PROPOSED ONTOLOGY FRAMEWORK FOR DYNAMIC RESOURCE PROVISIONING ON PUBLIC CLOUD

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International Journal of Advanced Research in Engineering and Technology

(IJARET) Volume 7, Issue 2, March-April 2016, pp. 118–131, Article ID: IJARET_07_02_012

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___________________________________________________________________________

PROPOSED ONTOLOGY FRAMEWORK

FOR DYNAMIC RESOURCE

PROVISIONING ON PUBLIC CLOUD

Lata Gadhavi

Research Scholar, Department of Computer Science and Engineering,

Institute of Technology, Nirma University, Ahmedabad

Vishakha Modi

Student, Department of Computer Science and Engineering, SPBPEC,

Saffrony Institute of Technolopgy, Mehsana, Gujarat

Madhuri Bhavsar

Professor, Department of Computer Science and Engineering,

Institute of Technology, Nirma University, Ahmedabad

ABSTRACT

Cloud computing is an essential ingredient of today’s modern information

technology. Cloud computing is totally based on internet. With the use of

cloud computing resources can be shared from anywhere and anytime. In

cloud computing there are multiple users simultaneously requests for the

number of services and its important to provision all resources to user in

efficient manner to satisfy their requirements. To come out this problem in this

paper we had reviwed the different types of resource allocation strategies and

proposed an ontology based resource management framwork for dynamic

resource allocation. Ontology Framework contain four sections, each section

equipped with functionality to collect information regarding all resources

available in actual cloud deployment based on signed SLA agreement, and

then replies to the user with appropriate allocation.

Key words: Cloud Computing, Resource Allocation, Resource Management,

Ontology

Cite this Article: Lata Gadhavi, Vishakha Modi and Madhuri Bhavsar.

Proposed Ontology Framework for Dynamic Resource Provisioning on Public

Cloud. International Journal of Advanced Research in Engineering and

Technology, 7(2), 2016, pp. 118–131.

http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=7&IType=2

Proposed Ontology Framework for Dynamic Resource Provisioning on Public Cloud


1. INTRODUCTION

Cloud computing is the common buzzword in today’s internet world. The National

Institute for Standards and Technology has defined “It is a model which enables

ubiquitous, convenient, on-demand network access to a shared pool of configurable

computing resources like networks, servers, applications and services that can be

rapidly provisioned and it released with minimal management effort or service

provider interaction”. This cloud model has five characteristics are On-demand self-

service, broad network access, resource pooling, rapid elasticity and measured

Service. As shown in figure1, there are three service models including IaaS, PaaS and

SaaS. SaaS is a software as a service delivered the software over browser and there is

no need to install and run applications on the customer’s computers. PaaS is a

platform as a service provider on which build, run, test and develop applications using

languages, libraries, services and tools supported by the provider [1]. IaaS is

infrastructure as a service providing the fundamental building block of cloud

resources as a service. It virtualized the resource like memory, processor, network,

CPU etc. There are four deployment models in cloud computing are private cloud,

public cloud, hybrid cloud and community cloud.

Figure1 Cloud Computing Infrastructure

Cloud computing is a model of service delivery and access where dynamically

scalable and virtualized resources are provided as a service over the Internet. The goal

of cloud computing is to provide on-demand computing services with high reliability,

scalability, and availability. One of the important requirements in cloud computing is

improve the performance of QoS parameters like response time, latency, throughput

etc.. All the QoS constraints, like throughput, response time and latency are dependent

on the mechanism of efficient resource allocation. In this paper we proposed an

ontology based resource management framework for the efficient dynamic resource

allocation in cloud computing. There are seven components which perform the job

Lata Gadhavi, Vishakha Modi and Madhuri Bhavsar


allocation task in ontology based resource management system in cloud computing.

These components are job queue, job scheduling module, cloud ontology, VM

allocation module, VM monitoring module, fault tolerant module and accounting

module.

We discussed essentials of cloud computing in section II as Domain Analysis, in

Section III as Related Work, in Section IV as proposed architecture, in section V we

described ontology framework and in section VI concluded with future directions.

2. DOMAIN ANALYSIS

Resource allocation is one of the biggest challenge in cloud computing. In resource

management the main task is to allocate the appropriate resources to user according to

service level agreement which is initial agreement between cloud service provider and

cloud users. Resource allocation can be done by statically and dynamically on the

basis of the requirements of the user. Static resource allocation and dynamic resource

allocation of resources make use of the resources without violating SLA and it

satisfies the QOS parameters.

