A REVIEW OF RESOURCE ALLOCATION TECHNIQUES IN CLOUD …€¦ · Key words: Cloud computing, Resource allocation. Cite this Article: Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti

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A REVIEW OF RESOURCE ALLOCATION

TECHNIQUES IN CLOUD COMPUTING Rishi Aluri

Department of Computer Engineering, SVKM NMIMS, Mukesh Patel School of Technology

Management and Engineering, Shirpur Campus

Shriya Mehra



Apoorva Sawant



Pankti Agrawal



Mayank Sohani



ABSTRACT

In this world where technology changes so rapidly, cloud computing field has

managed to make a huge impact towards customer satisfaction in a real-time

environment. Cloud computing has made a huge reduction in cost of storage and has

led to efficient management and processing of data. Cloud consumers request various

services based on their dynamically changing needs. The resources are limited, which

makes it tough to provide all the requested resources. The providers of cloud have to

make sure that the services are distributed in such a manner that it meets the needs of

the consumers as well as makes efficient use of resources. So, resource allocation has

a very crucial role. This paper mainly focuses on the existing techniques for resource

allocation, comparisons between the techniques and summarizes them.

Key words: Cloud computing, Resource allocation.

Cite this Article: Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal and

Mayank Sohani, A Review of Resource Allocation Techniques in Cloud Computing.

International Journal of Computer Engineering and Technology, 9(2), 2018, pp. 51-

60.

http://www.iaeme.com/IJCET/issues.asp?JType=IJCET&VType=9&IType=1

Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti Agrawal and Mayank Sohani


1. INTRODUCTION

1.1. Cloud Computing [1][2]

The cloud computing is based on the simple and effective principle i.e. resource sharing. In

cloud computing we basically share resources over the internet i.e. we store the data in the

cloud and the group of organizations accesses it over the internet.

Cloud computing represents a group of systems which are connected in a network to each

other to provide an infrastructure that can be changed in size or scale which will enable the

data and the software systems to make use of it [2].

Cloud computing has two models. One is Deployment Model and the second one being

Service Model.

1.1.1. Deployment Model [1]

Deployment model defines how to access the cloud. There are four different types of cloud

defined:

Public Cloud [1]: Allows the users to access the services as well as system easily. It can be

owned, operated and managed by businesses, academics or government organization or they

can work in combinations as well. Examples are Amazon, Google, and Microsoft.

Private Cloud [1]: Allows the users to access the services and systems within the organization

itself. It can be owned, operated and managed by the organization itself or maybe the third

party can come into the picture. Examples are IBM, Sun, Oracle.

Community Cloud [1]: It provides services to a group of organizations that can be owned,

operated and handled by those organizations or by a third party. Examples are Microsoft

Government community cloud.

Hybrid Cloud [1]: It is the combination of private cloud and public cloud where the exacting

activities are executed using the private cloud whereas the activities that are not that critical or

demanding are performed using public cloud. Examples: Windows Azure, VMware vcloud

1.1.2. Service Model

Service models are of three types:

Software-as-a-service (SAAS) [1]: SAAS or On-demand Service allows the end users to use

the software applications. It is a service, which can be accessed across the globe until you

have a computer and an internet connection. Examples are Gmail, Yahoo mail.

Platform-as-a-service (PAAS) [1]: PAAS offers a runtime environment for both developing as

well deploying of applications. It is free of cost. The developers can develop their software

and can execute the same on this platform. Examples are: Salesforce.com, Windows Azure.

Infrastructure-as-a-service [1]: Service that can be rented for a limited period like a trial

version of an application is called IAAS. In this service, we get access to resources such as

virtual machines, virtual storage, physical machines etc. Example: Amazon EC2.

1.2. Resource Allocation in Cloud Computing

In cloud computing, Resource allocation is the process in which different cloud applications

demand different resources for executing their tasks over the internet. These different

resources are allocated to the cloud applications to complete application tasks in a real-time

cloud environment. Resource allocation techniques are applied to each independent unit

allowing service providers to efficiently utilise the resources [2].

Resource allocation strategy [13] integrates the activities that are provided by the cloud

for the allocation and utilization of scarce resources, which are in the scope of cloud

A Review of Resource Allocation Techniques in Cloud Computing


environment that meets the needs of a cloud application. To accomplish the job completely

the application should be provided with the type and amount of resources it needs. For

allocation to be optimal, we also need the order and the time of the resources. One should

avoid the following conditions [13]:

Resource dispute situation arises when at the same moment of time two application try to

access the resources.

