Optimal Resource Allocation Technique (ORAT) for Green Cloud Computing - Research Paper Notes | EduRev

: Optimal Resource Allocation Technique (ORAT) for Green Cloud Computing - Research Paper Notes | EduRev

 Page 1


International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
20 
Optimal Resource Allocation Technique (ORAT) for 
Green Cloud Computing 
 
K. L. Giridas 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu-629 180. 
 
 
Shajin Nargunam 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu- 629 180. 
 
 
ABSTRACT 
As the IT trade progress towards game-changing expertise, a 
cloud Eco-system is gradually increasing in the country with 
expertise corporation ramping up employing and guiding for 
cloud computing. An accomplishment of green IT is probable 
to assist an organization in several ways like operating cost, 
stakeholder value, sustainability, employee morale and so on.  
Cloud computing might hoist privacy and security concerns 
but this could have one obvious advantage, far enhanced 
energy efficiency. The previous work studied about the 
accumulation of e-waste by integrating the old and mid-range 
processors with modern processors but the care about resource 
efficiency is less. In this paper, proposes an optimal resource 
allocation method for cloud computing environments. This 
paper progress a resource allocation representation of green 
cloud computing environments, considering both bandwidth 
and processing capability, allocated concurrently to every 
service request and returned it on an hourly basis. The owed 
resources are committed to every service request. It is 
established by simulation evaluation that the proposed ORAT 
method can diminish the request loss possibility and therefore, 
decrease the total resource obligatory, compared with the 
predictable allocation method. Through the optimal resource 
allocation, the resources for the tasks are allocated for cloud 
computing environment by eradicating e-waste and make IT 
as Green IT. The proposed Optimal resource allocation 
technique (ORAT) for cloud computing in Green IT can be 
implemented in CloudSim software, and various performance 
characteristics can be simulated to estimate the performance 
of the proposed ORAT in terms of processing ability, resource 
utilization, bandwidth. 
General Term 
Cloud Computing, Green Computing. 
Keywords 
Cloud computing environment, Green IT, resource allocation, 
Optimization. 
1. INTRODUCTION 
Cloud Computing is one of the mainly admired subject in the 
ICT sector nowadays. Cloud computing means incorporated, 
active infrastructures that carry IT as a service moreover 
inside (private cloud) or on the outside (public cloud). It is 
significant to recognize the trade-offs between Software as a 
Service (SaaS), Platform as a Service (PaaS), and 
Infrastructure as a Service (IaaS), and among private and 
public clouds. Envision the potentials for the association if 
you may possibly persist to construct virtualized environment 
into an entirely mechanized, service- oriented transportation 
of collective resources (storage, server, and network) that 
permits to simply distribute IT Services to interior users.  The 
cloud computing types is illustrated in fig 1. 
There are at any rate three merits to optimizing the 
employment of a shared IT environment:   
• Incredible agility   
• Intense efficiency   
• Highest exploitation  
  
 
 
 
 
 
 
 
 
 
 
 
Fig 1: Cloud computing types 
It combines cloud computing environment, cooling, saving 
power, and space, with money. Cloud presents a future-proof 
proposal that can develop no wildly as commerce requires. 
This indicates that you can revolve out novel applications 
sooner, be more receptive to client needs, and decrease IT 
costs on a huge scale by organizing a greatly competent 
infrastructure. The cloud computing construction is included 
of two considerable parts: the front end and the back end. The 
front end is the region at which the customer of the computer 
or the consumer himself is capable to access. The cloud 
computing outline might have predictable two principal 
apprehensions with the use of the cloud computing platform: 
privacy and security. Green Computing is particularly 
significant and appropriate: as computing develops into 
gradually more persistent, the energy consumption 
attributable to evaluating, regardless of the clarion identify to 
diminish utilization and turn around greenhouse effects. This 
is forcing the IT influential to hub on competence and total 
cost possession, predominantly in the framework of the 
world-wide economic crisis.   
                      
CLOUD 
Hybrid 
Public 
cloud 
 
Private 
cloud 
Page 2


International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
20 
Optimal Resource Allocation Technique (ORAT) for 
Green Cloud Computing 
 
K. L. Giridas 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu-629 180. 
 
 
Shajin Nargunam 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu- 629 180. 
 
 
ABSTRACT 
As the IT trade progress towards game-changing expertise, a 
cloud Eco-system is gradually increasing in the country with 
expertise corporation ramping up employing and guiding for 
cloud computing. An accomplishment of green IT is probable 
to assist an organization in several ways like operating cost, 
stakeholder value, sustainability, employee morale and so on.  
Cloud computing might hoist privacy and security concerns 
but this could have one obvious advantage, far enhanced 
energy efficiency. The previous work studied about the 
accumulation of e-waste by integrating the old and mid-range 
processors with modern processors but the care about resource 
efficiency is less. In this paper, proposes an optimal resource 
allocation method for cloud computing environments. This 
paper progress a resource allocation representation of green 
cloud computing environments, considering both bandwidth 
and processing capability, allocated concurrently to every 
service request and returned it on an hourly basis. The owed 
resources are committed to every service request. It is 
established by simulation evaluation that the proposed ORAT 
method can diminish the request loss possibility and therefore, 
decrease the total resource obligatory, compared with the 
predictable allocation method. Through the optimal resource 
allocation, the resources for the tasks are allocated for cloud 
computing environment by eradicating e-waste and make IT 
as Green IT. The proposed Optimal resource allocation 
technique (ORAT) for cloud computing in Green IT can be 
implemented in CloudSim software, and various performance 
characteristics can be simulated to estimate the performance 
of the proposed ORAT in terms of processing ability, resource 
utilization, bandwidth. 
General Term 
Cloud Computing, Green Computing. 
Keywords 
Cloud computing environment, Green IT, resource allocation, 
Optimization. 
1. INTRODUCTION 
Cloud Computing is one of the mainly admired subject in the 
ICT sector nowadays. Cloud computing means incorporated, 
active infrastructures that carry IT as a service moreover 
inside (private cloud) or on the outside (public cloud). It is 
significant to recognize the trade-offs between Software as a 
Service (SaaS), Platform as a Service (PaaS), and 
Infrastructure as a Service (IaaS), and among private and 
public clouds. Envision the potentials for the association if 
you may possibly persist to construct virtualized environment 
into an entirely mechanized, service- oriented transportation 
of collective resources (storage, server, and network) that 
permits to simply distribute IT Services to interior users.  The 
cloud computing types is illustrated in fig 1. 
There are at any rate three merits to optimizing the 
employment of a shared IT environment:   
• Incredible agility   
• Intense efficiency   
• Highest exploitation  
  
 
 
 
 
 
 
 
 
 
 
 
Fig 1: Cloud computing types 
It combines cloud computing environment, cooling, saving 
power, and space, with money. Cloud presents a future-proof 
proposal that can develop no wildly as commerce requires. 
This indicates that you can revolve out novel applications 
sooner, be more receptive to client needs, and decrease IT 
costs on a huge scale by organizing a greatly competent 
infrastructure. The cloud computing construction is included 
of two considerable parts: the front end and the back end. The 
front end is the region at which the customer of the computer 
or the consumer himself is capable to access. The cloud 
computing outline might have predictable two principal 
apprehensions with the use of the cloud computing platform: 
privacy and security. Green Computing is particularly 
significant and appropriate: as computing develops into 
gradually more persistent, the energy consumption 
attributable to evaluating, regardless of the clarion identify to 
diminish utilization and turn around greenhouse effects. This 
is forcing the IT influential to hub on competence and total 
cost possession, predominantly in the framework of the 
world-wide economic crisis.   
                      
