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125
Optimized Scheduling and Resource Allocation Using Evolutionary Algorithms
in Cloud Environment
Anusha Bamini Antony Muthu1*
Sharmini Enoch1
1
Noorul Islam University, India
* Corresponding author’s Email: anushabamini@gmail.com
Abstract: Cloud computing is a powerful computing technology, which render a flexible services at anywhere to the
user. One of the major issue of cloud computing was scheduling. In this work, a bacterial foraging optimization
algorithm with genetic algorithm (GABFO) was combined to find out trustworthy scheduling problems in cloud
workflow. Generally job scheduling and resource allocation in cloud is a tedious optimization problem at the time of
considering QoS requirements. Lot of existing works under scheduling only concentrates on cost optimization and
deadline problems, and it ignores the importance of reliability, availability and robustness. The main subscription of
my work is to state a new optimized approach to schedule the jobs efficiently and allocate the resources in a efficient
manner by introducing GABFO algorithm. Experiments were done in PSO, Genetic, BFO and then Genetic and BFO
was combined to generate a hybrid optimized result, and the work was compared with above mentioned algorithms.
The algorithms were executed for 52 iterations and totally 10 runs are calculated. The size of the job as well as
virtual machines was varied for each iteration to calculate performance variation. We considered the optimization
parameter as time and cost, and throughput. The work is implemented in cloudsim to create a simulated cloud
environment. Final result shows better performance and maximum resource utilization in GABFO when compared to
PSO, GA, BFO.
Keywords: Workflow, Scheduling, Resource allocation, Bacterial foraging optimization.
1. Introduction
In recent years distributed environments are
playing a major role for computing. In that way
cloud computing was used as a technology to use
the resources based on pay and use model. Cloud
services are classified in the terms of: Infrastructure
as a Service (IaaS), Platform as a Service (PaaS),
and Software as a Service (SaaS) [1, 2]. In this work
we target on Infrastructure as a Service to utilize the
pool of virtual resources. These resources are
accessed on the basis of on demand. Resources
considered in cloud are RAM, network speed,
bandwidth. We can use the resources in a flexible
manner. But the maximum utilization of resources
provides better performance. The new scheduling
techniques must be utilizing the resource efficiently.
To overcome lot of the problem in scheduling [3,
4, 5, 6, 7] a new workflow model was introduced in
this paper. However the simple scheduling and
resource allocation is not important, but the
optimization of resource allocation [8, 9, 10] is
important. Hence we developed a new scheduling
approach which schedule the given job into number
of tasks and allocate each task to resources in an
optimized manner. In our work the first stage is to
split the job into number of tasks, and in the second
stage specific allocation of tasks into resources was
done. The resource allocated is considered as virtual
machines. Each job is allocated virtually to needed
resources. For better allocation we combined the
concept of genetic (GA) [11, 12] and bacterial
foraging optimization (BFO). The fitness value is
calculated for each iteration and the best fitted value
was choose by considering minimized time and cost.
The problem is to assign a task to resources and
regulate the tasks on resources to optimize the entire
cost and utilization time. The main objective is task
International Journal of Intelligent Engineering and Systems, Vol.10, No.5, 2017
DOI: 10.22266/ijies2017.1031.14
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scheduling and resource allocation must be carefully
analyzed and jointly optimized to achieve reduced
time and cost, and finally producing the QoS
parameter of better reliability.
This work is based on the swarm intelligence
techniques of bacterial foraging optimization (BFO)
[13] and evolutionary computing concept of genetic
algorithm (GA) [14-16]. BFO was introduced by the
person Passino and it is inspired by the social
foraging behavior of Escherichia coli. BFO has
inspired the concentration of researchers for the
reason that of its suitability and performance in
recovering real-world optimization problems
occurred in numerous application domains. The
concept of E.coli is used to solve simple
optimization problems. Where the chemotactic step
size is adjusted for each run according to the current
fitness of a bacterium. GA was inspired by Darwin’s
theory about Evolution. GA was commonly used for
natural selection and generating high quality
solutions in optimization. The objective of GA is to
solve optimization problem.
