Optimization of FCFS based Resource provisioning algorithms for Cloud Computing

Guided by Prof. Lakshmi Kurup

Aditya Marphatia Dept. of Computer Engineering

DJSCOE Mumbai, India

adityamarphatia@gmail.com

Aditi Muhnot Dept. of Computer Engineering

DJSCOE Mumbai, India

aditimuhnot@gmail.com

Tanveer Sachdeva Dept. of Computer Engineering

DJSCOE Mumbai, India

tanveersachdeva@gmail.com

Esha Shukla Dept. of Computer Engineering

DJSCOE Mumbai, India

eshashukla91@gmail.com

Abstract In our project, we propose an optimized version of the FCFS scheduling algorithm addresses these major challenges of task scheduling in cloud. The incoming tasks are grouped on the basis of task requirement like minimum execution time or minimum cost and prioritized (FCFS manner). Resource selection is done on the basis of task constraints using a greedy approach. The proposed model will be implemented and tested on simulation toolkit. We intend to create a module depicting the normal FCFS algorithm in comparison to our optimized version algorithm for resource provisioning in the cloud.

Index TermsCloud computing, FCFS, module, resource, scheduling

I. INTRODUCTION Cloud computing has emerged as a popular computing

model to support on demand services. It is a style of

computing where massively scalable resources are

delivered as a service to external customers using Internet

technologies. Scheduling in cloud is responsible for

selection of best suitable resources for task execution, by

taking some static and dynamic parameters and restrictions

of tasks into consideration. The users perspective of

efficient scheduling may be based on parameters like task

completion time or task execution cost etc. Service

providers like to ensure that resources are utilized

efficiently and to their best capacity so that resource

potential is not left unused.

Much of the research so far has been focused on Cloud

security and data protection aspects, therefore the efficient

provisioning of resources and necessity of cloud services to

be cost effective is often neglected. In our project, we

propose an optimized version of the FCFS scheduling

algorithm which addresses these major challenges of task

scheduling in cloud. The incoming tasks are grouped on the

basis of task requirement like minimum execution time or

minimum cost and prioritized (FCFS manner). Resource

selection is done on the basis of task constraints using a

greedy approach. The proposed model will be implemented

and tested on simulation toolkit. We intend to create a

module depicting the normal FCFS algorithm in

comparison to our optimized version algorithm for resource

provisioning in the cloud.

II. PROBLEM DEFINITION WITH SCOPE OF PROJECT While current cloud systems are beginning to offer

the utility-like provisioning of services,

provisioning of resources has to be controlled by

the end users. This, however, is a new and

unfamiliar paradigm for computer application

developers and users, who are accustomed to

working with a fixed set of resources they own.

Resource cost is an important consideration while

using cloud resources for scientific computing as

well.

Resource provisioning algorithms have certain

budget constraints and the implementation of the

cloud on a large-scale is a very costly affair.

Cloud security has been a major issue uptil now,

with the privacy of the databases being stored on

the cloud being under threat. The effectiveness and

efficiency of traditional protection mechanisms are

being reconsidered as the characteristics of this

innovative deployment model can differ widely

from those of traditional architectures.

There can be better more-costly implementable

resource provisioning algorithms such as the

probability dependent priority used by Ye Hu over

the preference of the FCFS scheduling. However it

requires many more factors to be considered and

the cost rises up.

III. PROPOSED SYSTEM The different resource provisioning algorithms

mentioned have certain flaws on which work could

be done in this direction to extract the advantageous

points of these algorithms and come up with a better

solution that tries to minimize the drawbacks of

resultant algorithm.

The algorithm devised by us, will be using various

current resource provisioning strategies. There are

two ways of choosing the deciding parameters for the

implementation of the proposed algorithm:

1. Deadline Constrained

2. Cost Constrained

Outline of the Proposed Algorithm:

1. Task Grouping: Grouping means collection of

components on the basis of certain behaviour or

attribute. By task grouping in cloud it is meant

that tasks of similar type can be grouped together

and then scheduled collectively. In the proposed

framework tasks are grouped on the basis of

constraint which can be deadline or minimum

cost. Grouping, if employed to combine several

tasks, reduces the cost-communication ratio.

2. Prioritization: Priority determines the importance of the element with which it is

associated. In terms of task scheduling, it

determines the order of task scheduling based on

the parameters undertaken for its computation. In

the present framework, the task list is rearranged

with tasks arranged in ascending order of

deadline in order to execute the task with

minimum time constraint first. Hence, they are

given more priority in scheduling sequence. The

cost based tasks are prioritized on the basis of

task profit in descending order. This is

appreciable as tasks with higher profit can be

executed on minimum cost based machine to

give maximum profit.

3. Greedy Allocation: Greedy algorithm is suitable

for dynamic heterogeneous resource

environment connected to the scheduler through

homogeneous communication environment.

Greedy approach is one of the approaches used

to solve the job scheduling problem.

Greedy algorithms look for simple, easy-to-

implement solutions to complex, multi-step

problems by deciding which next step will

provide the most obvious benefit. Such

algorithms are called greedy because while the

optimal solution to each smaller instance will

provide an immediate output, the algorithm

doesnt consider the larger problem as a whole.

Different Parameters for Greedy Approach

Realization

i. Deadline Constrained Based: To improve

the completion time of tasks greedy

algorithm is used with aim of minimizing

the turnaround task of individual tasks.

ii. Minimum Cost Based: The resource with minimum cost is selected and tasks are

scheduled on it until its capacity is

supported.