The situation of Lack of resources arises when the number of resources is finite.

Resource mutilation is a situation, which arises when resources are not granted to the

applications in spite of the availability of resources.

An under-provisioning situation, which arises when the application is allocated, only limited

amount of resources.

Over-provisioning situation arises when the application is provided with the surplus amount of

resources than needed.

2. RELATED WORK

Pandaba Pradhan, Prafullu Ku. Behera & BNB ray [11] have proposed a modified round-

robin algorithm for allocation of resources in a real-time cloud environment. This algorithm

has made use of two registers that are:

SR - It is used for storing the addition of the burst time for the processes left in the queue

AR - To calculate AR we must need to do following steps:

Realise the value present in the SR

Find the total number of requests present in the queue.

Now, calculate and store the avg. value of burst time by dividing the value found from Step 1

by value found from Step 2.

After execution of a process, it will be removed from the ready queue if its burst time is

finished else it will be added at the end of the queue and accordingly, the SR & AR will be

updated.

Weiwei Lin, James z. wang, Cheng Liang & Deyu Qi [10] have proposed a threshold

based allocation of resources in a real-time cloud environment. The main work in this

technique is to monitor and predict the resources that are needed and then efficiently utilise

the resources based on the need of the application. This scheme works because of two

important procedures. The first is the Broker that runs on the clients physical machine with

the application and then second comes the Datacentre that manages the physical resources

such as CPU and RAM. The datacentre waits for the broker to send the requests from the

client and when extra resources are required then it provide those resources to complete the

request. If resources are excessive then it revokes the excessive virtual resources.

Lixia Liu, Hong Mei1, Bing Xie proposed MQLB-RAM [6] and it is based on various

resource allocation techniques. In this paper the workload distribution and computation of

resources in a real-time cloud environment is done by allocating the available resources to

multiple computers, networks or servers. It integrates the requirements of clients and cloud

service providers to form multi-QOS indexes. By using the load-balancing technique and a

constant analysis on the load-balancer, the needs of the clients such as minimum cost, high

performance system, and good reliable network are met. To attain load balancing by using the

resources and money efficiently, the algorithm also measures the difference between the

weights of each index in the peer.



Chandrasekhar S. Pawar and Rajnikant B. Wagh [9] have proposed priority based

dynamic allocation of resources in real-time cloud environment. In dynamic resource

allocation, there is a dynamic response to the varying workload. The additional resources are

assigned or allocated and virtual machines are created and migrated. It runs on the production

time. To achieve Service level agreement, the algorithm proposed responds dynamically to

varying workload by pre-empting the lower priority task, which is being currently performed

with the high priority task that has been added dynamically. If the two colliding tasks have

similar priority, then virtual machines are created from available resources.

As proposed by Marwa Hashim eawna, Salma Hamdy Mohammed & El-Sayed M.

El-Horbaty [5] the main problem was of resource provisioning. Resource utilization must be

improved and the time and effective cost need to be reduced in order to meet the customer

satisfaction. Particle Swarm Optimization based scheduling heuristic was presented by Suraj

Pandey et al. The average computation cost was calculated by performing each task of an

application on various resources and sequencing them based on their cost. This paper suggests

using the algorithm based on Simulated Annealing for scheduling of tasks in the cloud

infrastructure. This scheme uses the concept cooling and ultimately freezing of metal into a

crystalline structure with minimum energy. It begins at the initial temperature T then the new

cost is computed and a new gain variable (NG) is assigned to it. The solution is accepted

when the computed NG is less than or equal to the current gain.

As proposed by Hwa Min Lee·Young-Sik Jeong·Haeng Jin Jan [7], the resources

present in each platform is handled by the scheduler of the VM. It uses Round Robin

algorithm and First Fit Greedy algorithm. In the Greedy algorithm, the operator of the VM is

chosen as host when a node is found to be firs. Open Nebula is also an open source tool,

which provides the cloud services over various hardware. It can be public, private or hybrid.

Open Nebula makes use of the Virtual Machine scheduler also called the matchmaking

scheduler makes a priority list of the available resources. Requirements and Pre-defined rank

are taken as the input to generate the resource number the virtual machine is kept as output.

When the scheduler receives the virtual machine request, those requests, which do not fit into

the requirements, are discarded by it.

