A survey on various resource allocation policies in cloud computing environment

IJRET: International Journal of Research in Engineering and Technology ISSN: 2319-1163
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Volume: 02 Issue: 05 | May-2013, Available @ http://www.ijret.org 760
A SURVEY ON VARIOUS RESOURCE ALLOCATION POLICIES IN
CLOUD COMPUTING ENVIRONMENT
Vaghela Ankita
PG student, Department of Computer Engineering, Alpha College of Engineering and Technology, Gujarat, India,
ankita.solanki27@gmail.com
Abstract
Cloud computing is bringing a revolution in computing environment replacing traditional software installations, licensing issues into
complete on-demand services through internet. In Cloud computing multiple cloud users can request number of cloud services
simultaneously. So there must be a provision that all resources are made available to requesting user in efficient manner to satisfy
their need. Resource allocation is based on quality of service and service level agreement. In cloud computing environment, to
allocate resources to the user there are several methods but provider should consider the efficient way to guarantee that the
applications’ requirements are attended to correctly and satisfy the user’s need This paper survey different resource allocation
policies used in cloud computing environment.
Keywords: Cloud computing, Resource allocation
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1. INTRODUCTION
Cloud Computing is a technology that uses the internet and
central remote servers to maintain data and applications.
Cloud computing allows consumers and businesses to use
applications without installation and access their personal files
at any computer with internet access. This technology allows
for much more efficient computing by centralizing data
storage, processing and bandwidth.
Cloud computing is a model for enabling ubiquitous,
convenient, on-demand network access to a shared pool of
configurable computing resources e.g., networks, servers,
storage, applications, and services that can be rapidly
provisioned and released with minimal management effort or
service provider interaction.
Cloud computing customers do not own the physical
infrastructure; rather they rent the usage from a third party
provider. They consume resources as a service and pay only
for resources that they use.
Cloud computing provide three types of services[7], including
software as a service (SaaS),platform as a service (PaaS) and
infrastructure as a service (IaaS). In Software as a service
consumers purchase the ability to access and use an
application or service that is hosted in the cloud. A benchmark
example of this is Salesforce.com, as discussed previously,
where necessary information for the interaction between the
consumer and the service is hosted as part of the service in the
cloud. In Platform as a service Consumers purchase access to
the platforms, enabling them to deploy their own software and
applications in the cloud. The operating systems and network
access are not managed by the consumer, and there might be
constraints as to which applications can be deployed. In
Infrastructure as a service Consumers control and manage the
systems in terms of the operating systems, applications,
storage, and network connectivity, but do not themselves
control the cloud infrastructure.
Resource Allocation
Resource allocation [8] is a subject that has been addressed in
many computing areas, such as operating systems, grid
computing and datacenter management. A Resource
Allocation System (RAS) in Cloud Computing can be seen as
any mechanism that aims to guarantee that the applications’
requirements are attended to correctly by the provider’s
infrastructure. Along with this guarantee to the developer,
resource allocation mechanisms should also consider the
current status of each resource in the Cloud environment, in
order to apply algorithms to better allocate physical and/or
virtual resources to developers’ applications, thus minimizing
the operational cost of the cloud environment.
Cloud resources can be seen as any resource (physical or
virtual) that developers may request from the Cloud. For
example, developers can have network requirements, such as
bandwidth and delay, and computational requirements, such as
CPU, memory and storage. Generally, resources are located in
a datacenter that is shared by multiple clients, and should be
dynamically assigned and adjusted according to demand. It is
important to note that the clients and developers may see those
finite resources as unlimited and the tool that will make this
possible is the RAS. The RAS should deal with these
unpredictable requests in an elastic and transparent way. This

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elasticity should allow the dynamic use of physical resources,
thus avoiding both the under-provisioning and over-
provisioning of resources.