The Algorithm can be implemented using Java.

IV. FEASABILITY OF PROPOSED SYSTEM This being a cloud computing project, the major

feasibility issues will be in the efficient

implementation of the cloud environment and in the

simulation of the proposed algorithm to have better

utilization of available resources.

Economic Feasibility: Economics of this project

need to be understood from the development of the

cloud environment perspective.

Cloud Feasibility: An understanding of where you

are and what your objectives mean. We will

determine what hardware and applications can be

optimized through virtualization, a fundamental

cloud component. Comprehensive analysis not only

identifies benefits, risks and gaps, but also determines

the financial impact on the project.

The various factors to be kept in mind when studying

the feasibility of the cloud are:

1. High availability designed to the highest

specifications for resiliency to minimise

downtime enabling SLA based services to be

delivered to the customer;

2. Efficiency The underlying technology focuses

on maximising the utilisation of computing

power and disk storage resulting in less wasted

resource.

3. Flexibility - Customers need only to buy what

they require today rather than buying an IT

solution that they will have to grow into

overtime and then provision extra resource as

and when it is needed.

Technological and System feasibility: An error-free

simulation analysis is very important point that needs

to be considered

On cloud side, this project can face obstructions as it

deals with interfacing with heterogeneous

environments. The ACID properties of the cloud

database need to be satisfied. But still this should not

be a major concern.

Operational Feasibility:

The algorithm that is proposed should have a

considerable positive impact on the resource

provisioning to both parties i.e. the client and the

cloud service provider.

Currently there is an existing toolkit that depicts the

different resource provisioning algorithms called as

CloudSim: an extensible simulation toolkit that

enables modeling and simulation of Cloud computing

systems and application provisioning environments.

The CloudSim toolkit supports both system and

behavior modeling of Cloud system components such

as data centers, virtual machines (VMs) and resource

provisioning policies. The different resource

provisioning algorithms are compared using this

simulator software and it is proved that the proposed

algorithm is better in resource utilization than other

algorithms.

V. ASSUMPTION Our work is based on the assumption that fine-

grained allocation and pricing of resources is possible

for each virtual environment. This is not true for the

existing cloud environments, but is consistent with

the utility vision of computing, and recent research

points to the progression of clouds in such a

direction. Thus, we assume that CPU cycles and

memory can be shared in a fine-grained fashion

between different instances. The user of an instance

can request for a different CPU cycle percentage or

memory allocation at any point during its execution.

Additionally, while evaluating the efficiency of the

proposed algorithm in comparison with other

algorithms assumes that he jobs arrival is Uniformly

Randomly Distributed to get generalized scenario.

ACKNOWLEDGMENT We wish to thank Prof. Narendra Shekokar and our guide

Prof. Lakshmi Kurup for their continued encouragement and support and the Dept. of Computer Engineering for providing us with the resources and infrastructure to undertake this project.

REFERENCES

IEEE/ACM INT. CONF. ON GRID COMPUTING (GRID 2008)

85-94, 2008.[11]GROSU, D., DAS, A., AUCTION-BASED RESOURCE ALLOCATION PROTOCOLS IN GRIDS, 16TH IASTED INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED COMPUTING AND SYSTEMS, 2004.

[12] IZAKIAN, H., LADANI, B.T., ZAMANIFAR, K., ABRAHAM, A.,SNASEL, V., A CONTINUOUS DOUBLE AUCTION METHOD FORRESOURCE ALLOCATION IN COMPUTATIONAL GRIDS, IEEESYMPOSIUM ON COMPUTATIONAL INTELLIGENCE INSCHEDULING, 2009, PP. 29-35, 2009.

[13]H. IZAKIAN, A.ABRAHAM, B. TORK LADANI . AN AUCTION FOR RESOURCE ALLOCATION IN COMPUTATIONAL GRIDS .FUTURE GNERATION COMPUTER SYSTEMS VOL 26 ,PP 228, 2011.

XIAOHONG WU AT LA.. CLOUD COMPUTING RESOURCEALLOCATION MECHANISM RESEARCH BASED ON REVERSEAUCTION . . ENERGY PROCEDIA VOL 13, 2011 ,PP 736741

[15] I. FUJIWARA . APPLYING DOUBLE-SIDED COMBINATIONALAUCTIONS TO RESOURCE ALLOCATION IN CLOUDCOMPUTING .

IEEE 10TH ANNUAL INTERNATIONAL SYMPOSIUM ONAPPLICATIONS AND THE INTERNET. 2010 .

[16] ADABI, S., MOVAGHAR, A., RAHMANI, A., M., BEIGY, H.,MARKET-BASED GRID RESOURCE ALLOCATION USING NEW NEGOTIATION MODEL, JOURNAL OF NETWORK ANDCOMPUTER APPLICATIONS, 2012. [17]S. TEYMOURI, A. M. RAHMANI. A CONTINUES DOUBLEAUCTION METHOD FOR RESOURCE ALLOCATION IN ECONOMIC

Grids, International Journal of Computer Applicationvol 43, 2012, pp 7-12, 2012..

[20] JADE, JAVA AGENT DEVELOPMENT FRAMEWORK, 2004.HTTP://JADE.CSELT.IT