CLOUD 
Hybrid 
Public 
cloud 
 
Private 
cloud 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
21 
Environmental knowledge is the purpose of one or more 
of environmental science, environmental supervising, green 
chemistry, and electronic devices to scrutinize structure and 
defend the standard environment and resources, and to 
manage the destructive collides of human involvement. This 
is also phrased as green technology (abbreviated 
as greentech). The Green IT is also utilized to exemplify 
sustainable energy creation strategies such as  Wind Turbine's, 
Photovoltaic etc. Sustainable progression is the central part 
of Green IT environment. The term Green IT is also 
illustrated a class of electronic devices that can encourage 
sustainable administration of resources. 
In this work, we developed a resource distribution model of 
green cloud computing environments, by resending both 
processing capability and bandwidth are owed concurrently to 
every service request and borrowed out on an hourly basis. 
The billed resources are devoted to every service request and 
diminish the resource usage to make IT green. 
2. LITERATURE REVIEW 
Cloud computing is a representation for allowing expedient, 
on-demand system entry to a common pool of configurable 
calculating resources. Positioning and interior structures of 
resources have been studied [1], but power management is not 
done. Amazon Elastic Compute Cloud (EC2) [2] is an 
instance of HaaS (Hardware as a Service), which is a structure 
of cloud computing. Fairness should be followed whereas 
captivating numerous types of resource into deliberation.  
Cloud computing services are simple to use, and can decrease 
both trade costs and ecological loads [3]. Quick flexibility and 
measured service [4] are highlighted for cloud computing 
scenario. There are several papers that converse algorithms for 
attaining fairness for cases where a combined resource 
allocation is not measured [5]. To afford cloud computing 
services reasonably, it is significant to optimize resource 
distribution under the statement that the requisite fair resource 
[6] can be taken from a common resource group. Besides, to 
be able to present processing capability and storage ability, it 
is essential to assign bandwidth to entry them at the equal 
time. The paper [7] learns the optimization crisis of reducing 
resource leasing cost for managing flexible applications in 
cloud whilst gathering application service necessities. Such a 
crisis arises when unnecessary produced data acquires 
important economic cost on transmit and inventory in cloud. 
A monetary approach presented in [8], which services “offer” 
for possessions as a purpose of distributed performance. 
Cloud computing services are quickly ahead in reputation. 
They permit the consumer to charge, only at the time when 
desirable, only a preferred quantity of calculating resources 
(processing capability and storage space capability) out of a 
massive distributed computing resources [9] without upsetting 
concerning the position or interior structures of these 
resources. The National Institute of Standards and Technology 
(NIST) recognized four necessary distinctiveness of cloud 
computing: resource pooling [10]. The reputation of cloud 
computing be obliged to amplify in the network speed, and to 
the reality that virtualization and network computing 
technologies have turn into commercially accessible. It is 
predictable that endeavors will hasten their movement from 
construction and possessing their individual systems to 
leasing cloud computing services [11].  
 
 
To run common server resources [12], in this services “offer” 
for possessions as a purpose of distributed performance. In 
this work, an optimal resource allocation method is used for 
optimization of resource usage based on users’ task. 
3. PROPOSED OPTIMAL RESOURCE 
ALLOCATION TECHNIQUE (ORAT) 
FOR GREEN CLOUD COMPUTING 
ENVIRORNMENT 
The proposed work is efficiently designed to optimize 
resource allocation under the supposition that the requisite 
resource can be obtained from a joint resource pool. Besides, 
to be capable to offer processing capability and storage space 
facility, it is essential to assign bandwidth to entrée them at 
the similar time. The architecture diagram of the proposed 
Optimal resource allocation technique (ORAT) for green 
cloud computing is shown in fig 2. 
As cloud computing services quickly enlarge their client 
support, it has developed into significant to afford them 
reasonably. To do so, it is necessary to optimize resource 
distribution under the statement that the necessary quantity of 
resource can be obtained from a widespread resource pool and 
borrowed out to the user on an hourly center. In addition, to 
be capable to present processing capability and storage space 
facility, it is required to preserve concurrently a network 
bandwidth to process them. Consequently, it is required to 
distribute several types of resources (such as processing 
capability, storage space capacity and bandwidth) 
concurrently in a synchronized way instead of assigning each 
type of resource separately. The quantity of resource vital and 
the period in which it is utilized are not permanent. They can 
differ really from user to user and since service to service.  
The paper proposed an optimal resource allocation method for 
green cloud computing environments. For the preface 
assessment, this paper presumes the utilization of two types of 
resources: processing capability and bandwidth. In common, 
services can be divided into two groups: a non-delay system 
(defeat system) and a waiting scheme. A non-delay system 
assigns an additional resource instantly to the user ahead the 
advent of the request, and discards the request if there is no 
additional capacity. A waiting system assigns an additional 
capability to users in the series in which their needs have 
arrived, as a substitute of assigning resources instantly upon 
the advent of a request. This paper presumes a service that 
sprints as non-delay. This paper also thinks stationary 
resource distribution, which is the most essential structure of 
resource distribution, although active provision, which uses 
procedure immigration and bandwidth consolidation, can 
augment the exploitation of resources. 
 
 
 
 
 
 
 
 
 
Page 3


International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
20 
Optimal Resource Allocation Technique (ORAT) for 
Green Cloud Computing 
 
K. L. Giridas 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu-629 180. 
 
 
Shajin Nargunam 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu- 629 180. 
 
 
ABSTRACT 
As the IT trade progress towards game-changing expertise, a 
cloud Eco-system is gradually increasing in the country with 
expertise corporation ramping up employing and guiding for 
cloud computing. An accomplishment of green IT is probable 
to assist an organization in several ways like operating cost, 
stakeholder value, sustainability, employee morale and so on.  
Cloud computing might hoist privacy and security concerns 
but this could have one obvious advantage, far enhanced 
energy efficiency. The previous work studied about the 
accumulation of e-waste by integrating the old and mid-range 
processors with modern processors but the care about resource 
efficiency is less. In this paper, proposes an optimal resource 
allocation method for cloud computing environments. This 
paper progress a resource allocation representation of green 
cloud computing environments, considering both bandwidth 
and processing capability, allocated concurrently to every 
service request and returned it on an hourly basis. The owed 
resources are committed to every service request. It is 
established by simulation evaluation that the proposed ORAT 
method can diminish the request loss possibility and therefore, 
decrease the total resource obligatory, compared with the 
predictable allocation method. Through the optimal resource 
allocation, the resources for the tasks are allocated for cloud 
computing environment by eradicating e-waste and make IT 
as Green IT. The proposed Optimal resource allocation 
technique (ORAT) for cloud computing in Green IT can be 
implemented in CloudSim software, and various performance 
characteristics can be simulated to estimate the performance 
of the proposed ORAT in terms of processing ability, resource 
utilization, bandwidth. 
General Term 
Cloud Computing, Green Computing. 
Keywords 
Cloud computing environment, Green IT, resource allocation, 
Optimization. 
1. INTRODUCTION 
Cloud Computing is one of the mainly admired subject in the 
ICT sector nowadays. Cloud computing means incorporated, 
active infrastructures that carry IT as a service moreover 
inside (private cloud) or on the outside (public cloud). It is 
significant to recognize the trade-offs between Software as a 
Service (SaaS), Platform as a Service (PaaS), and 
Infrastructure as a Service (IaaS), and among private and 
public clouds. Envision the potentials for the association if 
you may possibly persist to construct virtualized environment 
into an entirely mechanized, service- oriented transportation 
of collective resources (storage, server, and network) that 
permits to simply distribute IT Services to interior users.  The 
cloud computing types is illustrated in fig 1. 
There are at any rate three merits to optimizing the 
employment of a shared IT environment:   
• Incredible agility   
• Intense efficiency   
• Highest exploitation  
  
 
 
 
 
 
 
 
 
 
 
 
Fig 1: Cloud computing types 
It combines cloud computing environment, cooling, saving 
power, and space, with money. Cloud presents a future-proof 
proposal that can develop no wildly as commerce requires. 
This indicates that you can revolve out novel applications 
sooner, be more receptive to client needs, and decrease IT 
costs on a huge scale by organizing a greatly competent 
infrastructure. The cloud computing construction is included 
of two considerable parts: the front end and the back end. The 
front end is the region at which the customer of the computer 
or the consumer himself is capable to access. The cloud 
computing outline might have predictable two principal 
apprehensions with the use of the cloud computing platform: 
privacy and security. Green Computing is particularly 
significant and appropriate: as computing develops into 
gradually more persistent, the energy consumption 
attributable to evaluating, regardless of the clarion identify to 
diminish utilization and turn around greenhouse effects. This 
is forcing the IT influential to hub on competence and total 
cost possession, predominantly in the framework of the 
world-wide economic crisis.   
                      