In this paper, we develop a cost and time
minimization scheduling technique which supports
in the cloud. From the services of cloud computing
we are using the feature of Infrastructure as a
Service (Iaas). In IaaS we consider the features of a
computing resources and virtual machine
performance. To get an optimized result in task
scheduling and resource allocation both concepts are
merged. BFO and GA were combined to solve
problems in scheduling and resource mapping. By
using these algorithms the optimized results were
achieved with good resource utilization.
The rest of the paper structured as follows.
Section 2 provides a brief literature survey about
scheduling, resource allocation concepts and various
algorithms. Section 3 represents the problem
definition, which includes the input, output,
constraint and objective. Section 4 and 5 represents
the standard bacterial foraging optimization
algorithm and standard genetic algorithm. The
proposed GABFO algorithm was explained in
section 6. Simulation and analytical results were
analyzed and plotted in section 7. Finally section 8
represents the conclusion of the entire work.
2. Related work
Scheduling under distributed systems has been
studied well in the previous decades. Various
algorithms are implemented in scheduling to meet
the QoS constraints of users. The general aim of
scheduling is to reduce the execution time of jobs.
More number of scheduling algorithms is proposed
126
for distributed computing [17-20]. Most of the
algorithms [20-22] are applied for cloud scheduling
based on its suitability. Goal of the scheduling
algorithms are achieving better performance. The
concept of scheduling the bag-of-task (BoT)
application was proposed [23] in agent based
scheduling concept. In this paper 14 scheduling
concepts are executed concurrently. Based on the
size of the task time is allocated for sharing the
resources. The proposed elastic resource allocation
technique will dynamically allocate and reallocate
the resources. The result shows that the BoT was
allocated and reallocated efficiently. The precedence
constrained scheduling of parallel applications on
heterogeneous computing systems (HCSs) was
proposed in [5]. This proposes a parallel bi –
objective hybrid genetic algorithm to reduce the
energy consumption and increase makespan. The
energy consumption was minimized by using a
method of dynamic voltage scaling (DVS). Results
prove that it dominates previous algorithm in terms
of completion time, makespan and energy
consumption.
A scheduling algorithm based on berger model
[24, 25] was designed to establish the dual fairness
constraint in virtualized cloud. The fairness of
resource allocation was judged by the application of
justice function. The results showed that the user
tasks and the fairness were efficiently executed. A
Biogeography-Based Optimization (BBO) was
proposed to sole binary integer problem in job
scheduling through better solution adaptation
strategy [26]. In BBO, the GA and ACO strategies
were incorporated to generate a new set of solutions,
at each iteration; the Mann-Whitney test was
conducted to evaluate performance output of BBO
algorithm. Results proved that BBO performance
was better than the GA and PSO algorithms. An
Improved Genetic Algorithm (IGA) was proposed
for job scheduling by speeding up the process of GA
[27]. The proposed model has five components such
as preprocessing unit, job schedulers, users, and data
center and data center manager. The preprocessing
unit encoded the attributes into users’ job attribute
vector, which included expected instruction count,
job deadline and delay cost.
An optimal task scheduling and resource
allocation was proposed using Particle Swarm
Optimization (PSO) based fitness function [28-29,
31]. To balance the load PSO based fitness function
was applied to reduce the make span and to
maximize the processing capacity. The results
showed that the PSO based method resulted in less
execution time and cost. A Position Balance Parallel
Particle Swarm Optimization (PBPPSW) [31]
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127
method was introduced with high profit resource
allocation and flexible user satisfaction level was
maintained. The performance metrics of average
response time, total profit and number of virtual
machines were considered for evolution. The results
showed that the PB-PPSO method achieved
increased profit and small response time with a less
number of virtual machines.
