a technical review on cloudsim based vm scheduling...
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A Technical Review on Cloudsim based VM Scheduling Techniques in
Cloud Computing Environment
Nagadevi.S
SRM University
Chennai, India
Dr.S.V.Kasmir Raja
Dean, Research
SRM University
Chennai, India
Abstract— Virtualization is a key concept of
cloud computing. Data centers virtualizes their
physical resources to improve resource
utilization, revenue maximization ,power
consumption and so on. Virtual machines are
created as separate entity to run users
applications. To make effective use of these
virtual machines an optimized virtual machine
placement decision has to be made. Placing VMs
on suitable PMs is known a virtual machine
placement. This paper analyses various
algorithms and techniques used for virtual
machine placement.
Index Terms—VM provisioning , allocation,
auction-based
I. Introduction
Nowadays, organizations move their business to
their own Datacenters. Datacenters provide the
environment for the user’s applications to run.
Efficient utilization of Datacenter resources is the
key issue for every organization. With the help of
virtualization concept, organizations virtualize their
physical recourses to service many request to run an
application. The Virtual Machines (VM) are created
and assigned to them by the Cloud Service
Provider. The key challenges faced by the
organizations, in maintaining the datacenters are:
how to reduce the power consumed by unused (idle)
resources, how to dynamically schedule the VMs
among the users application to improve
multidimensional resource usage.
II. Background
When a user submits the job to the Datacenter , the
job scheduler will configure appropriate resources
i.e. VMs. i.e. scheduling jobs onto virtual machines
is done in PAAS. Job scheduling includes accepting
or rejecting the request based on the available VMs,
and also optimizing the mapping of jobs to VMs.
Once the requested VMs are configured and
mapped to jobs, these VMs are actually created in a
physical server called Physical Machine .So,
scheduling VMs onto physical machines is known
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 5, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
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as VM scheduling. VM scheduling is done in IAAS.
Allocating virtual machines to physical machines is
one of the key tasks of Infrastructure as a service
(IAAS) cloud. VM scheduling Algorithms are used
to schedule the VM requests to the physical
machines of a data center (DC)
III. VM Placement
VM placement technique is used whenever a user
request for a resource (VM) in a datacenter.
Allocating the requested VM on a PM is a VM
placement technique. VMs should be placed on
PMs such that any VM placement should not
overload any PM and VM placement must reduce
the number of active PMs. The main objective of
the VM placement is to reduce energy consumed by
the datacenter. Energy consumption is reduced by
minimizing the number of active physical machines.
By reducing the number of VM migrations, the
energy, cost and effort of a datacenter could be
greatly reduced.VM placement technique is
required at two instances of a datacenter. One is
initially when the datacenter is unloaded .Another
one is when there is a need to reallocate the VMs in
a dynamic cloud environment.
The VMP problem is selecting a suitable PM to host
a new VM so that the performance of the system is
higher i.e. packing VMs onto a fixed capacity PMs
e.g. Multi Dimensional Bin packing problem.
VM placement decision is required at two places.
One is Initial- Static VM placement which places
VMs at once in an unloaded DC. Second is
Dynamic VM placement which place Vms at run
time to cope up with the dynamic environment.
VM placement decisions are made based on
Reservation, On-demand(Initial VM
placement,Migration of VM),Spot Market.
IV. VMP Problem Definition:
Given a set of n virtual machines and a set of m
physical machines mapping n VMs to m PMs is
called VM placement. Dynamic VM placement
assumes that m PMs are already allocated with n1
number of virtual machines. Now at a point in time
dynamic VM placement involves mapping n+n1
number of VMs to m PMs.
The main objectives of the VMPP are:
1. To reduce energy consumption by reducing
number of running physical machines
2. To do dynamic resource allocation
3. To improve resource utilization
4. To minimize a cost of a data center
5. To improve SLA
6. To reduce number of VM migrations
V. Related Work:
In [1] ,VM Placement decisions were made
based on the following: Reservation, On-
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 5, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
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Demand and Spot Market. On-Demand VM
placement decisions are based on initial VM
Placement and migration of VMs. Both of the
above steps can be considered for power
conservation, SLA, revenue maximization and
reliability. In this paper only initial VM
placement is studied. Initial VM placement done
at two levels: 1. Cluster / Cloud in which VMs
communicate across cloud. 2. Node/PM in
which VMs communicate only within a single
PM. In VM placement algorithms, physical
machines are partitioned into two sets : those
that meet some criterion(candidates) and those
that do not. Within the candidate PMs , PMs
were ordered based on some heuristics like
online bin packing and then VMs are placed on
PMs until all the PMs are exhausted.