CLOUD 
Hybrid 
Public 
cloud 
 
Private 
cloud 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
21 
Environmental knowledge is the purpose of one or more 
of environmental science, environmental supervising, green 
chemistry, and electronic devices to scrutinize structure and 
defend the standard environment and resources, and to 
manage the destructive collides of human involvement. This 
is also phrased as green technology (abbreviated 
as greentech). The Green IT is also utilized to exemplify 
sustainable energy creation strategies such as  Wind Turbine's, 
Photovoltaic etc. Sustainable progression is the central part 
of Green IT environment. The term Green IT is also 
illustrated a class of electronic devices that can encourage 
sustainable administration of resources. 
In this work, we developed a resource distribution model of 
green cloud computing environments, by resending both 
processing capability and bandwidth are owed concurrently to 
every service request and borrowed out on an hourly basis. 
The billed resources are devoted to every service request and 
diminish the resource usage to make IT green. 
2. LITERATURE REVIEW 
Cloud computing is a representation for allowing expedient, 
on-demand system entry to a common pool of configurable 
calculating resources. Positioning and interior structures of 
resources have been studied [1], but power management is not 
done. Amazon Elastic Compute Cloud (EC2) [2] is an 
instance of HaaS (Hardware as a Service), which is a structure 
of cloud computing. Fairness should be followed whereas 
captivating numerous types of resource into deliberation.  
Cloud computing services are simple to use, and can decrease 
both trade costs and ecological loads [3]. Quick flexibility and 
measured service [4] are highlighted for cloud computing 
scenario. There are several papers that converse algorithms for 
attaining fairness for cases where a combined resource 
allocation is not measured [5]. To afford cloud computing 
services reasonably, it is significant to optimize resource 
distribution under the statement that the requisite fair resource 
[6] can be taken from a common resource group. Besides, to 
be able to present processing capability and storage ability, it 
is essential to assign bandwidth to entry them at the equal 
time. The paper [7] learns the optimization crisis of reducing 
resource leasing cost for managing flexible applications in 
cloud whilst gathering application service necessities. Such a 
crisis arises when unnecessary produced data acquires 
important economic cost on transmit and inventory in cloud. 
A monetary approach presented in [8], which services “offer” 
for possessions as a purpose of distributed performance. 
Cloud computing services are quickly ahead in reputation. 
They permit the consumer to charge, only at the time when 
desirable, only a preferred quantity of calculating resources 
(processing capability and storage space capability) out of a 
massive distributed computing resources [9] without upsetting 
concerning the position or interior structures of these 
resources. The National Institute of Standards and Technology 
(NIST) recognized four necessary distinctiveness of cloud 
computing: resource pooling [10]. The reputation of cloud 
computing be obliged to amplify in the network speed, and to 
the reality that virtualization and network computing 
technologies have turn into commercially accessible. It is 
predictable that endeavors will hasten their movement from 
construction and possessing their individual systems to 
leasing cloud computing services [11].  
 
 
To run common server resources [12], in this services “offer” 
for possessions as a purpose of distributed performance. In 
this work, an optimal resource allocation method is used for 
optimization of resource usage based on users’ task. 
3. PROPOSED OPTIMAL RESOURCE 
ALLOCATION TECHNIQUE (ORAT) 
FOR GREEN CLOUD COMPUTING 
ENVIRORNMENT 
The proposed work is efficiently designed to optimize 
resource allocation under the supposition that the requisite 
resource can be obtained from a joint resource pool. Besides, 
to be capable to offer processing capability and storage space 
facility, it is essential to assign bandwidth to entrée them at 
the similar time. The architecture diagram of the proposed 
Optimal resource allocation technique (ORAT) for green 
cloud computing is shown in fig 2. 
As cloud computing services quickly enlarge their client 
support, it has developed into significant to afford them 
reasonably. To do so, it is necessary to optimize resource 
distribution under the statement that the necessary quantity of 
resource can be obtained from a widespread resource pool and 
borrowed out to the user on an hourly center. In addition, to 
be capable to present processing capability and storage space 
facility, it is required to preserve concurrently a network 
bandwidth to process them. Consequently, it is required to 
distribute several types of resources (such as processing 
capability, storage space capacity and bandwidth) 
concurrently in a synchronized way instead of assigning each 
type of resource separately. The quantity of resource vital and 
the period in which it is utilized are not permanent. They can 
differ really from user to user and since service to service.  
The paper proposed an optimal resource allocation method for 
green cloud computing environments. For the preface 
assessment, this paper presumes the utilization of two types of 
resources: processing capability and bandwidth. In common, 
services can be divided into two groups: a non-delay system 
(defeat system) and a waiting scheme. A non-delay system 
assigns an additional resource instantly to the user ahead the 
advent of the request, and discards the request if there is no 
additional capacity. A waiting system assigns an additional 
capability to users in the series in which their needs have 
arrived, as a substitute of assigning resources instantly upon 
the advent of a request. This paper presumes a service that 
sprints as non-delay. This paper also thinks stationary 
resource distribution, which is the most essential structure of 
resource distribution, although active provision, which uses 
procedure immigration and bandwidth consolidation, can 
augment the exploitation of resources. 
 
 
 
 
 
 
 
 
 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
22 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig 2: Architecture diagram of the proposed ORAT 
3.1 Resource allocation in cloud computing environment 
The resource allotment in a cloud computing environment can 
be represented as assigning the requisite quantity of numerous 
types of resource concurrently from a widespread resource 
pool for a definite stage of time for every request. The owed 
resources are committed (not shared) to every request.  For 
the preface assessment, this paper proposes two types of 
resource: resource processing capability and bandwidth.  
The requisite quantity of resource and the interlude of time in 
which it is utilized are not predetermined. They can differ 
significantly from user to user and from service to service. For 
instance, video delivery, file transfer, and videoconferencing 
services need a huge quantity of bandwidth but not so greatly 
resource processing capability. In distinction, a secretarial 
service needs a huge amount of processing capability but not 
so much bandwidth. It is understood that the hardware 
resources for green cloud computing services are not mounted 
at a distinct center, but in numerous biologically dispersed 
centers, as shown in Figure 3., in order to assist accumulation 
of resources, to employ load balancing and to guarantee high 
consistency. Each center has servers (counting virtual servers) 
that offer processing capability, and bandwidths that present 
access to these servers.  
 
Centre 1       Centre 2  Centre n 
 
 
                             ….    
 