A hybrid scheduling algorithm [32-34] was
proposed by combining genetic algorithm and fuzzy
theory to assign the task to virtual machines.
Genetic algorithm was modified to balance the load
and to reduce the execution time and cost. An agent
based best fit resource allocation scheme was
proposed to increase the resource utilization [35].
The results showed that the best fit approach was
better in terms of job execution time, cost, virtual
machine allocation and resource utilization. A
continuous resource allocation strategy was
presented to optimize the scheduling process in
cloud [36]. The suggested resource allocation
mechanism adopted minimal domination matching
to compensate the trade-off space.
3. Problem definition
Assume the cloud customer has different jobs and
each job is splitter into number task and each task
has divided into sub tasks. These tasks are allocated
to resources (memory, network, CPU) as virtual
machines. The concept of scheduling and resource
allocation has different aim. We need to find a
schedule to execute a DAG workflow on
Infrastructure as a Service computing resource to
minimize the execution cost and time.
Input: The schedule is defined in the format of
S=(R, A, TC, TT) in the form of set of resources (R),
task to resource allocation (A), total cost (TC) and
total time (TT). For each resource R= (r1, r2... rn)
different virtual machines are allocated, and each
resource has its start time STri and end time ETri.
Here A represents allocation and the allocation
consist of set of tuples in the form of at art= (ti, rj,
STri, ETri). A task ti is scheduled to run on the
resourcerj. The total cost TC and total time TT are
calculated as follows:
( ETri STri )
i 1
TT max ETri : t i T
TC
R
C
VM
(1)
(2)
Output: Assignment of tasks to resources in
minimized time and cost. The n number of tasks and
n number of resources are allocated as {(t1, r1), (t2,
r2)... (tn, rn)}.
Constraints: Each task must be completed within
short time and without interruption. One virtual
machine can complete one task at a time. The
processing time is depends on the virtual machine
allocated.
Objective: The aim is to assign each task to
matching virtual machine resources and sequence
the tasks to minimize the time, cost and throughput.
The challenge of job scheduling and resource
allocation was optimized by combing GABFO with
minimized time and cost, finally reliability is
achieved.
4. The
standard
optimization
bacterial
foraging
BFO algorithm was used to solve optimization
problem [18]. Bacterial Foraging Optimization is an
evolutionary method based on E.coli bacteria. The
area having high level nutrients are searched by
bacteria. This task is used for optimization process.
By
sending
signals
individual
bacterium
communicates with others. During foraging
locomotion is achieved by a set of tensile flagella.
E.coli bacteria tumble or swim using flagella. These
are the two basic operation of bacteria performed at
the time of foraging.
After considering two
previous factors foraging decision is taken by
bacteria. The process, in which a bacterium moves
by taking small steps while searching for nutrients,
is called chemotaxis. The basic idea of BFOA is
mimicking chemotactic movement of virtual
bacteria in the problem search space.
Foraging theory is based on the assumption that
animals search for and obtain nutrients in a way that
maximizes their energy intake E per unit time T
spent foraging. Hence, they try to maximize a
function like E/T. Maximization of such a function
provides nutrient sources to survive and additional
time for other important activities (e.g., fighting,
fleeing, mating, reproducing, sleeping, or shelter
building). Herbivores generally find food easily but
must eat a lot of it. Carnivores generally find it
difficult to locate food but do not have to eat as
much since their food is of high energy value. The
“environment” establishes the pattern of nutrients
that are available and it places constraints on
obtaining that food (e.g., small portions of food may
be separated by large distances). During foraging
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there can be risks due to predators, the prey may be
mobile so it must be chased and the physiological
characteristics of the forager constrain its
capabilities and ultimate success. Bacterial Foraging
Algorithm is explained by following steps.
