Time Efficient VM Allocation using KD-Tree Approach in Cloud Server Environment

International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169
Volume: 5 Issue: 7 01 – 05
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IJRITCC | July 2017, Available @ http://www.ijritcc.org
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Time Efficient VM Allocation using KD-Tree Approach in Cloud Server
Environment
Moiz Husain Bohra
Computer Science and Engineering
Samrat Ashok Technological
Institute
Vidisha (M.P.), India
moiz.mhb@gmail.com
Prof. Sandeep Raghuwanshi
Institute
sraghuwanshi@gmail.com
Dr. Yogendra Kumar Jain
Institute
ykjain_p@yahoo.co.in
Abstract— Cloud computing is an incipient and quickly evolving model, with new expenses and capabilities being proclaimed frequently. The
increases of user on cloud with the expansion of variety of services, with that the complete allocation of resource with the minimum latent time
for Virtual machine is necessary. To allocate this virtual cloud computing resources to the cloud user is a key technical issue because user
demand is dynamic in nature that required dynamic allocation of resource too. To improve the allocation there must be a correct balanced
algorithmic scheduling for Resource Allocation Technique. The aim of this work is to allocate resource to scientific experiment request
coming from multiple users, wherever customized Virtual machines (VM) are aloft in applicable host out there in cloud. Therefore, properly
programmed scheduling cloud is extremely vital and it’s significant to develop efficient scheduling methods for appropriately allocation of
VMs into physical resource. The planned formulas minimize the time interval quality so as of O (Log n) by adopting KD-Tree.
Keywords— Cloud Computing; Virtualization; Scheduling; VM Allocation
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I. INTRODUCTION
Computing is a field of study that help in implementing
the standard scientific issues in disciplines like Molecular
science, Earth science, Bioinformatics and even a lot of.
Computing is related to substantial large-scale computer
simulation and modeling and infrequently needs computer
resources to satisfy the experiment.
Cloud computing [1] is an ideal for executing the
computing issues with due consideration of its
commitment of provisioning wide resources. So many
professional explained it in their ways, Cloud computing is
a style of computing which rely on distribute resource
rather than its inherent local servers or own devices to
manage different applications. Clouds computing is a
utility delivery computing instead of product due to the
fact in cloud computing the resource are placed at distinct
location and their respective resources are access via
network as a service on the idea of pay as you go [2]. By
virtualization technology resources provided by cloud can
be used dynamically and effectively. There are three flavor
of cloud computing: platform, infrastructure and software
as services. Framework of cloud computing consist of
deployment model, delivery model, characteristic and
resource shown in figure 1.
The services in cloud are made accessible to clients on a
pay-as-you-utilize model. The use of various cloud
computing techniques finding its roots in IT environments.
Presently, there are many commercial Clouds that provide
platform-level Services or applications, computing or
storage resources.Additionally, By using open-source
Cloud Computing ,its accesible to create private Clouds
(i.e., intra-data-center).
Fig 1: Frame Work of Cloud Computing