 
 
 
                      
         ---server’s processing ability 
       --- Link (Bandwidth) 
 
C
max j
 - Maximum size of resource processing capability at    
           center j 
N
maxj - 
Maximum size of bandwidth at center j 
Fig 3: System model for cloud computing services 
When a new request is produced, one hub from between k 
centers is chosen consistent with the resource distribution 
algorithm. The highest size of processing capability and 
bandwidth at center j (j=1, 2,..,n) is specified to be C
maxj
 and 
N
maxj
 correspondingly.  The notion of resource allocation 
obtains the resource practice stage into deliberation. When a 
service request appears, the finest center is chosen from 
numerous centers, and both the processing capacity and 
bandwidth accessible in the chosen center are owed for a 
definite period of time. The processing capacity and 
bandwidth of only this particular center are owed. If no center 
has a sufficient quantity of auxiliary resources (both 
processing capacity and bandwidth), the request is discarded. 
The resources owed are unconfined after the usage period has 
gone. 
3.2 Optimal resource allocation technique for green cloud 
computing environment 
The objective of the proposed optimal resource allocation 
technique (ORAT) for green cloud computing allocation is to 
core i7 processor pool 
core 2 duo processor pool 
core i7 processor pool 
P3 processor pool P4 processor pool 
Optimal resource allocation 
technique 
Allocate the processor 
to each user 
Set of users 
 
 
. 
. 
 
 
 
 
… … … … …
… .. 
U1 
U2 
Un 
Improve Bandwidth, resource 
utilization 
 
 
 
 
Cloud computing environment 
           Cmax 1  
 
 
 
        Nmax 1              
           Cmax 2  
 
 
 
        Nmax 2              
           Cmax n  
 
 
 
        Nmax n             
Page 4


International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
20 
Optimal Resource Allocation Technique (ORAT) for 
Green Cloud Computing 
 
K. L. Giridas 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu-629 180. 
 
 
Shajin Nargunam 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu- 629 180. 
 
 
ABSTRACT 
As the IT trade progress towards game-changing expertise, a 
cloud Eco-system is gradually increasing in the country with 
expertise corporation ramping up employing and guiding for 
cloud computing. An accomplishment of green IT is probable 
to assist an organization in several ways like operating cost, 
stakeholder value, sustainability, employee morale and so on.  
Cloud computing might hoist privacy and security concerns 
but this could have one obvious advantage, far enhanced 
energy efficiency. The previous work studied about the 
accumulation of e-waste by integrating the old and mid-range 
processors with modern processors but the care about resource 
efficiency is less. In this paper, proposes an optimal resource 
allocation method for cloud computing environments. This 
paper progress a resource allocation representation of green 
cloud computing environments, considering both bandwidth 
and processing capability, allocated concurrently to every 
service request and returned it on an hourly basis. The owed 
resources are committed to every service request. It is 
established by simulation evaluation that the proposed ORAT 
method can diminish the request loss possibility and therefore, 
decrease the total resource obligatory, compared with the 
predictable allocation method. Through the optimal resource 
allocation, the resources for the tasks are allocated for cloud 
computing environment by eradicating e-waste and make IT 
as Green IT. The proposed Optimal resource allocation 
technique (ORAT) for cloud computing in Green IT can be 
implemented in CloudSim software, and various performance 
characteristics can be simulated to estimate the performance 
of the proposed ORAT in terms of processing ability, resource 
utilization, bandwidth. 
General Term 
Cloud Computing, Green Computing. 
Keywords 
Cloud computing environment, Green IT, resource allocation, 
Optimization. 
1. INTRODUCTION 
Cloud Computing is one of the mainly admired subject in the 
ICT sector nowadays. Cloud computing means incorporated, 
active infrastructures that carry IT as a service moreover 
inside (private cloud) or on the outside (public cloud). It is 
significant to recognize the trade-offs between Software as a 
Service (SaaS), Platform as a Service (PaaS), and 
Infrastructure as a Service (IaaS), and among private and 
public clouds. Envision the potentials for the association if 
you may possibly persist to construct virtualized environment 
into an entirely mechanized, service- oriented transportation 
of collective resources (storage, server, and network) that 
permits to simply distribute IT Services to interior users.  The 
cloud computing types is illustrated in fig 1. 
There are at any rate three merits to optimizing the 
employment of a shared IT environment:   
• Incredible agility   
• Intense efficiency   
• Highest exploitation  
  
 
 
 
 
 
 
 
 
 
 
 
Fig 1: Cloud computing types 
It combines cloud computing environment, cooling, saving 
power, and space, with money. Cloud presents a future-proof 
proposal that can develop no wildly as commerce requires. 
This indicates that you can revolve out novel applications 
sooner, be more receptive to client needs, and decrease IT 
costs on a huge scale by organizing a greatly competent 
infrastructure. The cloud computing construction is included 
of two considerable parts: the front end and the back end. The 
front end is the region at which the customer of the computer 
or the consumer himself is capable to access. The cloud 
computing outline might have predictable two principal 
apprehensions with the use of the cloud computing platform: 
privacy and security. Green Computing is particularly 
significant and appropriate: as computing develops into 
gradually more persistent, the energy consumption 
attributable to evaluating, regardless of the clarion identify to 
diminish utilization and turn around greenhouse effects. This 
is forcing the IT influential to hub on competence and total 
cost possession, predominantly in the framework of the 
world-wide economic crisis.   
                      
CLOUD 
Hybrid 
Public 
cloud 
 
Private 
cloud 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
21 
Environmental knowledge is the purpose of one or more 
of environmental science, environmental supervising, green 
chemistry, and electronic devices to scrutinize structure and 
defend the standard environment and resources, and to 
manage the destructive collides of human involvement. This 
is also phrased as green technology (abbreviated 
as greentech). The Green IT is also utilized to exemplify 
sustainable energy creation strategies such as  Wind Turbine's, 
Photovoltaic etc. Sustainable progression is the central part 
of Green IT environment. The term Green IT is also 
illustrated a class of electronic devices that can encourage 
sustainable administration of resources. 
In this work, we developed a resource distribution model of 
green cloud computing environments, by resending both 
processing capability and bandwidth are owed concurrently to 
every service request and borrowed out on an hourly basis. 
The billed resources are devoted to every service request and 
diminish the resource usage to make IT green. 
2. LITERATURE REVIEW 
Cloud computing is a representation for allowing expedient, 
on-demand system entry to a common pool of configurable 
calculating resources. Positioning and interior structures of 
resources have been studied [1], but power management is not 
done. Amazon Elastic Compute Cloud (EC2) [2] is an 
instance of HaaS (Hardware as a Service), which is a structure 
of cloud computing. Fairness should be followed whereas 
captivating numerous types of resource into deliberation.  
Cloud computing services are simple to use, and can decrease 
both trade costs and ecological loads [3]. Quick flexibility and 
measured service [4] are highlighted for cloud computing 
scenario. There are several papers that converse algorithms for 
attaining fairness for cases where a combined resource 
allocation is not measured [5]. To afford cloud computing 
services reasonably, it is significant to optimize resource 
distribution under the statement that the requisite fair resource 
[6] can be taken from a common resource group. Besides, to 
be able to present processing capability and storage ability, it 
is essential to assign bandwidth to entry them at the equal 
time. The paper [7] learns the optimization crisis of reducing 
resource leasing cost for managing flexible applications in 
cloud whilst gathering application service necessities. Such a 
crisis arises when unnecessary produced data acquires 
important economic cost on transmit and inventory in cloud. 
A monetary approach presented in [8], which services “offer” 
for possessions as a purpose of distributed performance. 
Cloud computing services are quickly ahead in reputation. 
They permit the consumer to charge, only at the time when 
desirable, only a preferred quantity of calculating resources 
(processing capability and storage space capability) out of a 
massive distributed computing resources [9] without upsetting 
concerning the position or interior structures of these 
resources. The National Institute of Standards and Technology 
(NIST) recognized four necessary distinctiveness of cloud 
computing: resource pooling [10]. The reputation of cloud 
computing be obliged to amplify in the network speed, and to 
the reality that virtualization and network computing 
technologies have turn into commercially accessible. It is 
predictable that endeavors will hasten their movement from 
construction and possessing their individual systems to 
leasing cloud computing services [11].  
 
