install plugins and
execute tasks locally. Typical tasks will be middleware installation and configuration, and
application modules deployment.
Cloudify Manager deploys and manages applications described in blueprints. The manager’s
main responsibilities are to run automation processes described in workflow scripts and issue
execution commands to the agents. Cloudify is controlled via a REST API. The REST API covers
all the cloud orchestration and management functions. Cloudify’s Web GUI works with the REST
API to add additional value and visibility. Cloudify uses a Workflow engine to allow automation
process through built-in and custom workflows. Workflow engine is responsible of timing and
orchestrating tasks for creating or manipulating the application components. The user can write
custom workflows in Python using API’s that provide access to the topology components.
Figure 4. Cloudify Stack
Cloudify uses different databases as data store, some of the technologies for processing and
messaging, and different servers as front end. Total stack is shown in Figure 4. Cloudify uses
elastic search as its data store for deployment state. The deployment model and runtime data are
stored as JSON documents. Blueprints are stored in the elastic search and it is used as runtime
DB. Cloudify uses InfluxDB as the monitoring metrics repository. Influx provides flexible
schema for metrics and metrics metadata as well as a query language. Cloudify stores every
metric reported by a monitoring tool into influxdb and define time based aggregations as well as
statistic calculations. Clodify uses RabbitMQ task broker for messaging. Cloudify offers a policy
engine that runs custom policies in order to make runtime decisions about availability, service
level agreement, etc. For example, during installation, the policy engine consumes streams of
events coming from monitoring probes or tools. The policy engine analyses these streams to

28
decide if a specific node is up and running and provides the required functionality. Policies are
registered, activated, deactivated and deleted by the Workflow Engine. For logging purpose
logstash is used and agent play main role in processing. Nginx proxy and file server, Flask or
Gunicorn REST server, and Node.js GUI servers can be used as front end in the Cloudify.
5. OPEN SHIFT
OpenShift [15] enables us to create, deploy and manage applications within the cloud. Two basic
functional units of the Openshift are the Broker and Node servers. Communication between the
Broker and Nodes is done through a message queuing service. Broker is the single point of
contact for all application management activities. It is responsible for managing user logins, DNS,
application state, and general orchestration of the applications. Customers don’t contact the
broker directly; instead they use the Web console or CLI tools to interact with Broker over a
REST based API. Nodes are the systems that host user applications. In order to do this, the Nodes
are configured with Gears and Cartridges. The division is shown in Figure 5.
Figure 5. OpenShift
A gear represents the part of the Node’s CPU, RAM and base storage that is made available to
each application. An application can never use more of these resources allocated to the gear, with
the exception of storage. OpenShift supports multiple gear configurations, enabling users to
choose from the various gear sizes at application setup time. When an application is created, the
Broker instructs a Node to create a new gear to contain the application. Cartridges represent
pluggable components that can be combined within a single application. These include
programming languages, database engines, and various management tools. Users can choose from
built-in cartridges that are served directly through OpenShift, or from community cartridges that
can be imported from a git repository. The built-in cartridges require the associated languages and
database engines to be installed on every Node.
6. MORE PAAS FRAMEWORKS
In this section we are going to give brief introduction about Stratos, mOSAIC, IBM BlueMix,
Heroku, Amazon Elastic Beanstalk, Microsoft Azure and Google App Engine, and Stakato PaaS
frameworks.

29
6.1. STRATOS
Apache Stratos [6] is a highly-extensible PaaS framework that helps to run Apache Tomcat, PHP,
and MySQL applications, and can be extended to support many more environments on all major
cloud infrastructures. For developers, Stratos provides a cloud-based environment for developing,
testing, and running scalable applications. In Single JVM deployment model Stratos could
accommodate up to 100 cartridge instances. In a distributed deployment model Stratos could
accommodate up to 1000 cartridge instances.
6.2. MOSAIC
mOSAIC [10] is an open-source API and platform for designing and developing multi-Cloud-
oriented applications. The architecture has been designed with open and standard inter faces. The
main goal is to provide a unified cloud programming interface which enables flexibility to build
applications across different cloud providers. The main middleware components providing
integration features are the Cloudlet, Connector, Interoperability, and Driver API. The Cloudlet
and Connector API layers facilitate the integration into the target language environment which is
used by the developers in their applications. The Driver API layer provides abstraction over
resource allocation on top of the native resource API. Interoperability API is the middleware layer
that integrates the connector API and compatible driver API implementations that could be
written in different languages. It is a remote API that follows the model of RPC with
functionalities including marshalling, request/response correlation, and error detection. Apart
from its cloud integration features, mOSAIC framework is promised to have a semantic-oriented
ontology for describing cloud resources.
6.3. BLUEMIX
Bluemix [16] is the newest cloud offering from IBM. BlueMix allows developers to create,
deploy, and manage applications on the cloud. It is an implementation of IBM’s Open Cloud
Architecture based on Cloud Foundry, an open source Platform as a Service (PaaS). It delivers
enterprise-level services that can easily integrate with cloud applications without requiring to
know how to install or configure them.
6.4. HEROKU
Heroku [17] is a PaaS solution created on top of the Amazon EC2 that allows to deploy, run and
manage applications written in Ruby, Node.js, Java, Python, Clojure, Scala and PHP. It runs on
top of Amazon EC2 VMs and offers a readymade set up environment in which the developer can
upload code, compile it and run it. All these things can be done with some simple commands. The
developer has the opportunity to test and deploy applications without necessary knowledge of
underlying infrastructure. Heroku can manage the resources required to scale the project.
6.5. ELASTIC BEANSTALK
Amazon Elastic Beanstalk [12] supports users to deploy their applications on Amazon Web
Service (AWS) cloud platform. AWS Elastic Beanstalk PaaS framework allows users to create
applications and push them to a definable set of AWS services, including Amazon EC2, Amazon
S3, Amazon Simple Notification Service (SNS), Amazon CloudWatch, auto scaling, and elastic
load balancers. Elastic Beanstalk frameworks support different languages for cloud development.