Chemotaxis
Swarming
Reproduction and
Eliminational-Dispersal
The chemotactic step was described by the equation
i
i
i i
(3)
represents an attractant repellant profile and 𝜑
represents the position of the bacteria. The nutrients
for the bacteria is located as, 𝐹 < 0, 𝐹 = 0, 𝐹 > 0
specifies the presence and absence of nutrients. The
natural area for the bacteria is represented as,
𝐻(𝑗, 𝑘, 𝑙) = {𝜑 𝑥 (𝑗, 𝑘, 𝑙)|𝑥 = 1,2, … , 𝑁}
Eq. (5) shows the parameters of N bacteria at jth
chemotactic step, kth reproduction step and lth
elimination dispersal. Then 𝐶(𝑥, 𝑗, 𝑘, 𝑙) represent
the cost of bacteria at ith position.
𝜑 𝑥 (𝑖, 𝑗, 𝑘)𝜖𝑅𝑛
n
Where Δ(i)n is a n-dimensional randomly generated
vector with elements within the following interval:
[-1,1]. After that, each bacterium θi(j,k,l) modifies
its position as mentioned in Eq. (4), where C(i) is
the stepsize for search direction φ(i). Eq. (4)
represents the swim of a bacterium
i j 1, k , l i j, k , l C i i
(4)
5. The standard genetic algorithm
GA is an evolutionary algorithm introduced
by Holland. GA mimics the process of natural
evolution. It generates the solution to optimization
problem using inheritance, crossover, selection and
mutation.
Produce initial population
Evaluate fitness function
Produce new population (using mutation
and crossover)
The search in the genetic algorithm starts with
an initial population. Each individual is evaluated by
its fitness function. According to fitness value
unfitted populations are eliminated. Individuals are
manipulated using genetic operators. Totally three
operators are used in genetic algorithm. At first the
production operator is used to create copies of best
fitted population. The low fitness values populations
are eliminated. The second one is the crossover
operator. This makes swapping of individual
elements. Third one is the mutation operator.
Application of this operator is used for random
search.
6. The proposed hybrid GABFO algorithm
The objective of hybrid GABFO algorithm was
get the minimum function 𝐹(𝜑) , 𝜑𝜖𝑅, where 𝜑
represents the position of the bacteria. 𝐹(𝜑)
(5)
(6)
𝜑 𝑥 = (𝑖 + 1, 𝑗, 𝑘) = 𝜑 𝑥 (𝑖, 𝑗, 𝑘) + 𝐶(𝑥)𝜑(𝑖)
(7)
The value of 𝐶(𝑖) > 0 is the step size for each
tumble. Here Ns is lifetime length of the bacteria
calculated using chemotactic steps.
Algorithm: Hybrid GABFO Algorithm
Step 1: Initialize the input parameters N, Nc, Nre, Ns,
C(i)
N: No. of bacteria
Nc: Chemotactic step
Nre: Reproduction step
Ns: No. of steps
C(i): Size of the step taken
Step 2: Calculate the elimination dispersal step
using j=j+1
Step 3: Calculate reproduction step using k=k+1
Step 4: Calculate chemotaxis loop using l=l+1
For i=1, 2,…,N calculate fitness
function FT (i, j, k, l)
Let FT= FT (i, j, k, l). Save this
value to find better cost.
Tumble: generate a random vector
on [-1, 1]
Move: When
i j 1, k , l i j, k , l C i
i
i i
n
Compute FT(i, j+1, k, l)
Perform swim
Else, Go to next bacterium (i,1)
Step 5: If j<Nc, go to step 3. Bacterium life is not
over.