 
To run common server resources [12], in this services “offer” 
for possessions as a purpose of distributed performance. In 
this work, an optimal resource allocation method is used for 
optimization of resource usage based on users’ task. 
3. PROPOSED OPTIMAL RESOURCE 
ALLOCATION TECHNIQUE (ORAT) 
FOR GREEN CLOUD COMPUTING 
ENVIRORNMENT 
The proposed work is efficiently designed to optimize 
resource allocation under the supposition that the requisite 
resource can be obtained from a joint resource pool. Besides, 
to be capable to offer processing capability and storage space 
facility, it is essential to assign bandwidth to entrée them at 
the similar time. The architecture diagram of the proposed 
Optimal resource allocation technique (ORAT) for green 
cloud computing is shown in fig 2. 
As cloud computing services quickly enlarge their client 
support, it has developed into significant to afford them 
reasonably. To do so, it is necessary to optimize resource 
distribution under the statement that the necessary quantity of 
resource can be obtained from a widespread resource pool and 
borrowed out to the user on an hourly center. In addition, to 
be capable to present processing capability and storage space 
facility, it is required to preserve concurrently a network 
bandwidth to process them. Consequently, it is required to 
distribute several types of resources (such as processing 
capability, storage space capacity and bandwidth) 
concurrently in a synchronized way instead of assigning each 
type of resource separately. The quantity of resource vital and 
the period in which it is utilized are not permanent. They can 
differ really from user to user and since service to service.  
The paper proposed an optimal resource allocation method for 
green cloud computing environments. For the preface 
assessment, this paper presumes the utilization of two types of 
resources: processing capability and bandwidth. In common, 
services can be divided into two groups: a non-delay system 
(defeat system) and a waiting scheme. A non-delay system 
assigns an additional resource instantly to the user ahead the 
advent of the request, and discards the request if there is no 
additional capacity. A waiting system assigns an additional 
capability to users in the series in which their needs have 
arrived, as a substitute of assigning resources instantly upon 
the advent of a request. This paper presumes a service that 
sprints as non-delay. This paper also thinks stationary 
resource distribution, which is the most essential structure of 
resource distribution, although active provision, which uses 
procedure immigration and bandwidth consolidation, can 
augment the exploitation of resources. 
 
 
 
 
 
 
 
 
 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
22 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig 2: Architecture diagram of the proposed ORAT 
3.1 Resource allocation in cloud computing environment 
The resource allotment in a cloud computing environment can 
be represented as assigning the requisite quantity of numerous 
types of resource concurrently from a widespread resource 
pool for a definite stage of time for every request. The owed 
resources are committed (not shared) to every request.  For 
the preface assessment, this paper proposes two types of 
resource: resource processing capability and bandwidth.  
The requisite quantity of resource and the interlude of time in 
which it is utilized are not predetermined. They can differ 
significantly from user to user and from service to service. For 
instance, video delivery, file transfer, and videoconferencing 
services need a huge quantity of bandwidth but not so greatly 
resource processing capability. In distinction, a secretarial 
service needs a huge amount of processing capability but not 
so much bandwidth. It is understood that the hardware 
resources for green cloud computing services are not mounted 
at a distinct center, but in numerous biologically dispersed 
centers, as shown in Figure 3., in order to assist accumulation 
of resources, to employ load balancing and to guarantee high 
consistency. Each center has servers (counting virtual servers) 
that offer processing capability, and bandwidths that present 
access to these servers.  
 
Centre 1       Centre 2  Centre n 
 
 
                             ….    
 
 
 
 
                      
         ---server’s processing ability 
       --- Link (Bandwidth) 
 
C
max j
 - Maximum size of resource processing capability at    
           center j 
N
maxj - 
Maximum size of bandwidth at center j 
Fig 3: System model for cloud computing services 
When a new request is produced, one hub from between k 
centers is chosen consistent with the resource distribution 
algorithm. The highest size of processing capability and 
bandwidth at center j (j=1, 2,..,n) is specified to be C
maxj
 and 
N
maxj
 correspondingly.  The notion of resource allocation 
obtains the resource practice stage into deliberation. When a 
service request appears, the finest center is chosen from 
numerous centers, and both the processing capacity and 
bandwidth accessible in the chosen center are owed for a 
definite period of time. The processing capacity and 
bandwidth of only this particular center are owed. If no center 
has a sufficient quantity of auxiliary resources (both 
processing capacity and bandwidth), the request is discarded. 
The resources owed are unconfined after the usage period has 
gone. 
3.2 Optimal resource allocation technique for green cloud 
computing environment 
The objective of the proposed optimal resource allocation 
technique (ORAT) for green cloud computing allocation is to 
core i7 processor pool 
core 2 duo processor pool 
core i7 processor pool 
P3 processor pool P4 processor pool 
Optimal resource allocation 
technique 
Allocate the processor 
to each user 
Set of users 
 
 
. 
. 
 
 
 
 
… … … … …
… .. 
U1 
U2 
Un 
Improve Bandwidth, resource 
utilization 
 
 
 
 
Cloud computing environment 
           Cmax 1  
 
 
 
        Nmax 1              
           Cmax 2  
 
 
 
        Nmax 2              
           Cmax n  
 
 
 
        Nmax n             
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
23 
exploit the number of requests to which both resource 
processing capability and bandwidth are maintained well. As 
the amount of requisite resource processing capability does 
not usually have a rigid association with that of requisite 
bandwidth, the finest resource allocation cannot be attained if 
only a single resource kind is measured in the assortment of a 
center.  
The resource that needs the prevalent balanced size of 
resource, contrast the amount of requisite resource with the 
greatest resource size for every resource type, is first chosen 
as ‘recognized resource’. Then the smallest amount accessible 
of the recognized resource from amongst k centers is chosen. 
In this case, the parts of resource processing capability and 
bandwidth are diverse, being considered in proportion of CPU 
power and b/s (bits per second) correspondingly. It is 
proposed to evaluate the size of the diverse resources as in the 
subsequent instance. Suppose that the highest quantity of 
bandwidth in a center is 100Mb/s. A demand for 20% of CPU 
power and 30Mb/s needs 20% of resource processing capacity 
and 30% of bandwidth correspondingly. As the amount of 
requisite bandwidth is better than that of requisite processing 
capability, bandwidth will be the known resource in this case.   
        Centre 1           Centre 2           Centre n 
 
TPA 
 
 
      . . . 
TB 
 
 
 