Resource allocation process can be divided in two levels. At the first level when

any application is come in the cloud, the load balancer who manages the load of the

system take this request and assign it to the physical machine. In second level when

any application receives multiple incoming requests, these requests should be assign

to a specific requested application .Example of Amazon EC2 is the example of same

process which uses elastic load balancing technique so it can scale up and scale down

resources on the basis of the requirements of customer. The main advantage of cloud

computing is no need to install any software and hardware to access the application,

to develop the any application and to host the application over the Internet in cloud

computing. In cloud compouting there are so many chances for hacking and phishing

attack on client data when we used public cloud.

In efficient resource allocation following types of situation needs to avoid [2]:

Resource Contention: This situation is created when there are two application wants

to access the same resources at the same time.

Resource Fragmentation: This situation is created when there are two or more

resources are isolated means there are so many resources available but lack of

adaptivness, they do not meet their requirements.

Scarcity of resources: This type of situation is created when there are limited

resources are available.

Over-Provisioning: This type of situation is created when the application gets more

resources than the demands.

Under-Provisioning: This type of situation is created when the application do not get

the requested resources because of fewer numbers of resources available.

TYPES OF RESOURCE ALLOCATION [2]

Resource provisioning can be classified in two type as below:

Static Provisioning: Applications which have unchanging requirements, on that time

static provisioning is used. In static provisioning the cloud service user make

contracts with cloud service provider, and on the basis of this contract cloud service

provider prepares the resources in advance before the service start. In static

provisioning cloud service user has to pay on monthly basis.



Dynamic Provisioning: Applications which have changing requirements ,on that

time dynamic provisioning techniques is used in which virtual machines can be

migrated to new compute nodes within the cloud. In dynamic provisioning ,if any

user need more than one resources it can be provided by service provider and if any

user do not need any resource it can be removed by service provider means in

dynamic provisioning customer is billed on a pay per use basis..

3. RELATED WORK

Resource provisioning techniques are used for efficient use of the cloud resources.

There are so many resource provisioning techniques are available. The resource

provisioning techniques are used for improve the performance of QOS parameters

(response time, throughput, availability, reliability etc), minimize the cost for cloud

user and maximize the resource utilization for cloud service provider, reduce the

power consumption and reduce the SLA violation. In this paper we had reviewed

various papers for dynamic resource allocation techniques in cloud computing and

these all are listed below.

Aman Kumar et al. in [3], presented the SLA aware an efficient Agent based

framework for resource allocation on SaaS level in cloud computing. This mechanism

contains five different agents and each agent performs the task to collect the

information about the all resources which are available in cloud based on Service

Level Agreement and then it replies to cloud user on basis of their requirements. In

this paper author had done resource computation and scheduling by using the vector

space model which retrieves the information. This model computes the similarity

coefficient based on two dimensional input set where one is set by cloud provider and

second is set by cloud user .In this paper author had work on SaaS level for reducing

the cost of building infrastructure through virtualization of resources.

Philipp Hoenisch et al. in [9], proposed an approach for automatically lease and

release the cloud resources based on knowledge of the current and future

requirements. In this paper author had proposed ViePEP called the Vienna Platform

For Elastic Process, for the self adaptive resource allocation for elastic process

execution. This ViePEP has been extended by proposing a prediction and reasoning

algorithm for elastic process execution. The reasoner is responsible for schedule the

services and allocates cloud resources to users on basis of their requirements in a cost

efficient way. Author had only proposed this method to scale in a horizontal way by

adding more virtual machines to the system.

Zhijia Chen et al. in[8], presented the self-adaptive prediction method using

ensemble model and subtractive-fuzzy clustering based fuzzy neural network for

dynamic and efficient resources allocation. Accurate resource demands prediction is

very important for efficient and dynamic resource allocation. For this purpose author

had analyzed the characters of users requirements and on basis of it prediction model

is constructed. They had adopt some predictors to compose the ensemble model and

the structure and learning algorithm for fuzzy neural network is researched. This

paper proposed the fuzzy c-means which is combined with the subtractive clustering

algorithm which is the subtractive-fuzzy clustering. In this paper results shows that

the method is effective for predicting the resources which demanded by user.

Weiwei Lina et al. in [11], proposed the dynamic resource allocation at the

application level, instead of studying how to map the physical resources to virtual

resources for better resource utilization in cloud computing. Author proposed a

threshold based dynamic resource allocation in which resources are dynamically



allocates the virtual resources among the application based on their requirements. To

come out this problem author had proposed Threhold based approach. Threshold

based dynamic resource allocation scheme is implemented by using Cloud Sim in this

paper. The experimental results of this paper show that the proposed method improve

the performance and also reduces the user cost.