30
6.6. AZURE
Microsoft Windows Azure [11] allows the application deployment and management on Azure
cloud platform. The Microsoft Azure framework offers a cloud services operating system and a
set of services to support easy development and operation of applications for cloud platform.
Azure provides functionality to build and manage applications that span from consumer Web to
enterprise scenarios.
6.7. APP ENGINE
Google App Engine [18] allows developers to deploy their application on Google cloud platform.
App Engine framework allows developers to use Java, Python and some other languages for
developing their web applications.
6.8. STAKATO
Stackato [7] is open PaaS software based on Cloud Foundry, Docker and other open-source
components. It has multi-tenancy capabilities and can be installed on internal infrastructure or
public cloud. Multi-tenancy capabilities are important because they allow us to run multiple
applications on the same IaaS infrastructure. Stackato allows developers to automatically package
applications into their own Docker containers and scales instances up or down on demand.
Stackato provisions all required components, including languages, frameworks and service
bindings, automates logging and monitoring, allows for automated application versioning and
rollback.
7. COMPARISON OF MAJOR PAAS
In this section major PaaS frameworks comparison is given in the table format. Table 2 shows the
basic functionality and its corresponding AppScale, Cloud Foundry, Cloudify, and OpenShift
architectural components.
Table 2. Open PaaS Components comparison
Functionality AppScale Cloud Foundry Cloudify OpenShift
Core functionality AppController Cloud controller Manager Broker
Providing third party
database services
Database Master Service Broker Agent Cartridge
Routing of incoming
traffic
AppLoadBalancer Router Manager REST API
Querying the state of
apps
AppScale Tools Cloud controller CLI client Broker
Messaging AppController Message Bus Manager Broker
Application instance
management
AppServer Droplet Execution
Agent
Agent Node
Application state
change
AppLoadBalancer Health Manager Manager Broker
Containerization Database Slave Warden Agent Gear
Load balancing of user
requests
AppLoadBalancer Droplet Execution
Agent
Manager Broker
Framework provider AppServer Blob Store Agent Cartridge

31
Table 3 shows the AppScale, Cloud Foundry, Cloudify, OpenShift, Apache Stratos, mOSAIC,
IBM BlueMix, Heroku, Amazon Elastic Beanstalk, Microsoft Azure and Google App Engine
PaaS environment’s supported languages (java, python, ruby, perl, groovy), databases
(MongoDB, MySQL, HBase, PostgreSQL), frameworks (spring, rails, and flask), and
deployments (private or public). In OpenShift, languages and databases are supported in the form
of cartridges. User defined cartridges are also allowed in OpenShift. Cloud Foundry provisions
languages in the form of build packs. Users can also pick to write their own build packs.
Cloudify, Cloud Foundry and Openshift have extensible language support feature. AppScale,
Cloud Foundry, Cloudify, OpenShift, Apache Stratos and mOSAIC are open source cloud
platforms, for this reason their deployment is given as private PaaS. IBM BlueMix, Heroku,
Amazon Elastic Beanstalk, Microsoft Azure and Google App Engine PaaS environments can be
deployed on public cloud and these are not open source, so these things are considered as Public
PaaS.
Table 3. Language, Database and Frameworks supported by open PaaS
PaaS Languages Databases Frameworks Deployment
AppScale
Python, Java, Go,
PHP
Cassandra,
HBase,
Hypertable,
MongoDB,
SimpleDB,
MySQL
Django,
Flask, Spring
Private PaaS
Cloud
Foundry
Java, Ruby, Scala,
Node.js, Groovy,
Grails, PHP, Go,
Python
MonogoDB,
MySQL,
PostgreSQL
Spring,
Rails, Grails,
Play, Sinatra
Private PaaS
Cloudify Java, PHP, Ruby
MySQL,
MongoDB
Not
supported
Private PaaS
OpenShift
Java, PHP, Ruby,
Python, Perl,
JavaScript,
Node.js
PostgreSQL,
MySQL,
MongoDB
Rails, Flask,
Django,
Drupal,
Vert.x
Private PaaS
Stratos Java, PHP MySQL
Not
supported
Private PaaS
mOSAIC Java Not supported
Not
supported
Private PaaS
BlueMix
PHP, Python,
Ruby, Java,
Node.js, Go
Cleardb,
Elephantsql
Sinatra, Rails Public PaaS
Heroku
Scala, Ruby,
Python, PHP,
Node.js, Java,
Groovy, Go,
Clojure
PostgreSQL
Django,
Flask, Rails,
Grails, Play
Public PaaS
Amazon
Elastic
Beanstalk
.Net, Go, Java,
Node.js, PHP,
Python, Ruby
Not supported
Not
supported
Public PaaS
Microsoft
Azure
Java, .Net,
Node.js, Ruby,
Python, PHP
Not supported
CakePHP,
Django
Public PaaS
Google App
Engine
Go, Java, PHP,
Python
Not supported Django Public PaaS

