A Review: Metaheuristic Technique in Cloud Computing

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 750
A Review: Metaheuristic Technique in Cloud Computing
Partibha Rani 1, Akhilesh K. Bhardwaj 2
1M.Tech. Research Scholar Shri Krishan Institute of Engineering and Technology, Kurukshetra, India
2Assistant Professor Shri Krishan Institute of Engineering and Technology, Kurukshetra, India
--------------------------------------------------------------------------------***--------------------------------------------------------------------------------
Abstract - In the space of a few years, Cloud computing
has experienced remarkable growth. Indeed, its economic
model based on demand of hardware and software
according to technical criteria (CPU utilization, memory,
bandwidth or package has strongly contributed to the
liberalization of Computing resources in the world. In this
paper we have survey various type of meta-heuristic based
technique which treated with the task scheduling, load
balancing, ACO, PSO, GSA in cloud computing.
Key Words: Cloud computing, Meta-heuristic
technique, ACO, GA, PSO, GSA, Load balancing, Cloud
Scheduling
1. INTRODUCTION
1.1 What is a cloud computing?
Cloud computing can be defined as "a type of parallel and
distributed system consisting of a collection of
interconnected and virtualized computers that are
dynamically provisioned and presented as one or more
unified computing resources based on service-level
agreements established through negotiation between the
service providers and consumers". Cloud computing is a
model that enables on demand access to a shared pool of
configurable computing resources. Cloud computing is an
evolving technology. Cloud computing delivers
infrastructure, platform, and software that are made
available as subscription-based services in a pay-as-you-go
model to consumers. These services are referred to as
Infrastructure as a Service (IaaS), Platform as a Service
(PaaS), and Software as a Service (SaaS) in industries.
Cloud computing is Internet- based computing. Although
many formal definitions have been proposed, NIST
provides a somewhat more objective and specific definition
here "Cloud computing is a model for enabling convenient,
on- demand network access to a shared pool of
configurable computing resources (e.g., networks, servers,
storage, applications, and services) that scan be rapidly
provisioned and released with minimal management effort
or service provider interaction."Cloud computing is a new
kind of model, is coming. The use of Internet and new
technologies nowadays, for business and for the current
users, is already part of everyday life. Any information is
available anywhere in the world at any time and every day
its being used more this services that are called cloud
computer services.
1.2 Scheduling in cloud computing-
The Scheduler is responsible for organizing the execution
of work units composing the applications, dispatching
them to different nodes, getting back the results, and
providing them to the end user. The Executor is
responsible for actually executing one or more work units,
while the Manager is the client component which interacts
with the Aneka system to start an application and collect
the results. The assumptions made for algorithm has to be
designed are as follows.
1.There is a master node which is the only node
responsible for users to submit jobs into the cloud service.
2. The master node-approved jobs are the only ones
running on any node in cloud infrastructure. Master node
evaluates the best node which can execute the application
on the basis of the CPU availability of slave machines.
1.3 Type of clouds
Private cloud: A cloud that is used exclusively by one
organization. The cloud may be operated by the
organization itself or a third party. The St Andrews Cloud
Computing Co-laboratory8 and Concur Technologies [13]
are example organizations that have private clouds.
Public cloud: A cloud that can be used (for a fee) by the
general public. Public clouds require significant investment
and are usually owned by large corporations such as
Microsoft, Google or Amazon.
Community cloud: A cloud that is shared by several
organizations and is usually setup for their specific
requirements. The Open Cirrus cloud test bed could be
regarded as a community cloud that aims to support
research in cloud computing.
Hybrid cloud: A cloud that is setup using a mixture of the
above three deployment models but applications and data
would be allowed to move across the hybrid cloud.. Each
cloud in a hybrid cloud could be independently managed.
Hybrid clouds allow cloud bursting to take place, which is
where a private cloud can burst-out to a public cloud when
it requires more resources.
1.4 Meta-Heuristic Techniques
These techniques also make use of random solution space
for scheduling the tasks but the main difference between

heuristic and meta-heuristic is that heuristic methods are
problem specific while meta-heuristic methods are
problem independent. They generally use population based
concepts inspired by social behavior of insects such as Ant
Colony Optimization (ACO), Particle Swarm Optimization
(PSO), Tabu Search algorithm and Honey bee foraging
algorithm etc.
Load Balancer: This mechanism contains algorithms for
mapping virtual machines onto physical machines in a
cloud computing environment, for identifying the idle
virtual machines and for migrating virtual machines to
other physical nodes. Whenever a user submits an
application workload into cloud system, one can create a
new virtual machine. Now the mapping algorithm of Load
balancer will generate a virtual machine placement
scheme, assign necessary resources to it and deploy the
virtual machine on to the identified physical resource. Load
Balancer will have the following three sub modules.
Manager: It triggers live migration of VMs on to physical
servers depending on information provided by the VM
Mapper. It turns a server ‘on’ or ‘off’.