Michael R. Head et al. in [10], proposed Virtual Hypervisor which allows the

solution managers to take improved decision for resource allocation regarding their

virtual machines. Cloud manger work as ultimate physical resource manager. This

paper introduced the novel resource allocation algorithm which represents how the

virtual hypervisor abstraction can be fully awared by the global cloud manager.

Author had proposed simulations to achieve fairer resource sharing and isolation

across different Virtual Hypervisor.

Rustem Dautov et al. in [6], proposed an ontology-driven approach to create a self

managing cloud platform. This ontology-driven approach is build on IBM’s MAPE-K

refrence model. MAPE-K model is used for designing closed adaption loops. In this

paper Author had introduced the basic concept of autonomic computing and ontology.

This paper presents how ontology and rules can be utilized to represent self –

reflective knowledge and define adaption policies.

Rui Han et al. in [7], proposed an elastic scaling approach which can be used for

detecting the cost aware criteria and also analyze the bottlenecks within multitier

cloud based application. In this paper, an adaptive scaling algorithm is used that

allows the cloud user to scale their application only at bottleneck tiers and represents

an intelligent platform that can automates the scaling process and because of that

reduces the costs for users of cloud infrastructure services .This approach is generic

for a wide class of multi-tier applications and had demonstrated its effectiveness

against other approaches.

Dr. Abeer Tariq AL_Obaidy et al. in [4] proposed cloud computing infrastructure

for the implementation of multi-agent based application. This multi agent based

applications make cloud adaptive means it becomes more flexible, autonomous. In

this paper author had used ontology by which agents can take wise decision. With the

use of multi agent system if any type of changes occurred in cloud computing

environment it can be easily handled by them. Information transferred between

different agents as java object and their no need to parse the string every time because

of that it reduce the response time and so many users can request for services.

4. PROPOSED ARCHITECTURE

When any cloud user wants to process any job, cloud service provider constructs

virtual machines called resources and these virtual machines provided to cloud user as

a service. These all virtual machines have characteristics which related to QoS. In this

mechanism the first step is the creation of SLA between the cloud user and cloud

service provider. This SLA agreement represents the properties of cloud resources and

the requirements of a cloud user and guarantees the quality of service to the cloud

user. The cloud service provider dynamically allocates a job to a specific virtual

machine based on SLA. To realize resource management in this paper we present an

architecture using ontology for cloud computing as shown in figure 2.



Figure 2 Ontology based resource management in cloud computing

In the above figure 2, cloud users sends requests for the resources to the cloud

system and to evalute the resources ontology based job allocation algorithm is

applied. The cloud service provider called administrator uses the web portal to create

a user account and register the QoS parameters and SLA. This all configuration are

save in system database and. In this mechanism resource controller or agent

continuously monitors the infrastructure and it collects the information which is

stored in ontology model. In this work we used optimization algorithm which analyze

the data and decide user’s requirements on basis of it is reconfigure if necessary to

distribute resources based on service level agreement.

5. ONTOLOGY FRAMEWORK

We proposed ontology framework which deals with information that is related to

cloud computing. The ontology word represents the meaning like the study of being

or existence [8]. The use of ontology defines the relationships and classifications of

concepts within a specified domain of knowledge. The use of ontology fall into an

infinite loop during the process of inference. In cloud computing the requirements of

cloud users are continuosly changed. To satisfies the requiremnts of users and to

improve the performance of QoS parameters, we proposed an ontology model which

deals with the dynamic resource allocation in cloud computing. In resource allocation

process it is very important to consider which type of resources is required and from

where these request are came.

Different phases we described as per below:

Service requirements: In this phase the number of resources needed by the users are

must be known by training datasets. All the requirements of users first recorded in

this database



Figure 3 Ontology Framework

Service discovery: It finds the resources which recorded in database in service

requirement phase.

Service composition: In this phase different resources provided by semantic cloud are

combined and delivered to the users application.

Service consumption: After the allocation of resources to the application, user

consumed resources as per their need and if it is not required then free it.

5.1 Job Allocation Process in the Ontology

As shown in the figure 4, interaction of various components are described using

sequence diagaram. To present the flow of resource managemnet various components

like job queue, job scheduling module, cloud ontology, VM allocation module, VM

monitoring module, fault tolerant module and accounting module are included in the

figure 4.