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This work is concentrated on the Infrastructure as a
Service (IaaS) model, wherever physical resources are
manifest as utility. In this type of model, users demand
virtual machines (VM), which are allied to physical
resources.
However, so as to attain the most effective and efficient
performances, the physical resources are totally utilized by
VMs by complying with the dynamically Cloud
atmosphere. To adopt this, scheduling the process is a vital
concern and it's fundamental to have dynamic scheduling
method’s to suitably assign the VMs in physical resources.
In cloud scheduling refer means allocation of VMs. The
responsibility of allocation of VMs is of scheduler.
Scheduling techniques are of two types in cloud: dynamic
and static. Static VM scheduling relies according to earlier
information of each entity, nevertheless dynamic
scheduling relies on the instant mode of the model. User
demand is potent in nature, that the dynamic scheduling is
much better than the static VM scheduling however it's ton
of overhead. Here we have proposed dynamic resource
appropriations technique which reduces the resource time
of client request. The experimental result shows that the
proposing techniques gives minimum time in VM
allocation process.
The results of paper refer to approach as follows:
 Reduce the response time: The call of client for
assigning the appropriate host with minimum latency.
 VM placement: It grants the VM provision policy
by providing the VM to PM.
The additional of this paper is deprived as follows. Section
II describes the necessary background to understand the
concept. Then section III describes the related work.
II. LITERATURE REVIEW
Cloud Computing has become a computing ideal which
has been newly induced all over. Cloud computing
provides platform, application and computing resources
which are made access anywhere. Moreover, Services
provided by cloud resources are highly scalable and
dynamic and provide end-users array of services. Cloud
utilities are vast ample to cover any topic required for the
research.
1.1 Cloud Computing Basics
As the developing quality of Cloud Computing necessitate
too many analogues, as mentioned by Vaquero [3]. Some
of the definitions proposed by scientists which include:
 RajkumarBuyya [4], define it as: Cloud is a
consolidation of distributed and parallel computing, which
is consisting of a set of virtualized and interconnected
computers that are dynamically provisioned and released
 R. Cohen[5] Define it as: Cloud computing is one
of those catch buzz words that tries to encompass a many
of aspects ranging from load balancing, deployment,
business model, provisioning and architecture (like
Web2.0). It’s the next logical step in software (software
10.0). For me the simplest explanation for Cloud
Computing is describing it as, internet centric software...
Cloud model is consisting of three types of service i.e.
Platform as a Service (PaaS), Infrastructure as a Service
(IaaS) and Software as a Service (SaaS) as shown in figure
2. The four types of deployment models in cloud are
public, protected, community and hybrid [6]. Whereas the
main qualities are: wide network approach, on-demand
self-service, rapid flexibility,merging of resource and
regular services.
Cloud Users
(web browser, Mobile app. thin client...)
SaaS
(CRM, Email, Virtual desktop,
Communication........)
PaaS
(Execution Time, DB, Web server,
Development environment....)
IaaS
( Virtual machine, servers, storage
network)
Fig. 2. Cloud computing service model
Cloud Computing is the abstraction of virtualization, i.e.,
the potential of a system of imitating n number of OS. In a
Cloud, for providing resource flexibility to each user and
stability and security from one another, virtualization is an
important mechanism in cloud environment. The best use
of an algorithm and data structure for the allocation of
VMs from data center can lead the system efficient. For
achieving high quality performance and to boost the
resource utility proper allotment of resources is most vital
job in cloud environment. On an IaaS in job scheduling, at
one level the requests are mapped to physical resources
(execution middleware) using the first come first serve
basis, resources are scheduled at second level
infrastructure or Cloud wide and VM-level.

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Fig 3. High level view of Cloud Computing
The best use of algorithm and data structure for the
allocation of VMs from data center can lead the system
efficient. VM scheduler creates as much as cloud
infrastructure is required and the VMs are mapped into
suitable substantial hardware. For convenient task
scheduling suitable data structure is applied at the VM
level. Each task are mapped into virtual resources for
convenient execution. Task scheduling connect jobs to a
convenient resources to execute, and the delivered
efficiency make performance of cloud environment better.
1.2 KD-Tree:
A k-d tree which is a non-linear data structure is also
familiarly as K-Dimensional Tree [7]. k-d tree is a binary
tree with further suppression based on it. k-d tree is a
binary tree with other suppression based on it. k-d tree as a
binary tree that keeps k-dimensional data in each node. k-
d tree is effective data structure for various operation, like
searches involving a dimensional search key (e.g. nearest
neighbour searches and range searches). K-Nearest
neighbour (KNN) function is to fetch the closet adjoin
using the base value of k, which results in how many
Nearest Neighbour (NN) are there about to details the
category of a sample datum.
The KNN combines and ties the one point to its closest
neighbour. The user request many input like a point in
multidimensional space and a range point in the space.
The k-d Tree partition the data at each level of tree same as
done by Binary Tree [8]. The k-d tree has been inherited
from binary tree where each node represents a k
dimensional point. Each leaf node can be deduced of to
completely generate a cleaved hyper plane that partition
the space in to two branch, called as half-spaces. Point left
to the hyper-plane shows the left sub tree of that node and
point’s right of the hyper-plane are represented by the
right sub tree. The direction of hyper plane is selected in
the resulting manner: every node in the tree is linked with
one of the k dimensions, with the hyper-plane
perpendicular to that dimension's axis.
III. RELATED WORK
Resource allocation is one of the most significant problem
in the resource management issues of cloud environment.
The dynamically allotment of resources in cloud
environment has gain the consideration of many
researches.Therefore various resource allocation
techniques has been anticipated in the leaflet for
optimizing the time of allocation. W. Tian et al. [9]
proposed algorithm called DAIRS for dynamic resource
scheduling for cloud datacenters. Abirami S.P. and Shalini
Ramanathan [10] proposed an algorithm resource
allocation based on Linear scheduling strategy (LSTR). R.
Buyya et al [11] discusses balanced allocation of resource
algorithm based on First Fit Decreasing technique (FFD).
IV. PROPOSED WORK
The proposed work for the VM placement will effectively
solved the issue with minimization of latent time cloud
data center as shown in figure 4.