There are different resource allocation policies that are used in
cloud computing environment. Each of this policy uses certain
methods and algorithms which are given below:
2. DIFFERENT RESOURCE ALLOCATION
POLICIES
A time-driven adaptive mechanism for cloud resource
allocation [1]
Cloud computing service providers deliver their resources
based on virtualization to satisfy the demands of users. In
cloud computing, the amount of resources required can vary
per user request. Therefore, the providers have to offer
different amounts of virtualized resources per request. To
provide worldwide service, a provider may have data centers
that are geographically distributed throughout the world.
Likewise, the user locations vary in geographic location. Since
cloud computing services are delivered over the internet, there
may be undesirable response latency between the users and the
data centers. Hence, for the best service, the provider needs to
find a data center and physical machine that has a light
workload and is geographically close to the user. The
proposed model finds the best match for the user requests
based on two evaluations: 1) the geographical distances
between the user and data centers and 2) the workload of data
centers. Hence, the model allows the users to find a data center
that is guaranteed to be the closest distance and have the
lightest workload. Also, it finds a light workload physical
machine within the data center for a provider.
Multi-dimensional SLA-based Resource Allocation for
Multi-tier Cloud Computing Systems [2]
Resource allocation is the most important challenges in cloud
computing. The service provider should work hard for
allocating resources based on the client’s SLA (Service Level
Agreement).Force directed search algorithm is the solution for
SLA based resource allocation problem for multi-tier
applications in cloud computing .This algorithm considers the
Gold SLA, and Bronze SLA. The provider gives the guarantee
for the response time in Gold SLA. The requests are moved
forward and backward in multi-tier service model. The server
serves the backward requests. Probability Distribution
Function (PDF) is used for finding the arrival rate in the Gold
SLA. The resource management problem’s aim is to maximize
the total profit. The profit maximization problem has these
steps. It performs the summation of client’s utilities. It
calculates the operation cost of the services. This problem
does the multiplication the total power consumed during the
decision making time. It selects only one tier for servers. It
finds the ON servers. It calculates the allocated memory for
clients. The profit maximum problem is solved using upper
bound on the total profit. The force between clients and
servers are the major factor in the resource consolidation using
force directed search algorithm. This algorithm takes a client
based on the highest force towards a new server. It performs
the load replacement, if the server is available. The algorithm
performs the updating of forces between the clients and
servers. If there is no positive force differential, then the
algorithm stops its working. The algorithm saves the best
solution in each step.
Multi-dimensional Resource Allocation Algorithm in
cloud Computing [3]
Cloud computing has emerged as a new technology and it has
been increasingly adopted in many areas including science and
engineering as well as business. How to arrange large-scale
jobs submitted to cloud in order to optimize resource
allocation and reduce cost is an issue of common concern.
Paper present are two common ways to optimize resource
utilization. One is at the application level when applications
are arriving, other is in the period of applications running. In
this paper, author makes effort on the former way to address
multi-dimensional resource allocation problem by proposing a
resource allocation scheme using fewer nodes to process
user’s applications. To address multi-dimensional resource
allocation problem, raises several concerns. Firstly, allocate
method should decide which virtual machines should be
assigned with a new set of jobs. Secondly, there exists an
optimal set of nodes which can process new arriving
applications, how to find an efficient way to assign
applications in nodes is another issue should be solved. In
response to these issues, we use virtual machine as the
minimum resource allocation unit. When a new batch of
applications arrives, applications is decompose into several
types of jobs, each job with the same type has the same
requirement of resource. Aiming at the first issue mentioned
above, author formulate it by adding multi-dimensional
constraints in resource allocation process, which assure jobs
can be processed in nodes selected by object function. Aiming
at the second problem, under the purpose of optimize utility of
nodes, this nodes select problem as a binary integer
programming problem, and our object function can assure
using working nodes’ remain resources to process more jobs.
Unlike the existing resource allocation schemes which allocate
more physical resources (CPU, memory, etc.) to the exiting
virtual machine, this model assign jobs in running nodes to
make nodes work at a higher utilization.