Monitoring Service: This module collects parameters like
status of application, workload, utilization of resources,
power consumption etc. This service works like global
information provider that provides monitoring data to
support intelligent actions taken by VM mapper. The status
information is utilized to arrest the sprawl of unmanaged
and forgotten virtual machines.
VM Mapper: This algorithm optimally maps the incoming
workloads (VMs) on to the available physical machines. It
collects the information from monitoring Service time to
time and makes decision on the placement of virtual
machines. The VM mapper searches the optimal placement
by a genetic algorithm.
2. LITERATURE SURVEY
A. Jain et al. [1] have discussed the evolution of
computing from mainframe to cloud computing. Authors
have discussed the basic characteristics, type and
architecture of cloud computing. Moreover, authors have
also discussed the different research issues and
applications of cloud computing.
Zhang Huan-Qing et al.[2] proposed a task scheduling
algorithm based on Load Balancing Ant Colony
Optimization in Cloud Computing. This algorithm keeps
load balance by adjusting pheromone factors and
improving updating rules for pheromone factors. Others
aim at obtaining load equality to the highest level while
taking into account the built-in attribute and load of each
resource joint.
T. Liao et al [3] proposed a new approach for Ant colony
optimization. ACO is a probabilistic technique for solving
computational problems. They used ACO in cloud and grid
computing task scheduling, etc, but it doesn’t get a good
performance since there are still some problems in
pheromone update and the parameters selection.
However, PACO also has some problems, such as the
selection of the parameters and the way getting
pheromone. In order to let Ant colony optimization get a
better performance, a self adaptive ant colony
optimization has be proposed in this paper which
improves PACO.
C. Y Liu [4] represented a task scheduling based strategy
on genetic ant colony algorithm in cloud computing was
proposed, which used the global searchability of GA to find
the optimal solution, and converted to the initial
pheromone of ACO. Task scheduling problem in cloud
computing environment is NP-hard problem, which is
difficult to obtain exact optimal solution and is suitable for
using intelligent optimization algorithms to approximate
the optimal solution. Meanwhile, quality of service (QoS) is
an important indicator to measure the performance of
task scheduling.
X. F. Liu et al [5] proposed that Cloud computing
resources scheduling is essential for executing workflows
in the cloud platform because it relates to both the
execution time and execution costs. For scheduling T tasks
on R resources, an ant in ACS represents a solution with T
dimensions, in solving the problem of optimizing the
execution costs while meeting deadline constraints, they
developed an efficient approach based on ant colony
system (ACS). Compare the results with those of particle
swarm optimization (PSO) and dynamic objective genetic
algorithm (DOGA) approaches.
Jinhua Hu et al [6] gave explanation about the current
virtual machine(VM) resources scheduling in cloud
computing environment mainly considers the current
state of the system but seldom considers system variation
and historical data, which always leads to load imbalance
of the system. In view of the load balancing problem in VM
resources scheduling, this paper presents a scheduling
strategy on load balancing of VM resources based on
genetic algorithm.
Bhathiya Wickrema Singhe et al [7] presented advances
in Cloud computing opens up many new possibilities for
Internet applications developers. However, there is a lack
of tools that enable developers to evaluate requirements
of large-scale Cloud applications in terms of geographic
distribution of both computing servers and user
workloads.
Kun Li et al [8] clarified about cloud computing is
experiencing a rapid development both in academia and
industry; it is promoted by the business rather than

academic which determines its focus on user applications.
This technology aims to offer distributed, virtualized, and
elastic resources as utilities to end users. It has the
potential to support full realization of ‘computing as a
utility’ in the near future.
A. Jain et al [9] have proposed a new load balancing
approach for cloud computing. Proposed approach has
used the concept of biased random walk. Biasing has been
achieved through task size, and available capacity of
virtual machine. Proposed approach has not only
improved the load balancing but also improved the
reliability of the system.
B. Shaw et al [10] an efficient approach has been
proposed in this paper, they explained GSA (intelligence
based heuristic optimization method) is implemented for
economic operation of a two area power system. The
advantage of the method is that it is applicable to both
linear, nonlinear as in the case of PSO. The results
obtained by GSA are compared with the conventional
method and PSO.
U. K. Rout et al [11] A novel Economic Operation of Two
Area Power System through Gravitational Search
Algorithm has been proposed, which is based on the AGC.
This method is explained with an example and the result
obtained by the proposed method is compared with by
particle swarm optimization as reported in literature. It
has been shown that this method is more efficient and
takes less computation time than PSO. The economic
operation of two area (Grid) power system by GSA is
presented.
P. K. Roy [12] represented a Meta heuristic method for
optimization known as GSA In this paper GSA is
implemented to economic operation of a two area power
system and computes how much power has to be
generated internally in an area and how much power has
to be borrowed from other area through tie-line for a
specified load in most economical sense.
G -B. Wang et al [13] proposed there are many
researches on VM scheduling strategy. One well-known
strategy is centralized VM live migration scheduling
scheme. The scheduler mainly consists of two
components: central controller and local migration
controller. The central controller obtains all the physical
resources utilization on the whole, and then initializes the
VM migration based on pre-specified policies so as to
achieve load balancing and high resource utilization.