The job queue component stores the request which is coming from cloud users in

consecutive order. The task of job scheduling module to create a schedule in order to

assign jobs to VMs. Cloud ontology defines concepts underlying the proposed system

for cloud computing and describe their relations. The VM allocation module performs

the task of allocating VMs to physical resources after the job are scheduled and

assigned to each VM. The VM monitoring component monitors the state of virtual

machines and their resource information. This information is transmitted to cloud

ontology which performs job scheduling. If any failure is shown, the VM monitoring

module informs to the fault tolerance module .The task of fault tolerance modul to

find an alternative resource and transmits the feed back to the VM monitoring

module. The accounting module checks the users requirements based on SLA and

bills costs of cloud service usage. Virtual machines are composed of resources from

physical machines and performes dynamic resource allocation.



Figure 4 Job Allocation Process in ontology

5.2 System Algorithm

Ri User request;

Vm Virtual machines;

Tr threshold value;

Or Resource ontology;

Intialize job schedular

Init();

Vm has no of virtual machines={Vm1,Vm2,.........Vmn};

Tr[ ]=threshold value of {Vm1,Vm2,......Vmn};

Ri[ ]= no of user request ;

Or[ ]= resource prediction via ontology;

Check predicted resources & relations and match with current infrastructure(check the

no of Vm in particular regions or cluster)

if No of requests > no of available Vm then

Scale up Vm

Else

Scale down Vm

In scale down process check current usage

If current CPU utilization ==0 then

scale down the server

else

refresh no of Vm

For Monitoring Current infrastructure

Vm has no of virtual machines Vm1,vm2,.....Vmn;

Vector [ ] thershold valueof {Vm1,Vm2.....Vmn}



For i=0 to n virtual machines Vm

If no of virtual machines Vm > threshold value th

Scale up Vm

Else if no of virtual machines Vm < threshold value th

Allocate virtual machines to User request Ri

Else refresh and wait for next requests

End if

6. THE SETUP

To implement and validate the proposed framework functionality initially, we code

this framework in ECLIPSE MARS.2 and used public cloud called Amazon web

service Amazon EC2. The machine which hosts the public cloud is having

configuration, a minimum of 500GB disk space and 8GB RAM to deploy the Amazon

Web Service public cloud with windows 8 operating system. The current

implementation block view is shown in below figure 6.

Figure 6 Block diagram of the setup

This Activity block diagram applied for the efficient dynamic resource allocation

using ontology and displayed following results.

Figure 7 Values for QoS parameters



Above figure 7 represents, efficient dynamic resource allocation on public cloud

(Amazon web services). Here we considered the evalution parameters like response

time, network latency and throughput. It checks the total CPU utilization to scale up

and scaledown instances. It shows CPU utilization 0% and based on it instance is

scaled down.

As shown in figure 8 ,one instance is being stopped after scale down and only one

instance is in running state.

Figure 8 Scale down instance

The figure 9 shows output for QOS parameters values which repeat after fix time

span. Here CPU utilization is still 0%, so again scale down one instance. There is only

one instance is in running state, so no need to scale down the instance .

Figure 9 Value of QoS parameters repeat after fix time span



As shown in below figure 10 and 11 there are no of requests coming from

different regions and count how many requests are coming from different regions.

These all requests are stored in database

.

Figure 10 No of Request from different regions



Figure 11 No of Request from different regions

The below figure 13, represents howmany percentage CPU utilization is done in

last one hour after every 5 minute..

Figure 13 CPU utilization (percentage) Figure 14 Network in (Bytes)

The Figure 14 and Figure 15 represents the how many bytes are come in and goes

out of network for last one hour after every 5 minutes gape.



Figure 15 Network out (Bytes)

7. CONCLUSION AND FUTURE WORK

In this paper, we had presented two types of efficient resource provisioning

techniques, one of them is Static resource provisioning and second one is dynamic

resource provisioning techniques. We introduced an idea of how ontologies and rules

could be utilized in order to represent the internal knowledge of the platform to

provide reasoning over prior knowledge. With the use of ontologies, we can provide

benefits like automation, reliability and separation of concerns. To improve the

performance of public cloud, we implemented ontology method which considered

QoS parameters for specific application and manipulated comparison between

conventional and dynamic cloud enviornment. Proposed ontology algorithm will be

implemented on cloud environment for dynamic resources provisioning to the specific

applications.

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PROPOSED ONTOLOGY FRAMEWORK FOR DYNAMIC RESOURCE PROVISIONING ON PUBLIC CLOUD

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