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As the client request arrived for the resource requirement,
creation of new VM take place on PM. There are various
selection strategy are which can be elected as PM to host
the new created VM. Proposed work foucses on
minimizing the reponse time in order O(Log n). As the
request arrives for the placement of VM, the scheduler
fetch the VM from the pool and placed it on the proper
host. The host in the picked from the list and searched
according to proposed algorithm. When the request arrives
for VM placement at cloud data center, scheduler
determines the best list and using k-d-tree nearest neighbor
to find the best in resource capacity than the VM required
capacity in entire dimension as shown in Algorithm 2.
V. EXPERIMENTAL RESULT
The proposed work is done by using Cloudsim tool kit
[12] [13]. Cloudsim is a frame for Modeling and
Simulation of Cloud Computing infrastructures and
utilities. All the result are done using a core i5 window
with 3.60 GHz CPU. We have compare our work with
LSTR algorithm, where we found our proposed work
gives better result then it.
Fig: 4 Resource Allocation Time Graph
Algorithm 1: Host Creation or
Datacenter Creation Process
1. N Number of host
Resources types=3
(Mem, BW and CPU)
2. While 0 to n-1
3. Create a host[i]
4. Initialize it
5. Adds the Host[i] into the Host-
Array using FCFS
6. Now using KD-Tree insertion
operation insert the host in tree
7. And so on
8. End if
9. End for
Algorithm 2: Searching of Node for VM
Allocation Process
1. Create the VM.
2. find_nearest()
3. if(!Root_host)
4. Turn on the new host.
5. end if
6. checked_host= zero;
7. Parent_host = Finds the parent of a
target host;
8. Nearest_host = parent_host ;
9. Check_subtree(Root, x);
10. return nearest_host
11. end procedure
Table 1: Resource Allocation Table
Virtual
Machine
Allocation Time
(Proposed work)
(MS)
Allocation
Time
(LSTR)
(Micro second)
{5,5,5} 2824.934 367.62
{10,5,2} 87.544 224.594
{10,2,5} 57.96 359.839
{5,10,2} 79.09 251.541
{2,10,5} 91.166 509.278
{5,2,10} 60.978 451.318
{2,5,10} 64.601 286.558
{5,5,5} 53.734 314.108
{20,10,4} 79.694 432.79
{20,4,10} 74.261 362.99
{10,20,4} 89.959 48.3
{4,20,10} 74.261 213.123
{10,4,20} 80.902 444.943
{4,10,20} 51.219 335.018

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VI. CONCLUSION AND FUTURE WORK
The Job scheduling has received attention in the research
community. The job must efficiently processed in any
computing environment such as Cloud computing. It
involves large number of computing with independent
jobs. Cloud scheduling is NP-complete problem [14].
Therefore many experiments have done on it. Our work
describes the efficient scheduling technique which
provides proper resource allocation with minimum latent
period. The work gives better result than other traditional
technique. Proposed work is done using Cloudsim toolkit.
Our work is concentrate on the IaaS, where VMs are
carried out as host within the data center, energy
consumption is an issue.
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