R. N. Calheiros et al [14] In this approach, authors
proposed a novel distributed VM migration strategy to
solve the above problems. In our strategy, distributed local
migration agents autonomously monitor there source
utilization of each PM. If the agents finds that the PM is in
high load condition. The proposed ACO-Based VM
Migration Strategy (ACO-VMM) uses artificial ants to find
an ear-optimal mapping of PMs and VMs.
A. Jain et al [15] presented a hybrid load balancing
approach for cloud environment by combining the best
feature of join idle queue, join shortest queue and
minimum completion time approach. Moreover, authors
have added the prior overloading checking mechanism.
Authors have tested the proposed approach on cloud
analyst simulator and it has been found that proposed
hybrid approach JIMC has outperformed all the basic
approach on all the relevant parameter.
A. Beloglazov et al [16] reviewed the performance
metrics that evaluate the simulation result, such as energy
consumption, number of SLA violations. The utilization of
RAM is set as 50%.The proposed technique is very
effective not only for accumulate pheromone in the
iteration times but also prevent the quick
convergence resulted from the over-accumulated
pheromone. Beloglazov and Buyya proposed a useful
metric called SLAV (SLA Violations) to evaluate the SLA
delivered by a VM in an IaaS cloud.
M. A. Tawfeek et al [17] proposed a cloud task scheduling
policy based on ant colony optimization algorithm, of
which the main goal was minimizing the make span of a
given tasks set. A task scheduling based on genetic ant
colony algorithm in cloud computing was proposed, which
used the global search ability of GA to find the optimal
solution, and converted to the initial pheromone of ACO.
They proposed a task scheduling strategy with QoS
constraints based on GA and ACO.
S. C. Wang et al [18] in this paper, the authors stressed,
the importance 01' choosing the appropriate node in the
Cloud for the execution of a task. The authors have also
introduced the use 01' agents in collecting available
information such CPU capacity and remaining memory.
Finally, they proposed a scheduling algorithm gathering
the qualities of two algorithms the opportunistic load
balancing (OLB) and the load balance Min-Min.
Wen et al [19] proposed that Ant Colony algorithm can
also be combined with other algorithms such as particle
Swarm Optimization to improve its performance. It not
only enhances the convergence speed and improves
resource utilization ratio, but also stays away from falling
into local optimum solution.
ZHUO Tao et al [20] proposed an improved Ant Colony
Optimization (IACO) to improve the cloud computing
utilization. The proposed IACO algorithm improves
pheromone factors and inspired factors innovatively
based on the existent algorithms. Emulation tests are
conducted in the CloudSim and the results indicate that
IACO is superior to the conventional ACO and the latest
IABC in task executing efficiency.

Y. Gao et al [21] the authors of the research study
introduced a multi objective ant colony system algorithm
in order to ensure an efficient placement of virtual
machine (VM). Through this paper, the authors aim to
implement solutions which reduce both the total resource
wastage and power consumption. Proposed algorithm to
the existing multi-objective genetic algorithm and two
single-objective algorithms, highlight the fact that the
proposed algorithm is more efficient than the previous
algorithms.
N. M. Saberi et al [22] A GSA algorithm has been
proposed to find the solution more efficient than PSO. As
the computation time is low in GSA, it can be implemented
on-line. It has been pointed out that the convergence of
GSA is quite slow towards end (near the optimum
value).In this paper, the GSA (intelligence based heuristic
optimization method) is implemented for economic
operation of a two area power system.
Shahdan Sudin et al [23] A new velocity update strategy
is proposed in this paper , in which new velocity depends
on the previous velocity and the acceleration, based on the
fitness of the solutions. The proposed strategy is named as
fitness based gravitational search algorithm (FBGSA). In
FBGSA, the high fit solutions are motivated to exploit the
promising search regions, while the low fit solutions have
to explore the search space.
Pacini et al [24] addressed the problem of balancing
throughput and response time when multiple users are
running their scientific experiments on online private
cloud. The solution aims to effectively schedule virtual
machines on hosts. Cloud computing has become a
buzzword in the area of high performance distributed
computing as it provides on demand access to shared
resources over the Internet in a self service, dynamically
scalable and metered manner
M. Berwal et al [25] proposed load balancing algorithms
in Cloud can be classified as immediate mode scheduling
and batch mode scheduling. The immediate mode
scheduling organizes tasks based on its arrival by applying
a minimum execution time and minimum completion time
algorithms. One major aspect for dealing with
performance issues in Cloud computing is the load
balancing.
3. CONCLUSIONS
The paper reviewed the importance of cloud computing in
almost every field. It makes provisioning, scaling,
maintenance of your apps and serves a breeze. In the
studied literature, most of the authors have focused on
task scheduling, ant colony optimization, genetic
algorithm, PSO and GSA. Task scheduling in cloud
computing means based on the current information of task
and resource and in accordance with a certain strategy to
build a good mapping relationship between tasks and
resources. Comparisons show the Ant colony optimization
is better than other technique.
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