(a) User submits the request to cloud
controller. Request carries number of VMs
of each type (small, medium, Large)
(b) Cloud controller responds with
a. Full grant: allocated the
maximum request
b. Partial grant: allocated instances
less than maximum request
c. NERA :Not Enough resources
Available
(c) On which cluster should requested VMs are
placed based on
a. LLF Least full First
b. PAL Percent Allocated
c. RAN Random
(d) On which node should requested VMs be
placed based on Six Heuristics:
a. FF First Fit
b. LF Least Full First
c. MF Most Full First
d. NF Next fit
e. RA Random
f. TP Tag and Pack
(e) VMs are placed on a selected node on a
selected cluster. If not, the next node in the
list is selected . Node controller reallocates
the VM to the next node in the set. Else
NERA is returned to cloud controller.
In this paper [2]reallocation of VMs performed to
minimize the number of physical nodes. Idle nodes
are switched-off to decrease the power
consumption. VM provisioning and VM placement
were done using Bin packing using MBFD. VMs
are selected for migration based on MBFD.The
objective of this paper is to consolidate VMs
leveraging live migration and switch off idle
nodes to minimize power consumption while
providing QoS .BFD algorithm is used for VM
allocation. The following heuristics are used for
selection of VMs for migration , Single Threshold,
Minimum number of Migrations, Highest Potential
Growth and Random Choice.In this paper they have
considered only single core nodes.
The objective of this paper [3] is to map multi-core
VMs to multi-core PMs using constraint
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 5, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
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programming. Scheduling of multiple cores in VM
placement leads to optimized results for overall
performance and cost.VM placement is combined
with core scheduling to achieve optimal results.
Here only the CPU dimension is considered for
placement. This method reduces number of active
PMs, overloaded PMs and number of migrations. It
outperforms 25-60% performance over traditional
non-multi-core placement.
This paper[4] initial VM placement is done in an
unloaded datacenter to reduce resource
wastage(CPU,Memory,BW) , power consumption
and to minimize SLA violation(MIPS).ACO (Ant
Colony Optimization) is used over GA and
Heuristic method.Genetic Algorithm doesn’t use
feedback information, searches with blindness
,large search space, do a lot of redundant iteration,
convergence speed and efficiency of optimum
solution is slow and low.Heuristic Method:Single
point search, fall into local optimum search, can’t
get global optimal solution, may not produce
any solution .ACO : Positive feedback
mechanism, pheromone is constantly updated,
optimal solution by efficient convergence .Findings
:Reduces resource wastage, power
consumption and SLA
violation
Objective[5] : To increase resource
utilization, to meet SLA requirement and to
reduce no. of PMs Method : Pearson
correlation Coefficient --Correlation between a pair
of VMs is considered Result : CPU
utilization increased, SLA violation
decreases(capacity requirement), number of active
servers reduced Issues : only
CPU demand is considered for VM multiplexing.
Multi-dimensional resources like(CPU,
Mem,Storage etc..) are not considered.
[6]mappingVMs to PMs is called VMP.VMP is
part of VM Migration.Goal is to minimize Energy
by shutting down services.4 Steps in VM
Migration:
a) Select PM which is
overloaded/underloaded
b) Select one or more VM
c) Select PM where selected VM can be
placed
d) Migrate VM to PM
The main goal of VM placement decision is
• Power Conservation
• SLA
• Revenue maximization
• Reliability
[7] designs an online VMP algorithm to increase
cloud providers revenue for the multi dimensional
resources. To develop an energy saving technique
using VM consolidation by migrating VMs to a few
active servers.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 5, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
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[8] This paper enhances the reliability of a cloud
service by increasing the fault tolerance level.