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SLA-based Resource Allocation for Software as a
Service Provider (SaaS) in Cloud Computing
Environments [4]
SaaS is a software delivery method that provides access to
software and its functions remotely as a Web-based service. It
allows organizations to access business functionality at a cost
typically less than paying for licensed applications since SaaS
pricing is based on a monthly fee. In order to deliver hosted
services to customers, SaaS companies have to either maintain
their own hardware or rent it from infrastructure providers.
This requirement means that SaaS providers will incur extra
costs. Though the cost of the resources has to be minimum, it
is also important to satisfy a minimum service level to
customers. Saas providers are able to manage the variety of
customers, mapping customer requests to infrastructure level
parameters and considering heterogeneity of Virtual
Machines. The allocation method uses two different
algorithms such as Prof min Vm Max AvaiSpace and
ProfminVmMinAvaiSpace. First algorithm is designed to
minimize the number of VMs by utilizing already initiated
VMs. The criterion for reusing VMis, it should have
maximum available space. The algorithm optimizes the profit
by minimizing number of initiated VM. Moreover, it
minimizes number of violations caused by service upgrade
because VM hasthe maximum available space. In such a way,
it reduces the penalty caused by upgrading service. The
disadvantage of this algorithm is that it can decrease the profit.
The maximum available space is occupied by small number of
accounts and it leading other requests to be served by a new
VM. To overcome the disadvantages of this algorithm,
reducing the space wastage by using minimum available
space(MinAvaiSpace) Strategy instead of MaxAvaiSpace
Strategy. When there are more than one VM with same type,
deployed with the same product type as customer request
required, the VMs with enough available space to serve are
selected. Then request is scheduled to the machine with the
minimum available space in a best-fit manner). The proposed
algorithms minimize the SaaS provider’s cost and the number
of SLA violations based on the dynamic allocation of
resources to requests.
Adaptive Resource Allocation for Pre-empt able Jobs
in Cloud Systems [5]
In this paper authors propose an adaptive resource allocation
algorithm for the cloud system with preempt able tasks in
which algorithms adjust the resource allocation adaptively
based on the updated of the actual task executions. Adaptive
list scheduling (ALS) and adaptive min-min scheduling
(AMMS) algorithms are use for task scheduling which
includes static task scheduling, for static resource allocation, is
generated offline. The online adaptive procedure is use for re-
evaluating the remaining static resource allocation repeatedly
with predefined frequency. In each reevaluation process, the
schedulers are re-calculating the finish time of their respective
submitted tasks, not the tasks that are assign to that cloud.
Policy based resource allocation in IaaS cloud [6]Most of the
Infrastructure as a Service (IaaS) clouds use simple resource
allocation policies like immediate and best effort. Immediate
allocation policy allocates there sources if available, otherwise
the request is rejected. Best-effort policy also allocates the
requested resources if available otherwise the request is placed
in a FIFO queue. It is not possible for a cloud provider to
satisfy all the requests due to finite resources at a time. Haizea
is are source lease manager that tries to address these issues by
introducing complex resource allocation policies. Haizea uses
resource leases as resource allocation abstraction and
implements these leases by allocating Virtual Machines
(VMs). Haizea supports four kinds of resource allocation
policies: immediate, best effort, advanced reservation and
deadline sensitive. Proposed dynamic planning based
scheduling algorithm is implemented in Haizea that can admit
new leases and prepare the schedule whenever a new lease can
be accommodated. Experiments results show that it maximizes
resource utilization and acceptance of leases compared to the
existing algorithm of Haizea.