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129
Start
Start
Initialize
Initialize the
the input
input parameters
parameters
S,
S, N
Nss,, N
Ncc,, N
Nrsrs,, N
Ned
Ped
C(i)
ed,, P
ed,, C(i)
Do
Do maximum
maximum number
number of
of iterations
iterations for
for
elimination,
elimination, reproduction,
reproduction, chemotaxis
chemotaxis
Output
Output
optimal
optimal
solution
solution
Stopping
Stopping
condition
condition
satisfied
satisfied
Bacteria
Bacteria is
is divided
divided into
into two
two sets
sets based
based on
on
hybrid
hybrid probability
probability PP
Evaluate
Evaluate bacteria
bacteria object
object function
function
value
value
Evaluate
Evaluate bacteria
bacteria object
object function
function
value
value
Perform
Perform swim
swim
Select
Select bacteria
bacteria based
based on
on fitness
fitness
Perform
Perform reproduction
reproduction
Perform
Perform crossover
crossover
Perform
Perform elimination
elimination dispersal
dispersal
Perform
Perform mutate
mutate
Have
Have all
all
bacteria
bacteria
been
been
updated?
updated?
Have
Have all
all
bacteria
bacteria
been
been
updated?
updated?
Find
Find the
the optimal
optimal best
best solution
solution
Figure.1 Flowchart for GABFO
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130
For simulation we used CloudSim [3] as a tool
for testing and analyzing new algorithms for
creation and allocation of virtual machines to
Cloudlets for execution. This CloudSim is used to
create a simulation environment for cloud. In the
proposed work we considered two physical
machines (PM) and we created 4 virtual machines
(VM1, VM2, VM3, VM4) from 2PM for testing.
Here cloudlets are the jobs or tasks in the simulation
environment; each cloudlet is assigned to individual
virtual machine. We are submitting 10 types of jobs
as workflow model to the physical machine. The
given jobs are divided into number of 0-50 tasks.
Totally 52 iterations are calculated for each run.
And 10 times the workflow is executed under
different loads to get different execution results.
Following table 1 shows the simulation parameters
considered under our setup. The output parameters
we considered over five algorithms are execution
time, cost and throughput. Our result gives the
reduced time and cost as well as increased
throughput for various workloads. Compared to
PSO, GA and BFO the hybrid GABFO produced
better result. The work mentioned in [24] compared
with our result.
Fig.2 shows the result of total execution time of
jobs. Increases in number of instances directly affect
the execution time adversely. But, the proper virtual
machine placement and sharing the jobs during the
demand situation reduces the time consumption
effectively. The proper VM selection and the prior
load consumption are the major requirements for
minimum execution time. The total cost is defined
as the cost of resource and the amount of total time
period the resource used. The results of cost versus
various jobs are shown in Fig.3. More amounts of
jobs completed in a minimal duration are called as
throughput. Higher throughput gives the better result.
If the throughput increases resource utilization also
increases. Throughput result is shown in Fig.4.
Resource utilization is defined in Fig.5 shows that,
the number of allotted resources divided by the total
number of available resources. When the number of
jobs varied resource utilization also varied. From the
Number of PM
Number of VMs
Number of job
Number of task
Number of iteration
Number of executions
2
4
1-10
0-50
52
10
12
10
PSO
8
GA
6
4
BFO
2
0
10
20
30
40
50
60
70
80
90
100
7. Simulation and analytical results
Table 1. Simulation parameters
Resource Parameter
Quantity
time (ms)
Step 7: Elimination dispersal: Eliminate and
disperse bacterium with probability Ped.
If l<Ned then go to step 2,
Otherwise end.
analysis the execution time was reduced upto
23.67% and the cost was reduced upto 13.05%.