32
Table 4 compares the relational database support, NoSQL Support, horizontal scaling, vertical
scaling, auto scaling, spring framework support, Add-ons and language extensibility features of
AppScale, Cloud Foundry, Cloudify, OpenShift, Apache Stratos, mOSAIC, IBM BlueMix,
Heroku, Amazon Elastic Beanstalk, Microsoft Azure and Google App Engine PaaS Frameworks.
The Table 4 shows “yes” for supported features and “no” for the unsupported features in the
corresponding framework columns.
Table 4. PaaS framework’s Considerable Feature Support
Features
AppScale
Cloud
Foundry
Cloudify
OpenShift
Stratos
mOSAIC
BlueMix
Heroku
Elastic
Beanstalk
Microsoft
Azure
App
Engine
Relational
database
support
Yes Yes Yes Yes Yes No No Yes No No No
NoSQL
database
support
Yes Yes Yes Yes No No No No No No No
Horizontal
Scaling
Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes
Vertical
Scaling
No Yes No Yes No No Yes Yes Yes Yes No
Auto Scaling Yes No Yes Yes Yes Yes Yes No Yes Yes Yes
Spring
Framework
support
Yes Yes No No No No No No No No No
Add-ons
support
No No No No No No Yes Yes No Yes Yes
Language
Extensibility
No Yes Yes Yes Yes No Yes Yes No Yes No
8. CONCLUSIONS
Cloud computing service models like Software as a Service (SaaS), Platform as a Service (PaaS),
and Infrastructure as a Service (IaaS) are introduced in this paper. PaaS is explained in detail with
the help of open PaaS packages like AppScale, Cloud Foundry, Cloudify, and OpenShift.
AppScale components are explained in table format, Cloud Foundry, Cloudify and OpenShift
components are explained in detailed with a diagrams. Apache Stratos, mOSAIC, IBM BlueMix,
Heroku, Amazon Elastic Beanstalk, Microsoft Azure, Google App Engine, and Stakato PaaS
frameworks are also explained in this paper. Comparative study is performed among the
AppScale, Cloud Foundry, Cloudify and OpenShift open PaaS componets. Supported language,
databases, frameworks, and deployments like public PaaS or Private PaaS are also listed for
AppScale, Cloud Foundry, Cloudify, OpenShift, Apache Stratos, mOSAIC, IBM BlueMix,
Heroku, Amazon Elastic Beanstalk, Microsoft Azure and Google App Engine PaaS
environments. Relational database support, NoSQL Support, horizontal scaling, vertical scaling,
auto scaling, spring framework support and language extensibility features are compared among
the AppScale, Cloud Foundry, Cloudify, OpenShift, Apache Stratos, mOSAIC, IBM BlueMix,
Heroku, Amazon Elastic Beanstalk, Microsoft Azure and Google App Engine frameworks.

33
ACKNOWLEDGEMENTS
This research was supported by Basic Science Research Program through the National Research
Foundation of Korea (NRF) funded by the Ministry of Education. (Grant Number: 2011-
0011507).
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AUTHORS
Mohan Krishna Varma Nandimandalam is Completed Bachelor of Computer
Applications degree in 2002 and Received Master of Science in Information Systems
degree in 2004 from Sri Venkateswara University, Tirupati, Andhra Pradesh, India, and
Master of Technology in Computer Science and Engineering in 2007 from VIT University,
India. Currently working as a full time researcher in Distributed information Systems and
Cloud computing lab and doing Ph.D. in Graduate School of Business IT, Kookmin
University, South Korea.
Eunmi Choi is a Professor in the School of Business IT,Chairperson of School of
Management Information Systems, Head of Distributed Information System & Cloud
Computing Lab., and Executive Chief of Business IT Graduate School at Kookmin
University, Korea, Her current research interests include big data infra system and
processing, cloud computing, cyber physical system, information security, distributed
system, SW meta-modelling, and grid & cluster computing. Professor Choi received and MS
and PhD in computer science from Michigan State University, U.S.A., in 1991 and 1997,
respectively, and BS in computer science from Korea University in 1988.

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