1. SCHEDULING IN CLOUD
COMPUTING ENVIRONMENT
Submitted By:
Sakshi Saxena(10103451),
Mayuri Saxena(10104758)
Submitted To: Mr. Prakash Kumar

2. Introduction
What is Cloud Computing?What is Cloud Computing?
• Shared computing resourcesShared computing resources
• As opposed to local servers and devicesAs opposed to local servers and devices
• Made up of Grid InfrastructureMade up of Grid Infrastructure
• ScalableScalable
• VirtualizationVirtualization
• Web applicationsWeb applications
• Specialized raw computing servicesSpecialized raw computing services

3. Introduction 2
Cloud computing refers to both the applications delivered as services
over the Internet and the hardware and systems software in the data
centers that provide those services. With the advent of the Cloud,
new possibilities have arrived to build an application on the internet.
The computing paradigm includes- cluster computing, grid
computing, P2P computing, service computing, market oriented
computing and most recently introduced cloud computing. The
computing facilities however, must be efficient, candid, time-tested
and hefty. Since the customer satisfaction is the foremost priority,
therefore quality of services are provided by two promising
computer paradigms: Grid computing and Cloud computing.
The cloud computing services themselves have long been referred to
as Software as a Service (SaaS). Some vendors use terms such as
IaaS (Infrastructure as a Service) and PaaS (Platform as a Service)
to describe their products. Cloud computing systems promise to
offer subscription-oriented & enterprise-quality computing services
to users worldwide.

4. Cloud Computing ArchitectureCloud Computing Architecture

5. Virtualization
• Virtual Machine (VM)
is a software artifact
that executes other
software as if it was
running on a physical
resource directly.
• Typically uses a
Hypervisor or VMM
which abstracts the
hardware from an
Operating System

6. Problem Statement
Cloud is developing day by day and faces many challenges, one of
them is scheduling. The efficient job scheduling increases the client
satisfaction and utilize the system energy in terms of time. How to
use Cloud computing resources efficiently and gain the maximum
profits with efficient utilization of resources is one of the Cloud
computing service providers’ ultimate goals. Repetitive evaluation of
the performance of Cloud provisioning policies, application workload
models, and resources performance models in dynamic system are
difficult to achieve and rather a time consuming and costly
approach.
The objective of this project is analyzing and evaluating the
performance of various CPU scheduling in cloud environment to
improve the application performance under resource and service
demand variations. We will discuss different Task(cloudlet)
Scheduling Policies in Virtual Machine and their performance
analysis in Virtual environment of cloud computing in order to
achieve better Quality of Service (QoS).

7. Experimental Study
Several simulators have been specifically developed for
performance analysis of cloud computing environments
including CloudSim, GreenCloud, NetworkCloudSim,
CloudAnalyst, EMUSIM and MDCSim but the number of
simulation environments for cloud computing data
centers available for public use is limited. The CloudSim
simulator is probably the most sophisticated among the
simulators overviewed.
A comparative study was done comparing the various
existing algorithms with the proposed algorithm in
CloudSim and the results were illustrated in the form of
graphs.

8. CloudSim
CloudSim ToolKit - CloudSim is a framework developed by the GRIDS
laboratory of University of Melbourne which enables seamless
modeling on designing Cloud computing infrastructures. It provides
basic classes for describing data centers, virtual machines,
applications, users, computational resources, and policies for
management of diverse parts of the system. It provides features for
modeling and creating the data center. By using CloudSim,
developers can focus on particular system design issues of the
infrastructure of cloud system, without getting into the lower level
details related to Cloud-based infrastructures and services.
CloudSim supports various functions of cloud system entities (services,
host, data center, broker, VMs) such as queuing and processing of
events & communication between components.

9. Implementation
Input data, or test data, will be manually, and sporadically, input using the
test data created for the project. This test data contains requests like length
of cloudlets, processing element required by them, priority(default=0), start
time of cloudlets etc. A web interface using MVC framework has been
created with the help of Struts2.0 framework.
HARDWARE INTERFACE
Processor: Core I3 and above
RAM: 1 GB
Disk space: Min. 1Gb
SOFTWARE INTERFACE
Operating System: Windows 7/8
Language: JAVA, Struts 2
Tools: Eclipse Keepler , Apache Tomcat Server
CloudSim Toolkit

10. Use Case Diagram

11. Comparison with Existing
Algorithms

12. Product Functions
This project is implementing following scheduling algorithms on
cloudlets submitted to VM and then comparing the response time
and turnaround time of all the cloudlets.
• Time Shared Scheduling
• Space Shared Scheduling
• Shortest Job First
• First Come First Serve
• Round Robin
• Proposed algorithm

13. User Interface-MVC
Model View Controller or MVC as it is popularly called, is a software design pattern for
developing web applications. A Model View Controller pattern is made up of the
following three parts:
• Model - The lowest level of the pattern which is responsible for maintaining data.
• View - This is responsible for displaying all or a portion of the data to the user.
• Controller - Software Code that controls the interactions between the Model and
View.
MVC is popular as it isolates the application logic from the user interface layer and
supports separation of concerns. Here the Controller receives all requests for the
application and then works with the Model to prepare any data needed by the View.
The View then uses the data prepared by the Controller to generate a final
presentable response. The MVC abstraction can be graphically represented as
follows.