Anthony Thomas, Krishnalal, Jagathy Raj [3] have identified two parameters as the

basis for scheduling resources in a real-time cloud environment. The paper focuses on

providing a solution that achieves user satisfaction and full utilization of resources. The two

parameters consider are – First parameter is the Task Length and second is the Priority. Every

task is allocated a credit based on these two parameters. Task Size Credit is assigned to tasks

by first calculating average task length (avgT) and then by calculating the difference (TD) in

task sizes(T) for each task to that of the average task length. Compare TD of each task with

certain values devised from conditions to assign Task Size Credit to each task. Task Priority

Credit is assigned by finding a priority factor by dividing each task’s priority by a divisibility

factor. For E.g. - if the priority is a two digit number then divisibility factor will be 100 and

likewise. This Priority Factor is Task Priority Credit. Total Credit is the multiplication of Task

Size Credit with the Task Priority Credit. In this way, each task is allocated resources based

on their credits achieved.

Narander Kumar, Swati Saxena [8] divided the resource allocation technique into two

steps. First, A market-driven auction system to ensure trust along with equal opportunity and

provide service provider a chance to maximise profit. Second, A preference-driven payment

system to make sure that the winner is provided the resource and happens to pay a lesser

amount than the provided bid value. The first step is divided into - First, A pre-auction step,



where the service provider acquires the initial price for each Virtual Machine(VM) instance

by taking the average of the bidding prices on that VM instance from the previous allocation

round. Second, A market-driven open auction where the service provider invites all the

bidders and the bidding capacity of those bidders whose bidding price is equal to or greater

than the established starting price are selected. Then the cloud provider calculates the Mean

Total Bid Price(MeanTBP) from all the Total Bid Prices(TBP) from the bidders. The bidders

whose TBP is greater or equal to MeanTBP are considered winners of the first halves and are

allocated resources. In the second half, new bidders and the unselected bidders from the

previous round bid for required resources and again the same process follows for selection of

winners. In the second step of the technique, a calculation of how much the winner actually

has to pay is done. This is calculated based on their preference or need in three things - Task

Deadline(Fixed/Flexible), Service Time(Immediate/Flexible), VM’s Possession(Full-

time/Partial). The Actual Payment is calculated from the preferences of Winner.

Saraswati AT, Kalaashri.Y.RA, Dr.S.Padmavathi [4] have proposed the use of

dynamic Virtual Machine (VM) allocation to achieve scheduling of tasks and proper

utilization of resources. In this paper, the priority of jobs is given based on the deadline for the

job. The existing VMs are given all the low priority jobs. When a High priority job enters, a

low priority job has to pre-empt its resources allowing the higher priority job to work in its

resources. When no VM is free for executing a task this algorithm finds a low priority job

causing it to pause and allocating its resources to the requesting higher priority job. Each job

has a lease type such as Cancellable, Suspendable, Non-preemptable that impact the

conditions for preempting of a job. This technique helps in avoiding creation of new VM’s on

arrival of new jobs thus increasing the overall utilization of resources and also reducing the

overhead cost of VM’s.

3. RESOURCE ALLOCATION TECHNIQUE

3.1. Round Robin Algorithm

Round robin algorithms are designed for mainly time-sharing systems. This algorithm is

amongst the oldest and widely used algorithms. It is considered to be very useful. Round

robin algorithm consists of a circular queue which is called as a ready queue which consists of

processes waiting in line for their execution, quantum which is a defined unit of time [11].

The new processes that are generated are added at the end of the queue.

In round robin algorithm the first process is allocated to the CPU with one quantum of

time and after that CPU dispatches that process. If the process is not completed during that

one quantum, then it is removed from the queue and added at the end of the queue otherwise it

is simply removed from the queue since execution of this process has been completed. If the

execution time required by a process is less than one quantum, then as soon as the execution

is complete the process leaves the CPU.

3.2. Shortest Job First Algorithm

As the name suggests in the shortest job first technique, the process with the least execution

time will be processed in CPU first. It is same as the FCFS algorithm except that it chooses

the shortest process first rather than choosing the first job in the queue.

In shortest job first, all the processes are kept in the ready queue in the increasing order of

their execution time. After arranging them in the ready queue, one by one they are granted the

CPU time for completely executing their process [11].



3.3. Load balancing Technique

Load balancing is the process of increasing the performance of a cloud environment by

properly distributing the workload among different processors. It means that no machine is

heavily loaded, partially loaded or idle and hence there is a uniform distribution of load. The

number of cloud consumers has been rising day by day and hence the workload is increasing

on the machines. If not taken care of, this can lead to poor performance of the system,

wastage of resources and client dissatisfaction [7].

Therefore, a good load balancing mechanism should be used to increase stability,

flexibility, performance and maximize resource utilization.