3. COMPARISON
Technique Method Parameter Findings
Time-Driven adaptive
mechanismfor cloud
resource allocation
Finding work load of data
center and distance
between user and data
center
Response time Better response time and
resource utilization
Multi-Dimensional SLA-
based resource
allocationfor Multi-tier
Cloud Computing Systems
Force directed search
algorithm
Service Level Agreement
Maximize total profit
A Multi-Dimensional
resource allocation
Algorithm in cloud
Computing
Multi-Dimensional
Resource allocation
algorithm
Use lightest node to
allocate resource
Increase Resource
Utilization and reduce cost
of data center

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SLA-based resource
allocation for Software as
a Service Provider
(SaaS) in Cloud
Computing Environments
ProfminVmMaxAvaiSpace
and
ProfminVmMinAvaiSpace
algorithm
Service Level Agreement
and Cost
Minimize the SaaS
provider’s cost and the
number of SLA violations
Adaptive Resource
Allocation for Pre-empt
able Jobs in Cloud
Systems
Adaptive list scheduling
(ALS) and adaptive min-
min scheduling (AMMS)
algorithms
Service Level Agreement Increase Resource
Utilization
Policy based resource
allocation in IaaS cloud
Four policies used
Immediate, best effort,
advanced reservation and
deadline sensitive
Service Level Agreement Maximize Resource
Utilization
CONCLUSION
Cloud computing is the delivery of computing as a service
rather than a product, whereby shared resources, software and
information are provided to users over the network. It is anon
demand service because it offers dynamic flexible resource
allocation for reliable and guaranteed services in pay as-you-
use manner to public. This paper discusses various resource
allocation policies based on service level agreement and
quality of service. All above methods used different
techniques for allocating resource to the user. Time driven
gives better response time and increase resource utilization.
Multidimensional SLA based algorithm save resources and
increase resource utilization. Multidimensional algorithm
increase resource utilization and reduce cost of data center.
SLA-based policy minimizes the sass provider’s cost and the
number of SLA violations. Adaptive resource allocation
policy increase resource utilization. Policy based resource
allocation maximize resource utilization.
REFERENCES
[1] GIHUN Jung; KWANG MONG Sim; PAUL C. K.
Kwok; MINJIE Zhang. A TIME-DRIVEN Adaptive
Mechanism for Cloud Resource Allocation.
Proceedings of IC-BNMT2011, 2011, PP441-446
[2] HadiGoudarzi, MassoudPedram, Multi-dimensional
SLAbased Resource Allocation for Multi-tier Cloud
Computing Systems, in Proceedings of IEEE
International Conference on Cloud Computing
(CLOUD),Washington DC USA, 2011.
[3] Bo Yin, Ying Wang, LuomingMeng, XuesongQiu, A
Multi-dimensional Resource Allocation Algorithm in
Cloud Computing , Beijing University of Posts and
Telecommunications, Beijing 100876, China, 2012
[4] Linlin Wu, Saurabh Kumar Garg andRajkumarBuyya,
2011. “SLA-basedResource Allocation for Software as
aService Provider (SaaS) in CloudComputing
Environments”. 11thIEEE/ACM International
Symposium onCluster, Cloud and Grid Computing,
Pages195-204.
[5] Jiayin Li, MeikangQiu, Jian-Wei Niu, Yu Chen, Zhong
Ming, “Adaptive Resource Allocation for Pre-empt able
Jobs in Cloud Systems,” in 10th International
Conference on Intelligent System Design and
Application, Jan. 2011, pp. 31-36.
[6] AmitNathani, Sanjay Chaudharya, GauravSomani,
“Policy based resource allocation inIaaS cloud”, Future
Generation Computer Systems 28 (2012) 94–103 doi:
10.1016/ j.future. / 2011.05.016
[7] Introduction to Cloud Computing, White paper,
Dialogic, Making Innovation Thrive.
[8] GlaucoEstácioGonçalves, PatríciaTakako Endo,
ThiagoDamasceno, Resource allocation in clouds:
Concepts, Tools and Research Challenges.
BIOGRAPHIES
Vaghela Ankita (M.E), AlphaCollege of Engineering and
Technology, Khatraj, Kalol

A survey on various resource allocation policies in cloud computing environment

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