14. Struts 2.0
The model:
The model is responsible for managing the data of the application. It
responds to the request from the view and it also responds to
instructions from the controller to update itself.
The view:
A presentation of data in a particular format, triggered by a controller's
decision to present the data. They are script based templating
systems like JSP, ASP, PHP and very easy to integrate with AJAX
technology.
The controller:
The controller is responsible for responding to user input and perform
interactions on the data model objects. The controller receives the
input, it validates the input and then performs the business
operation that modifies the state of the data model.

15. Novelty
Cloud computing has been one of the fastest growing parts in IT
industry. Simulation based approaches become popular in industry
and academia to evaluate cloud computing systems, application
behaviors and their security. This project compares the existing
scheduling algorithms for cloud computing environment. On the
basis of the results we have designed an efficient scheduling
algorithm for faster execution of jobs submitted to run in a
datacenter, A Virtual Machine is an abstraction of computer
hardware within software. The basic information of virtual machine is
given . It includes process speed, image size, ram size, bandwidth,
and number of CPUs of virtual machine. Also, there are cloudlet
properties such as length, file size and output size. CloudSim uses
these factors to configure datacenters, brokers, and virtual
machines. To simulate the system, startsimulation method is called
in the user code. This method calls other methods such as run,
runStart, runClockTick, and runStop in sequence. As a novelty, this
policy incorporates MIPS rating of virtual machines in the decision
process.

16. Conclusion
In this project we have compared various scheduling algorithm in cloud
computing environment and tried to find an optimized solution which will
overcome the limitations of existing algorithm.
The Round Rubin (RR) job scheduling algorithm considered in this study
distributes the selected job over the available VMs in a round order where
each job is equally handled. The idea of the RR algorithm is that it sends the
selected jobs to the available VMs in a round form. In the First Come First
Serve job scheduling the arrival time of jobs are queued in the order of
which come first.
In Shortest Job First they give more priority to small jobs, medium and long
jobs are executed after the execution of small jobs. Fragmentation occurs at
many stages leads to waste of energy and increase the cost of customer on
pay per use. In space shared the form of either VM Scheduler Space
Shared or Cloudlet Scheduler Space Shared. It means that if there are more
running VM’s or Cloudlets than available PEs, the last elements to arrive
wait on a queue until enough resources are free. Cloudlet Scheduler Time
Shared fraction of available PEs are shared among running elements, and
all the elements run simultaneously.

17. References
• A.K. Amoura, E. Bampis, C. Kenyon, and Y. Manoussakis, “Scheduling Independent Multiprocessor Tasks”,
Algorithmica, vol. 32, pp. 247–261, 2002.
• Burya R Raman, R. Calheiros, R.N.(2009) “Modeling and Simulation of Scalable Cloud Environment and the
Cloud Sim Toolkit: Challenges and Opportunities’’, IEEE publication 2009,pp1-11
• B.Furht, and A. Escalante, “Handbook of cloud computing,” Cloud computing fundamentals chapter writen by B.
Furht, Springer, 2010.
• CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource
provisioning algorithms Rodrigo N. Calheiros1, Rajiv Ranjan2, Anton Beloglazov1,C´ esar A. F. De Rose3 and
Rajkumar Buyya1.
•
• G. Raj , S. Setia “Effective Cost Mechanism for Cloudlet Retransmission and Prioritized VM Scheduling
Mechanism over Broker Virtual Machine Communication Framework ,” International Journal on Cloud Computing:
Services and Architecture(IJCCSA),Vol.2, No.3, June 2012, pp. 41-50.
• M. Gahlawat , P. Sharma “Analysis and Performance Assessment of CPU Scheduling Algorithms in Cloud using
Cloud Sim,” International Journal of Applied Information Systems (IJAIS) Volume 5 - No. 9 , July 2013 , pp. 5-8.
• Pinal Salot, “A Survey of various Scheduling Algorithm in Cloud
• Computing Environment,” IJRET, vol. 2, pp.131–135, 2013.

18. Thank You !!!