Allocated Resources 
TPA-Total Processing Ability 
TB-Total Bandwidth 
Fig 4: Possible resource allocation for a user 
When a service request task is generated by the user, the 
subsequent resource allocation algorithm is conceded out and 
shown in fig 4.  
i) Assortment of known resource   
   if X
C
>X
N
 then processing capacity is the recognized 
resource.  
   Else bandwidth is the recognized resource.  
Where  
   X
C 
= {the amount of necessary processing capacity}/XC0 
   X
C0 
= Min {the highest amount of processing facility in a 
center}  
   X
N 
= {the size of requisite bandwidth}/XN0 
     X
N0 
= Min {the greatest amount of bandwidth in a center}  
   For instance, if there are two centers and the greatest 
amount of resource processing ability of every center is 100 
and 50 correspondingly, XC0 will be 50. 
ii) Assortment of the center 
    The center which suits the subsequent three conditions will 
be chosen:  
   a.) Min {the obtainable size of the notorious resource in the 
center}   
   b.)Accessible processing capacity in the center is equivalent 
to or superior than the requisite processing ability.  
     c.) Accessible bandwidth in the center is equivalent to or 
better than the requisite bandwidth.  
If there are two or more centers which persuades the above 
conditions, one center will be chosen at arbitrary.  Remind 
that the request will be discarded if there are no centers that 
persuade the above conditions.  
iii) Distribution of resource  
      Both requisite processing capacity and bandwidth are 
owed concurrently in the chosen center. The maintained 
resources are applied (not common) to the request.  
iv) Resource released 
   When the service time has concluded, both owed processing 
capability and bandwidth will be confined concurrently. 
The above algorithm is followed for resource allocation 
process based on an optimization of resource usage based on 
CPU cycles, bandwidth, processing ability it needs to process 
the user given tasks and an experimental evaluation is 
conducted to estimate the performance of the proposed ORAT 
and described in next section.  
4. EXPERIMENTAL EVALUATION 
The proposed Optimal resource allocation technique (ORAT) 
for green cloud computing is implemented in Java using 
cloudsim software. The proposed optimal resource allocation 
technique (ORAT used old range, mid range and high end 
processors. The old range processors includes 286, 386, 
Pentium, the mid range processors includes Pentium pro, 
Pentium III, Pentium IV, the high end processors be core2, 
core i7. These types of processors pools are integrated to 
analyze the performance of the proposed optimal resource 
allocation technique (ORAT) for green cloud computing 
based on processors utilization. The number of tasks assigned 
to the processor is based on the capability of the processor in 
the resource pool measured in terms of CPU cycles, 
bandwidth, and data rate. The proposed ORAT first identifies 
the tasks schedule, resource/processor capability. An 
optimized resource allocation takes place under the 
assumption that the required resource can be taken from a 
shared resource pool. In addition, to be able to provide 
processing ability and storage capacity, it is necessary to 
allocate bandwidth to access them at the same time. 
Operations can be assigned to the pool of old range and mid 
range processors with high end processors. The proposed 
ORAT for cloud computing infrastructure is measured in 
terms of: 
i.)Request loss probability. 
ii.)Resource utilization. 
iii) Data transfer rate. 
5. RESULTS AND DISCUSSION 
In this work, we have seen how a pool of processors can be 
allocated to users’ tasks based on optimal resource utilization 
technique to capture the performance of cloud computing 
process in Green IT to other systems written in mainstream 
languages such as Java. We run independent tests with several 
number of resources task with a constant number of tasks sent 
by each users. The entire process of the proposed Optimal 
resource allocation technique (ORAT) for green cloud 
computing is explained in section 3 briefly and this section 
Page 5


International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
20 
Optimal Resource Allocation Technique (ORAT) for 
Green Cloud Computing 
 
K. L. Giridas 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu-629 180. 
 
 
Shajin Nargunam 
Computer Science Engineering Department, 
Noorul Islam University, 
Kumaracoil, Nagercoil, 
Tamilnadu- 629 180. 
 
 
ABSTRACT 
As the IT trade progress towards game-changing expertise, a 
cloud Eco-system is gradually increasing in the country with 
expertise corporation ramping up employing and guiding for 
cloud computing. An accomplishment of green IT is probable 
to assist an organization in several ways like operating cost, 
stakeholder value, sustainability, employee morale and so on.  
Cloud computing might hoist privacy and security concerns 
but this could have one obvious advantage, far enhanced 
energy efficiency. The previous work studied about the 
accumulation of e-waste by integrating the old and mid-range 
processors with modern processors but the care about resource 
efficiency is less. In this paper, proposes an optimal resource 
allocation method for cloud computing environments. This 
paper progress a resource allocation representation of green 
cloud computing environments, considering both bandwidth 
and processing capability, allocated concurrently to every 
service request and returned it on an hourly basis. The owed 
resources are committed to every service request. It is 
established by simulation evaluation that the proposed ORAT 
method can diminish the request loss possibility and therefore, 
decrease the total resource obligatory, compared with the 
predictable allocation method. Through the optimal resource 
allocation, the resources for the tasks are allocated for cloud 
computing environment by eradicating e-waste and make IT 
as Green IT. The proposed Optimal resource allocation 
technique (ORAT) for cloud computing in Green IT can be 
implemented in CloudSim software, and various performance 
characteristics can be simulated to estimate the performance 
of the proposed ORAT in terms of processing ability, resource 
utilization, bandwidth. 
General Term 
Cloud Computing, Green Computing. 
Keywords 
Cloud computing environment, Green IT, resource allocation, 
Optimization. 
1. INTRODUCTION 
Cloud Computing is one of the mainly admired subject in the 
ICT sector nowadays. Cloud computing means incorporated, 
active infrastructures that carry IT as a service moreover 
inside (private cloud) or on the outside (public cloud). It is 
significant to recognize the trade-offs between Software as a 
Service (SaaS), Platform as a Service (PaaS), and 
Infrastructure as a Service (IaaS), and among private and 
public clouds. Envision the potentials for the association if 
you may possibly persist to construct virtualized environment 
into an entirely mechanized, service- oriented transportation 
of collective resources (storage, server, and network) that 
permits to simply distribute IT Services to interior users.  The 
cloud computing types is illustrated in fig 1. 
There are at any rate three merits to optimizing the 
employment of a shared IT environment:   
• Incredible agility   
• Intense efficiency   
• Highest exploitation  
  
 
 
 
 
 
 
 
 
 
 
 
Fig 1: Cloud computing types 
It combines cloud computing environment, cooling, saving 
power, and space, with money. Cloud presents a future-proof 
proposal that can develop no wildly as commerce requires. 
This indicates that you can revolve out novel applications 
sooner, be more receptive to client needs, and decrease IT 
costs on a huge scale by organizing a greatly competent 
infrastructure. The cloud computing construction is included 
of two considerable parts: the front end and the back end. The 
front end is the region at which the customer of the computer 
or the consumer himself is capable to access. The cloud 
computing outline might have predictable two principal 
apprehensions with the use of the cloud computing platform: 
privacy and security. Green Computing is particularly 
significant and appropriate: as computing develops into 
gradually more persistent, the energy consumption 
attributable to evaluating, regardless of the clarion identify to 
diminish utilization and turn around greenhouse effects. This 
is forcing the IT influential to hub on competence and total 
cost possession, predominantly in the framework of the 
world-wide economic crisis.   
                      