3.4. Priority-based resource allocation Algorithm

Priority based scheduling refers to the method of scheduling processes based on their priority.

Priority is assigned to every process and the process with higher priority is executed first. If

two tasks have the same priority, then they are executed on the basis of first-come-first-served

or round robin basis.

It can be of 2 types [10]:

Pre-emptive: if the newly arriving processes have higher priority than the existing ones then

the CPU is pre-empted.

Non-preemptive: when a new process arrives then the CPU isn’t pre-empted, instead, it is

placed at the top of the ready queue.

3.5. Hybrid Resource Allocation Algorithm

In resource provisioning, this approach is used and is called as Hybrid Algorithm. This is a

metaheuristic technique used in the area of cloud computing to allocate resources in a multi-

tier application. Here Particle Swarm Optimization is used as a searching technique used for

local searches and then elect the best position locally that is known as LBest and Simulated

Annealing is used as a searching technique used for global searches and then elect the global

best position known as GBest. Simulated Annealing applies the global search technique to

select the GBest and searches around GBest to get the best suitable one [5]. The algorithm is

as follows [5]:

Step1- Start with the generation of initial velocity and initial population.

Step2- Using the following equations, compute the value of the objective function:

(Where ST defines to be Fist Time, ECT implies Expected Completion time, DU means the

Duration Time, EET signifies Estimated Execution Time and Finish Time is FT)

Step3- Now on the basis of the objective function values classify the initial population

Step4- Now choose the local best position or LBest

Step5- Now depending on LBest select the GBest

Step6- Put SA to search around the GBest. If the solution comes out to be better than the

earlier GBest, exchange it with the new GBest.

Step7- The algorithm stops when we get i= i+1 as the last iteration, otherwise return to step4

Step8- Check if there is a need to relocate the GBest and hence get the output.



3.6. Performance-based Algorithm

Performance-based resource allocation technique is used for coherent and ordered allocation

of VMs in the cloud environment. The system includes a VM Allocation control manager and

a Node Performance Analyser. Every provider has a software that performs scheduling

operations operating in its data center [8]. The communication between the schedulers takes

place. Having the knowledge of the current statuses of the VMs, the decisions can be taken

accordingly. The objective is to evaluate the calculation ability of ach node and make use of

this information. The performance of a node is evaluated with the help of two parameters.

One is the stored performance of memory and the calculation time of CPU. Using the LU

decomposition, we can get the inverse matrix. The equations given are [8]:

CPU chooses the optimal node giving importance to the storage, performance of node,

and memory.

In the resource allocation algorithm, whenever a cloud is provided with a job, the job is

broken down into different subtasks by the scheduler. Now on the basis of the information

obtained from the other schedulers, the scheduler assigns each cloud with a particular task.

This request is sent using the internet, to the cloud service provider. These requests are given

to different virtual machines, which can perform the required operation. Now the VMs are

generated using specific tools. The details of the machine are sent to the virtual machine

scheduler.

3.7. Min-Min Algorithm

This algorithm is a load-balancing algorithm [3] that distributes the load, based on the task

sizes, among the available resources. It functions upon a list of tasks with some resources

required for all tasks. The algorithm focuses on selecting and allocating the resources to the

task with the least completion time. After a task is completed, it is removed from the task list.

This algorithm continues till all the tasks in the list are completed.

3.8. Dynamic resource allocation technique

Over a period, the workload on applications keeps on fluctuating for the lifetime. In static

resource allocation scheme, the application may have degraded due to lack of resources or

sometimes each resource is not used and hence is wasted [4]. This is the reason being users

prefer to use dynamic allocation technique to correctly allocate the resources to the

applications according to their needs and hence help in the utilization of resources properly.



4. OBSERVATION

Table 1 Observation Table

Paper

Reduce

Overhead

Cost

Task

Size

Task

Priority

Task

Deadline

Open

Market

Auction

Dynamic

ReAllocation/Allocation

Preemptable

Jobs

Load

Balancer

Resource

Provisioning

Dynamic Resource

Allocation Scheme

in Cloud

Computing[4] Credit-Based

Scheduling

Algorithm in Cloud

Computing

Environment[3] A Preference-

Based Resource

Allocation In

Cloud Computing

Systems[8] Modified Round

Robin Algorithm

for Resource

Allocation in Cloud

Computing[10] A Threshold-Based

Dynamic Resource

Allocation Scheme

for Cloud

Computing[11] Towards A Multi-

Centric Human-

Centric Cloud

Computing Load

Balancing

Resource

Allocation

Method[6] Priority Based

Dynamic Resource

Allocation in Cloud

Computing[9] Performance

Analysis Based

Resource

Allocation for

Green Cloud

Computing[7] Hybrid Algorithm

for Resource

Provisioning of

Multi-Tier Cloud

Computing[5]