GA
BFO
Number of jobs
Figure. 2 Total time
100
PSO
80
Cost ($)
𝐹𝑇 =
∑𝑁𝑐+1
𝑗=1 𝐹𝑇(𝑖, 𝑗, 𝑘, 𝑙)
GA
60
40
BFO
20
0
1 2 3 4 5 6 7 8 9 10
GAB
FO
Number of jobs
Figure. 3 Cost
50
Throuhhput (%)
Step 6: Calculate Reproduction step
PSO
40
30
GA
20
BFO
10
0
1 2 3 4 5 6 7 8 9 10
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GA
BFO
Number of jobs
Figure. 4 Throughput
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131
Number of jobs
8. Conclusion and future work
J9
GAB
FO
BFO
J7
J5
GA
J3
PSO
J1
0
50
100
Resource utilization (%)
Resource Utilization (%)
Figure. 5 Resource utilization
120
100
CPU
80
RAM
60
40
Band
width
20
0
5 10 15 20 25 30 35 40 45 50
Time (s)
Figure. 6 Virtual machine 1
Resource utilization (%)
100
CPU
80
60
RAM
40
20
Band
width
0
5 10 15 20 25 30 35 40 45 50
Time (s)
Figure. 7 Virtual machine 2
The maximum utilization of resources is
important in cloud. Because better resource
utilization gives better performance. Our test results
produce maximum utilization of resources compared
to previous works. The above Figs. 6 and 7 show the
sample results of resource utilization under virtual
machine 1, virtual machine 2. These two virtual
machines are placed under single physical machine.
CPU, RAM and bandwidth are the resources we
considered for allocation and utilization. Above
figure shows that approximately the resources are
utilized over 91.53%.
In this work, we addressed the “job scheduling
and resource allocation problem”, which is to
schedule and allocate virtual resources to achieve
high resource utilization to meet user’s needs with
minimum utilization parameters. The proposed work
provided the solution to the scheduling and resource
allocation problems using optimized hybrid
algorithms. Variations in genetic algorithm have
been investigated and implemented for learning and
improve the speed of convergence. This work
proposed a novel hybrid approach consisting of
Genetic Algorithm (GA) and Bacterial Foraging
Optimization Algorithm (BFOA) also the
performance of scheduling and resource allocation
was tested with varying execution steps. These
proposed experimental problems were tested with
CloudSim tool. Compared to PSO, GA, BFO
algorithms the hybrid GABFO demonstrated the
better scheduling results within better cost saving
scheme. Our simulation result shows that the
resources are highly utilized with optimized
performance result. We can apply this algorithm to
set a real cloud environment.
One of the extension of this work plans to
execute the defined various workflow models with
this same algorithm in simulated environment. It
would be interesting to investigate the performance
of scheduling workflows or allocating resources to
support various workflow models. Second plan is to
extend the work by automatic resource allocation
system using self learning algorithm. Which means
each problem and their solutions are learned by the
automatic decision system. If the same job appears
again without any execution the resources are
automatically allocated.
References
[1] P. Mell and T. Grance, “The NIST Definition
of Cloud Computing”, National Institute of
Standards and Technology Special Publication,
SP 800-145, p.7, 2011.
[2] D. Rani and R.K. Ranjan, “A Comparative
Study of SaaS, PaaS, and IaaS in Cloud
Computing”,
International
Journal
of
Advanced Research in Computer Science and
Software Engineering, Vol.4, No.6, pp.158-161,
2014.
[3] F. Zhang, J. Cao, K. Li, S.U. Khan, and K.
Hwang, “Multi-objective scheduling of many
tasks in cloud platforms”, Elsevier-Future
Generation Computer Systems, Vol. 37, pp.
309–320, 2014.
International Journal of Intelligent Engineering and Systems, Vol.10, No.5, 2017
DOI: 10.22266/ijies2017.1031.14
Received: June 20, 2017
[4] J. Yu and R. Buyya, “Workflow scheduling
algorithms for grid computing, Metaheuristics
for Scheduling in Distributed Computing
Environments”, Springer, Germany, Vol. 146,
pp. 173, 2008.
[5] M. Mezmaz, N. Melab, Y. Kessaci, Y.C. Lee,
E.G. Talbi, A.Y. Zomaya, and D. Tuyttens, “A
parallel bi-objective hybrid metaheuristic for
energy-aware scheduling for cloud computing
systems”, Journal of Parallel and Distributed
Computing, Elsevier, vol. 71, No. 11, pp. 14971508, 2011.