CLOUD 
Hybrid 
Public 
cloud 
 
Private 
cloud 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
21 
Environmental knowledge is the purpose of one or more 
of environmental science, environmental supervising, green 
chemistry, and electronic devices to scrutinize structure and 
defend the standard environment and resources, and to 
manage the destructive collides of human involvement. This 
is also phrased as green technology (abbreviated 
as greentech). The Green IT is also utilized to exemplify 
sustainable energy creation strategies such as  Wind Turbine's, 
Photovoltaic etc. Sustainable progression is the central part 
of Green IT environment. The term Green IT is also 
illustrated a class of electronic devices that can encourage 
sustainable administration of resources. 
In this work, we developed a resource distribution model of 
green cloud computing environments, by resending both 
processing capability and bandwidth are owed concurrently to 
every service request and borrowed out on an hourly basis. 
The billed resources are devoted to every service request and 
diminish the resource usage to make IT green. 
2. LITERATURE REVIEW 
Cloud computing is a representation for allowing expedient, 
on-demand system entry to a common pool of configurable 
calculating resources. Positioning and interior structures of 
resources have been studied [1], but power management is not 
done. Amazon Elastic Compute Cloud (EC2) [2] is an 
instance of HaaS (Hardware as a Service), which is a structure 
of cloud computing. Fairness should be followed whereas 
captivating numerous types of resource into deliberation.  
Cloud computing services are simple to use, and can decrease 
both trade costs and ecological loads [3]. Quick flexibility and 
measured service [4] are highlighted for cloud computing 
scenario. There are several papers that converse algorithms for 
attaining fairness for cases where a combined resource 
allocation is not measured [5]. To afford cloud computing 
services reasonably, it is significant to optimize resource 
distribution under the statement that the requisite fair resource 
[6] can be taken from a common resource group. Besides, to 
be able to present processing capability and storage ability, it 
is essential to assign bandwidth to entry them at the equal 
time. The paper [7] learns the optimization crisis of reducing 
resource leasing cost for managing flexible applications in 
cloud whilst gathering application service necessities. Such a 
crisis arises when unnecessary produced data acquires 
important economic cost on transmit and inventory in cloud. 
A monetary approach presented in [8], which services “offer” 
for possessions as a purpose of distributed performance. 
Cloud computing services are quickly ahead in reputation. 
They permit the consumer to charge, only at the time when 
desirable, only a preferred quantity of calculating resources 
(processing capability and storage space capability) out of a 
massive distributed computing resources [9] without upsetting 
concerning the position or interior structures of these 
resources. The National Institute of Standards and Technology 
(NIST) recognized four necessary distinctiveness of cloud 
computing: resource pooling [10]. The reputation of cloud 
computing be obliged to amplify in the network speed, and to 
the reality that virtualization and network computing 
technologies have turn into commercially accessible. It is 
predictable that endeavors will hasten their movement from 
construction and possessing their individual systems to 
leasing cloud computing services [11].  
 
 
To run common server resources [12], in this services “offer” 
for possessions as a purpose of distributed performance. In 
this work, an optimal resource allocation method is used for 
optimization of resource usage based on users’ task. 
3. PROPOSED OPTIMAL RESOURCE 
ALLOCATION TECHNIQUE (ORAT) 
FOR GREEN CLOUD COMPUTING 
ENVIRORNMENT 
The proposed work is efficiently designed to optimize 
resource allocation under the supposition that the requisite 
resource can be obtained from a joint resource pool. Besides, 
to be capable to offer processing capability and storage space 
facility, it is essential to assign bandwidth to entrée them at 
the similar time. The architecture diagram of the proposed 
Optimal resource allocation technique (ORAT) for green 
cloud computing is shown in fig 2. 
As cloud computing services quickly enlarge their client 
support, it has developed into significant to afford them 
reasonably. To do so, it is necessary to optimize resource 
distribution under the statement that the necessary quantity of 
resource can be obtained from a widespread resource pool and 
borrowed out to the user on an hourly center. In addition, to 
be capable to present processing capability and storage space 
facility, it is required to preserve concurrently a network 
bandwidth to process them. Consequently, it is required to 
distribute several types of resources (such as processing 
capability, storage space capacity and bandwidth) 
concurrently in a synchronized way instead of assigning each 
type of resource separately. The quantity of resource vital and 
the period in which it is utilized are not permanent. They can 
differ really from user to user and since service to service.  
The paper proposed an optimal resource allocation method for 
green cloud computing environments. For the preface 
assessment, this paper presumes the utilization of two types of 
resources: processing capability and bandwidth. In common, 
services can be divided into two groups: a non-delay system 
(defeat system) and a waiting scheme. A non-delay system 
assigns an additional resource instantly to the user ahead the 
advent of the request, and discards the request if there is no 
additional capacity. A waiting system assigns an additional 
capability to users in the series in which their needs have 
arrived, as a substitute of assigning resources instantly upon 
the advent of a request. This paper presumes a service that 
sprints as non-delay. This paper also thinks stationary 
resource distribution, which is the most essential structure of 
resource distribution, although active provision, which uses 
procedure immigration and bandwidth consolidation, can 
augment the exploitation of resources. 
 
 
 
 
 
 
 
 
 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
22 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Fig 2: Architecture diagram of the proposed ORAT 
3.1 Resource allocation in cloud computing environment 
The resource allotment in a cloud computing environment can 
be represented as assigning the requisite quantity of numerous 
types of resource concurrently from a widespread resource 
pool for a definite stage of time for every request. The owed 
resources are committed (not shared) to every request.  For 
the preface assessment, this paper proposes two types of 
resource: resource processing capability and bandwidth.  
The requisite quantity of resource and the interlude of time in 
which it is utilized are not predetermined. They can differ 
significantly from user to user and from service to service. For 
instance, video delivery, file transfer, and videoconferencing 
services need a huge quantity of bandwidth but not so greatly 
resource processing capability. In distinction, a secretarial 
service needs a huge amount of processing capability but not 
so much bandwidth. It is understood that the hardware 
resources for green cloud computing services are not mounted 
at a distinct center, but in numerous biologically dispersed 
centers, as shown in Figure 3., in order to assist accumulation 
of resources, to employ load balancing and to guarantee high 
consistency. Each center has servers (counting virtual servers) 
that offer processing capability, and bandwidths that present 
access to these servers.  
 
Centre 1       Centre 2  Centre n 
 
 
                             ….    
 
 
 
 
                      
         ---server’s processing ability 
       --- Link (Bandwidth) 
 
C
max j
 - Maximum size of resource processing capability at    
           center j 
N
maxj - 
Maximum size of bandwidth at center j 
Fig 3: System model for cloud computing services 
When a new request is produced, one hub from between k 
centers is chosen consistent with the resource distribution 
algorithm. The highest size of processing capability and 
bandwidth at center j (j=1, 2,..,n) is specified to be C
maxj
 and 
N
maxj
 correspondingly.  The notion of resource allocation 
obtains the resource practice stage into deliberation. When a 
service request appears, the finest center is chosen from 
numerous centers, and both the processing capacity and 
bandwidth accessible in the chosen center are owed for a 
definite period of time. The processing capacity and 
bandwidth of only this particular center are owed. If no center 
has a sufficient quantity of auxiliary resources (both 
processing capacity and bandwidth), the request is discarded. 
The resources owed are unconfined after the usage period has 
gone. 
3.2 Optimal resource allocation technique for green cloud 
computing environment 
The objective of the proposed optimal resource allocation 
technique (ORAT) for green cloud computing allocation is to 
core i7 processor pool 
core 2 duo processor pool 
core i7 processor pool 
P3 processor pool P4 processor pool 
Optimal resource allocation 
technique 
Allocate the processor 
to each user 
Set of users 
 
 
. 
. 
 
 
 
 
… … … … …
… .. 
U1 
U2 
Un 
Improve Bandwidth, resource 
utilization 
 
 
 
 
Cloud computing environment 
           Cmax 1  
 
 
 
        Nmax 1              
           Cmax 2  
 
 
 
        Nmax 2              
           Cmax n  
 
 
 
        Nmax n             
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
23 
exploit the number of requests to which both resource 
processing capability and bandwidth are maintained well. As 
the amount of requisite resource processing capability does 
not usually have a rigid association with that of requisite 
bandwidth, the finest resource allocation cannot be attained if 
only a single resource kind is measured in the assortment of a 
center.  
The resource that needs the prevalent balanced size of 
resource, contrast the amount of requisite resource with the 
greatest resource size for every resource type, is first chosen 
as ‘recognized resource’. Then the smallest amount accessible 
of the recognized resource from amongst k centers is chosen. 
In this case, the parts of resource processing capability and 
bandwidth are diverse, being considered in proportion of CPU 
power and b/s (bits per second) correspondingly. It is 
proposed to evaluate the size of the diverse resources as in the 
subsequent instance. Suppose that the highest quantity of 
bandwidth in a center is 100Mb/s. A demand for 20% of CPU 
power and 30Mb/s needs 20% of resource processing capacity 
and 30% of bandwidth correspondingly. As the amount of 
requisite bandwidth is better than that of requisite processing 
capability, bandwidth will be the known resource in this case.   
        Centre 1           Centre 2           Centre n 
 