Figure 1 Observation visualising using graph

5. CONCLUSIONS

Cloud computing has revolutionized the age of computing by increasing flexibility,

collaboration, security and reducing cost and infrastructure. Since the cloud, consumers are

increasing over time and so are their dynamically changing needs, it is necessary to make the

maximum and optimum utilization of the available finite resources. If used wisely, proper

resource allocation techniques can lead to great data management and processing leading to

great client satisfaction as well as best use of limited resources. This paper has discussed

various existing resource allocation techniques, comparisons between them and proposed

solutions by different authors.

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Reduce Overhead Cost Task Size Task Priority

Task Deadline Open Market Auction Dynamic ReAllocation/Allocation

Preemptable Jobs Load Balancer Resource Provisioning



REFERENCES

[1] Kamboj, Sheenam and Navtej Ghumman. “A survey on cloud computing and its types.”

2016 3rd International Conference on Computing for Sustainable Global Development

(INDIACom):2917-2974.

[2] S. M. Parikh, "A survey on cloud computing resource allocation techniques," 2013 Nirma

University International Conference on Engineering (NUiCONE), Ahmedabad, 2013

[3] Antony Thomas, G. Krishnalal, V.P. Jagathy Raj, Credit Based Scheduling Algorithm in

Cloud Computing Environment, In Procedia Computer Science, Volume 46, 2015, Pages

913-920, ISSN 1877-0509

[4] A.T. Saraswathi, Y.R.A. Kalaashri, S. Padmavathi, Dynamic Resource Allocation Scheme

in Cloud Computing, In Procedia Computer Science, Volume 47, 2015, Pages 30-36,

ISSN 1877-0509

[5] Marwah Hashim Eawna, Salma Hamdy Mohammed, El-Sayed M. El-Horbaty, Hybrid

Algorithm for Resource Provisioning of Multi-tier Cloud Computing, In Procedia

Computer Science, Volume 65, 2015, Pages 682-690, ISSN 1877-0509

[6] Lixia Liu, Hong Mei, and Bing Xie. 2016. Towards a multi-QoS human-centric cloud

computing load balance resource allocation method. J. Supercomput. 72, 7 (July 2016),

2488-2501.

[7] Hwa Min Lee, Young-Sik Jeong, and Haeng Jin Jang. 2014. Performance analysis based

resource allocation for green cloud computing. J. Supercomput. 69, 3 (September 2014),

1013-1026.

[8] Narander Kumar, Swati Saxena, A Preference-based Resource Allocation in Cloud

Computing Systems, In Procedia Computer Science, Volume 57, 2015, Pages 104-111,

ISSN 1877-0509.

[9] Chandrashekhar S. Pawar and Rajnikant B. Wagh. 2012. Priority Based Dynamic

Resource Allocation in Cloud Computing. In Proceedings of the 2012 International

Symposium on Cloud and Services Computing (ISCOS '12). IEEE Computer Society,

Washington, DC, USA, 1-6.

[10] Pandaba Pradhan, Prafulla Ku. Behera, B.N.B. Ray, Modified Round Robin Algorithm for

Resource Allocation in Cloud Computing, In Procedia Computer Science, Volume 85,

2016, Pages 878-890, ISSN 1877-0509.

[11] Weiwei Lin, James Z. Wang, Chen Liang, Deyu Qi, A Threshold-based Dynamic

Resource Allocation Scheme for Cloud Computing, In Procedia Engineering, Volume 23,

2011, Pages 695-703, ISSN 1877-7058.

[12] Mohammad Mehedi Hassan, Atif Alamri, Virtual Machine Resource Allocation for

Multimedia Cloud: A Nash Bargaining Approach, In Procedia Computer Science, Volume

34, 2014, Pages 571-576, ISSN 1877-0509.

[13] N. Kumar Pandey, S. Chaudhary and N. K. Joshi, "Resource allocation strategies used in

cloud computing: A critical analysis," 2016 2nd International Conference on

Communication Control and Intelligent Systems (CCIS), Mathura, 2016, pp. 213-216.

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A REVIEW OF RESOURCE ALLOCATION TECHNIQUES IN CLOUD …€¦ · Key words: Cloud computing, Resource allocation. Cite this Article: Rishi Aluri, Shriya Mehra, Apoorva Sawant, Pankti