[6] G. Malathy, R. Somasundaram, and G. Vidhya,
“A survey on workflow scheduling algorithms
and map reduce method in cloud environment”,
International Journal of Communications and
Engineering, Vol. 6, No. 6, pp. 1-6, 2012.
[7] J. Gasior and F. Seredynski, “A decentralized
multi-agent approach to job scheduling in cloud
environment”, Intelligent Systems, Springer,
Vol. 322, pp. 403-414, 2015.
[8] N. Anitha and A. Basu, “A dynamic resource
allocation based on multi attributes scoring in
collaborative cloud computing”, Global
Journal of Computer Science and Technology,
Vol. 15, No. 4, pp. 1-7, 2015.
[9] Z. Xaio, W. Song, and W. Chen, “Dynamic
resource allocation using virtual machines for
cloud
computing
environment”,
IEEE
Transactions on Parallel and Distributed
Systems, Vol.24, pp.1107-1117, 2013.
[10] A. Bamini and S. Enoch, “Role of Cloud
Computing in the Provision of Healthcare”,
World Journal of Medical Education and
Research, Vol.2, No. 1, pp. 12-17, 2012.
[11] M. Abdullah and M. Othman, “An improved
genetic algorithm for job scheduling in cloud
computing environment”, Procedia Information
Technology and Computer Science, Vol.2, pp.
291-296, 2013.
[12] J. Liu, X.G. Luo, X.M. Zhang, F. Zhang, and
B.N. Li, “Job scheduling model for cloud
computing based on multi-objective genetic
algorithm”, International Journal of Computer
Science Issues, Vol. 10, No. 1, pp. 134-139,
2013.
[13] A. Bamini and S. Enoch, “Optimized Resource
Scheduling
Using
Classification
and
Regression Tree and Modified Bacterial
Foraging
Optimization
Algorithm”,
International Journal of Applied Engineering
Research, vol. 10, No. 16, pp. 37170-37175,
2015.
[14] S. Kaur and A. Verma, “An efficient approach
to genetic algorithm for task scheduling in
132
cloud computing environment”, International
Journal of Information Technology and
Computer Science, Vol. 10, pp. 74-79, 2012.
[15] Y. Ge and G. Wei, “GA-Based task scheduler
for cloud computing systems”, IEEE
International Conference on Web Information
Systems and Mining, pp. 181-186, 2010.
[16] M. Shojafar, S. Javanmardi, S. Abolfazli, and N.
Cordeschi, “FUGE: A joint meta-heuristic
approach to cloud job scheduling algorithm
using fuzzy theory and a genetic method”,
Cluster Computing, Vol. 18, No. 2, pp. 829-844,
2015.
[17] F. Pop, R.I. Tutueanu, C. Barbieru, M.A. Vasile,
and J. Kolodziej, “Adaptive resource allocation
in cloud computing based on agreement
protocols”, Intelligent Agents in Data-intensive
Computing, Springer, Vol. 14, pp. 193-213,
2016.
[18] S. Zhan and H. Huo, “Improved PSO-based
task scheduling algorithm in cloud computing”,
Journal of Information and Computational
Science, Vol. 9, No. 13, pp. 3821-3829, 2012.
[19] A. Shokripour, M. Othman, H. Ibrahim, and S.
Subramanian, “New method for scheduling
heterogeneous multi-installment systems”,
Future Generation Computer Systems, Vol. 28,
pp. 1205-1216, 2012.
[20] A.K. Bardsiri and S.M. Hashemi, “A review of
workflow scheduling in cloud computing
environment”, International Journal of
Computer Science and Management Research,
Vol. 1, No.3, pp. 1-4, 2012.
[21] W. Wang, “Bayesian cognitive model in
scheduling algorithms for data intensive
computing”, Journal of Grid Computing,
Vol.10, pp. 173-184, 2012.