TPA 
 
 
      . . . 
TB 
 
 
 
Allocated Resources 
TPA-Total Processing Ability 
TB-Total Bandwidth 
Fig 4: Possible resource allocation for a user 
When a service request task is generated by the user, the 
subsequent resource allocation algorithm is conceded out and 
shown in fig 4.  
i) Assortment of known resource   
   if X
C
>X
N
 then processing capacity is the recognized 
resource.  
   Else bandwidth is the recognized resource.  
Where  
   X
C 
= {the amount of necessary processing capacity}/XC0 
   X
C0 
= Min {the highest amount of processing facility in a 
center}  
   X
N 
= {the size of requisite bandwidth}/XN0 
     X
N0 
= Min {the greatest amount of bandwidth in a center}  
   For instance, if there are two centers and the greatest 
amount of resource processing ability of every center is 100 
and 50 correspondingly, XC0 will be 50. 
ii) Assortment of the center 
    The center which suits the subsequent three conditions will 
be chosen:  
   a.) Min {the obtainable size of the notorious resource in the 
center}   
   b.)Accessible processing capacity in the center is equivalent 
to or superior than the requisite processing ability.  
     c.) Accessible bandwidth in the center is equivalent to or 
better than the requisite bandwidth.  
If there are two or more centers which persuades the above 
conditions, one center will be chosen at arbitrary.  Remind 
that the request will be discarded if there are no centers that 
persuade the above conditions.  
iii) Distribution of resource  
      Both requisite processing capacity and bandwidth are 
owed concurrently in the chosen center. The maintained 
resources are applied (not common) to the request.  
iv) Resource released 
   When the service time has concluded, both owed processing 
capability and bandwidth will be confined concurrently. 
The above algorithm is followed for resource allocation 
process based on an optimization of resource usage based on 
CPU cycles, bandwidth, processing ability it needs to process 
the user given tasks and an experimental evaluation is 
conducted to estimate the performance of the proposed ORAT 
and described in next section.  
4. EXPERIMENTAL EVALUATION 
The proposed Optimal resource allocation technique (ORAT) 
for green cloud computing is implemented in Java using 
cloudsim software. The proposed optimal resource allocation 
technique (ORAT used old range, mid range and high end 
processors. The old range processors includes 286, 386, 
Pentium, the mid range processors includes Pentium pro, 
Pentium III, Pentium IV, the high end processors be core2, 
core i7. These types of processors pools are integrated to 
analyze the performance of the proposed optimal resource 
allocation technique (ORAT) for green cloud computing 
based on processors utilization. The number of tasks assigned 
to the processor is based on the capability of the processor in 
the resource pool measured in terms of CPU cycles, 
bandwidth, and data rate. The proposed ORAT first identifies 
the tasks schedule, resource/processor capability. An 
optimized resource allocation takes place under the 
assumption that the required resource can be taken from a 
shared resource pool. In addition, to be able to provide 
processing ability and storage capacity, it is necessary to 
allocate bandwidth to access them at the same time. 
Operations can be assigned to the pool of old range and mid 
range processors with high end processors. The proposed 
ORAT for cloud computing infrastructure is measured in 
terms of: 
i.)Request loss probability. 
ii.)Resource utilization. 
iii) Data transfer rate. 
5. RESULTS AND DISCUSSION 
In this work, we have seen how a pool of processors can be 
allocated to users’ tasks based on optimal resource utilization 
technique to capture the performance of cloud computing 
process in Green IT to other systems written in mainstream 
languages such as Java. We run independent tests with several 
number of resources task with a constant number of tasks sent 
by each users. The entire process of the proposed Optimal 
resource allocation technique (ORAT) for green cloud 
computing is explained in section 3 briefly and this section 
International Journal of Computer Applications (0975 – 8887) 
Volume 55– No.5, October 2012 
24 
described the performance of the proposed architecture. 
Compared with an existing integrated time and task based 
process schedule to reduce the accumulate the e-waste which 
describes only about the assignment of users’ task schedule 
not keen about the resource wastage, the proposed ORAT 
architecture provides a better results in terms of utilization of 
resource for eradicating the e-waste by accumulating 
minimum amount of energy. The given below table and graph 
shows the result of the proposed ORAT for green cloud 
computing compared with existing integrated time and task 
based process schedule.  
Table 1. No. of Resources Vs. Request loss probability 
 
No. of requests 
Request loss probability (%)  
Proposed ORAT 
for green  cloud 
computing 
Existing ITTPS 
2 0.5 0.8 
4 0.9 1.6 
6 1.2 1.9 
8 1.4 2.3 
10 1.5 2.5 
 
The above table (table 1) describes the users’ task request loss 
probability to determine an efficienct resource utilization. The 
effect of the proposed Optimal resource allocation technique 
(ORAT) for green cloud computing is compared with an 
existing integrated time and task based process schedule.  
 
Fig 5.  No. of Resources Vs. Request loss probability 
Fig 5. describes the users’ task request loss probability based 
on number of resources present in the cloud computing 
environment. It compares the request loss prospect in the 
situation where the sizes of processing capability and 
bandwidth (C and N) ascend and drop in anti-phase, i.e., a 
great processing capability is pursued by a huge bandwidth. 
Assess the impact of ratio of greatest resource size of every 
center on the request loss possibility, assuming the total size 
of processing capability (Cmax1+Cmax2) and that of 
bandwidth (Nmax1+Nmax2) are stable. The proposed ORAT 
can decrease the request loss possibility and as a effect, 
decrease the total amount of resource contrast with an existing 
integrated time and task based process schedule, in the 
situation where the sizes of processing capacity and 
bandwidth increase and descend in anti-phase. This is also 
accurate even if the number of centers augments apart from 
for the case where the number of centers odd. 
Table 2. No. of Users Vs. Average Resource Utilization 
No. of users Average resource utilization (%)  
Proposed ORAT for 
green  cloud 
computing 
Existing ITTPS 
5 54 31 
10 63 43 
15 75 52 
20 82 60 
25 89 65 
 
The above table (table 2.) describes the utilization of 
resources in a meaningful manner without producing e-waste 
to determine efficient resource utilization. The effect of the 
proposed Optimal resource allocation technique (ORAT) for 
green cloud computing is compared with an existing 
integrated time and task based process schedule.  
 
Fig 6. No. of Users Vs. Average resource utilization 
Fig 6. describes the optimal resource utilization based on 
number of users present in the cloud computing environment 
with the tasks they need to process. In the proposed ORAT, 
the resource allocation is made to the users’ task based on the 
CPU cycles, bandwidth and data rate of the respective 
resource. Based on the resource capability, the task has been 
assigned by the framework to evaluate the utilization of 
resource in a meaningful manner without throwing as a waste. 
The resource utilization is measured in terms of average 
number of resources are entirely used by the tasks allotted to 
it. Compared to an existing ITTPS, the proposed optimal 
resource allocation technique (ORAT) for green cloud 
computing outperforms well in resource utilization and the 
variance is 40-50% high in the proposed ORAT. 
The below table (table 3.) describes the transfer rate of data 
based on number of tasks hold by the users. The effect of the 
proposed Optimal resource allocation technique (ORAT) for 
green cloud computing is compared with an existing 
integrated time and task based process schedule.  
 
 
2
4
6
8
10
0
0.5
1
1.5
2
2.5
Request loss 
probability
No. of requests
Proposed ORAT Existing ITTPS
5
10
15
20
25
0
20
40
60
80
100
Average resource 
utilization
No. of users
Proposed ORAT Existing ITTPS
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