Replication based k-fault tolerance metric is used as
a backup when the primary PM fails. The prpsed
technique consist of three steps.One is host server
selection,second is optimal redundant VM
placement, and third one is recovery strategy
decision. the proposed approach consumes less
network resources.
[9] The objective of Decrease and Conquer
Genetic Algorithm (DCGA) is to find a new
optimal VM placement plan such that the total
energy consumption by all the servers, the total
number of VM migrations, and the computation
time can be minimized. DCGA outperforms FFD in
terms of energy consumption. DCGa minimizes
computation time and number of VM migrations
compared to classical GA.
VI. Classification of vm placement algorithms
VM placement algorithms are broadly classified in
to Power Based and Application QoS Based as
shown in figure 1.
Power based approaches aims at reducing the power
consumption in computing and cooling resources of
a data center. The vm to pm mapping is performed
in such a way to minimize the power usage by
shutting down unused pms.
Application based approaches aims at maximizing
the quality of service of a datacenter. These
approaches maximizes the resource utilization and
also minimizes the cost .
Classification of VM placement algorithms are
Constraint programming, Bin packing problem,
stochastic integer programming, Genetic algorithm,
Adaptive algorithms.
Heuristics to select PM
• First Fit
• Least Full First
• Most Full First
• Next Fit
• Random
• Tag & Pack
• Single Dimensional Best Fit
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 14, Number 5, 2019 (Special Issue) © Research India Publications. http://www.ripublication.com
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• Dot Product Based Fit
Heuristics to choose Vm
• Single Threshold
• Minimization of Migrations
• Highest Potential Growth
• Random Choice
The environment scenario for the VMP is
• Static/ dynamic
• Multiple resource types/ single type
• Fixed price/auction based
• improve csp revenue/ improve cu revenue
Algorithm
• Generate tasks
• Create VM list
• Submit task and VM to broker
• Create data center
• Create Host machines
• For all the available VMs
– Choose VM to assign to a first
available Host
– Assign VM to Host
• Send tasks to VMs
VII. REFERENCES
[1] K.Mills,J.Filliben and
C.Dabrowski,”Comparing VM-Placement
Algorithms for On-Demand Clouds”, Third
IEEE International Conference on Cloud
Computing Technology and Science, 2011
[2] Anton Beloglazov and Rajkumar Buyya,”
Energy Efficient allocation of Virtual machines
in Cloud Data Centers”, 10th IEEE/ACM
International Conference on Cluster, Cloud and
Grid Computing, 2010.
[3] Zoltan Adam Mann, Multicore-aware virtual
machine placement in cloud data centers , IEEE
Transactions on Computers,2015
[4] Fei MA,Feng LIU,Zhen LIU, Multi-objective
Optimization for Initial Virtual Machine
Placement in cloud Data Center, Journal of
Information & Computational science 9: 16
(2012) 5029-5038
[5] Zar Lwin Phyo and Thander Thein, Correlation
Based VMs Placement Resource Provision,
International Journal of Computer science &
Information Technology(IJCSIT) vol 5, no 1 ,
February 2013
[6] Rajeev Kumar Gupta and R.K.Pateriya, “
Survey on Virtual machine Placement
Techniques in Cloud Computing Environment”,
International journal on Cloud Computing:
Services and Architecture(IJCCSA), Vol 4,
No.4,August 2014
[7] Laiping Zhao,Liangfu,Zhou Jin, and Ce Yu,”
Online Virtual machine placement for
Increasing Cloud Providers Revenue “ IEEE
Transactions Services Computing,2015
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[8] Ao Zhou, Shangguang Wang, , Bo Cheng, Zibin
Zheng, r, IEEE, Fangchun Yang, , Rong N.
Chang, Michael R. Lyu, and Rajkumar Buyya,
Cloud Service Reliability Enhancement via
Virtual Machine Placement Optimization, IEEE
TRANSACTIONS ON SERVICE
COMPUTING, VOL. XX, NO. XX, X XXXX
[9] Chanipa Sonklin, Maolin Tang, Yu-Chu Tian, A
decrease-and conquer genetic algorithm for
energy efficient virtual machine placement in
data centers,IEEE 15th International Conference
on Industrial Informatics(INDIN),2017
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