[22] G. Malathy, R. Somasundaram, and G. Vidhya,
“A survey on workflow scheduling algorithms
and map reduce method in cloud environment”,
International Journal of Communications and
Engineering, Vol. 6, No. 6, pp. 1-6. 2012.
[23] J.O. Gutierrez-Garcia and K.M. Sim, “A family
of heuristics for agent-based elastic cloud bagof-tasks concurrent scheduling”, Future
Generation Computer Systems, Elsevier, vol.
29, No. 7, pp. 1-18, 2012.
[24] N. Kumar and S. Saxena, “A preference-based
resource allocation in cloud computing
systems”, Proceedia Computer Science, Vol.
57, pp. 104-111, 2015.
[25] B. Xu, C. Zhao, B. Hu, and E. Hu, “Job
scheduling algorithm based on berger model in
cloud environment”, Advances in Engineering
Software, Vol. 42, No. 7, pp. 419-425, 2011.
International Journal of Intelligent Engineering and Systems, Vol.10, No.5, 2017
DOI: 10.22266/ijies2017.1031.14
Received: June 20, 2017
133
[26] S.S. Kim, J.H. Byeon, H. Yu, and H. Liu,
“Biogeography based optimization for optimal
job scheduling in cloud computing”, Applied
Mathematics and Computation, Vol. 247, pp.
291-296, 2014.
[27] S. Yeo and H.H.S. Lee, “Using mathematical
modeling in provisioning a heterogeneous
cloud computing environment”, Computer, Vol.
44, No. 8, pp. 55-62, 2011.
[28] R. Mohana, “A position balanced parallel
particle swarm optimization method for
resource allocation in cloud”, Indian Journal of
Science and Technology, Vol. 8, No. 3, pp.
182-188, 2015.
[29] Z. Yang, X. Qin, W. Li, and Y. Yang,
“Optimized task scheduling and resource
allocation in cloud computing using PSO based
fitness function”, Information Technology
Journal, Vol. 12, No. 23, pp. 7090-7095, 2013.
[30] Z. Yang, X. Qin, W. Li, and Y. Yang,
“Optimized task scheduling and resource
allocation in cloud computing using PSO based
fitness function”, Information Technology
Journal, Vol. 12, No. 23, pp. 7090-7095, 2013.
[31] R. Mohana, “A position balanced parallel
particle swarm optimization method for
resource allocation in cloud”, Indian Journal of
Science and Technology, Vol. 8, No. 3, pp.
182-188, 2015.
[32] K. Chandran, V. Shanmugasundaram, and K.
Subramani, “Designing a fuzzy logic based
trust and reputation model for secure resource
allocation in cloud computing”, International
Arab Journal of Information Technology, Vol.
13, No. 1, pp. 30-37, 2016.
[33] S. Javanmardi, M. Shojafar, D. Amendola, N.
Cordeschi, H. Liu, and A. Abraham, “Hybrid
job scheduling heuristics for cloud computing
environment”, Advances in Intelligent Systems
and Computing, Springer, Vol. 303, pp. 43-52,
2014.
[34] A. Bamini and S. Enoch, “Dynamic Scheduling
and Resource Allocation in Cloud”,
International Journal of Control Theory and
Applications, Vol. 10, No. 3, pp. 63-72, 2017.
[35] G.K. Shyam and S.S. Manvi, “Resource
allocation in cloud computing using agents”, In:
Proc. of the IEEE International Advance
Computing Conference, pp. 458-463, 2015.
[36] Z. Zhigang, Z. Hongli, Y. Xiangzhan, and G.
Junwu, “Continuous resource allocation in
cloud computing”, In: Proc. of the IEEE
International Conference on Communications,
pp. 319-324, 2015.
International Journal of Intelligent Engineering and Systems, Vol.10, No.5, 2017
DOI: 10.22266/ijies2017.1031.14