Simulation Based Workflow Scheduling for Scientific Application

Simulation Based Workflow Scheduling for
Scientific Applications
Priya Garg, Dr. Anju Bala
Computer and Science Engineering Department, Thapar University
Patiala 147004, India
gpiyag@gmail.com
anjubala@thapar.edu
Abstract—The cloud environment offers an appropriate location
for the implementation of huge range of scientific applications.
However, in the existing workflows the major dispute is to assign
the assets to the tasks in a well-organized way so, that it acquires
less finishing time and load on every virtual machines will be
impartial. To overcome this problem, GA_ MINMIN has been
proposed that combines the features of GA and MINMIN
scheduling algorithms. This algorithm is fundamentally a three-
layer structure where GA is connected on the main level and
hereditary calculation was performed for distributing belonging
in an advanced way. At second level, the execution request of the
assignments was resolved based on their size. This would be
finished with the assistance of MIN-MIN. At third level, all the
virtual machines have been running in parallel so that task
response time will get decreased with more advanced outcomes.
The proposed algorithm has been executed on the simulation
environment.
I. INTRODUCTION
Cloud computing is a process that aimed at distributing
information technology (IT) amenities in which possessions
are regained from the web through web-based gears and
tenders, as opposite to a straight linking to a server. The
requirements for registering and huge stockpiling assets were
quickly developing. In this manner, distributed computing
gets the consideration because of the superior figuring
administrations and offices that are given to the clients as
Software as a Service (SaaS), Infrastructure as a Service
(IaaS), and Platform as a Service (PaaS) [1]. Logical
researches are generally characterized as workflows, where
responsibilities were connected according to their data flow
and compute dependencies [2]. Different work processes have
diverse structures. As here we were chipping away at two
logical work processes such as Montage and CyberShake. The
assessment of the execution of work process streamlining
procedures in genuine frameworks is unpredictable and
tedious.
A. Motivation
Logical workflows were formerly measured for parallel
implementation of Cloud computing applications such as
Mosaic workflow for lunar physics and CyberShake workflow
for tremor risks. These workflows consist of large number of
tasks. So, we have proposed GA-Min-Min, to reduce their
execution and makespan by scheduling these tasks on virtual
machines in an optimized form. Researchers are working on
scheduling of workflows for the reduction of cost, load
balancing and minimizing the makespan by implementing
various scheduling approaches. GA is an effective approach
for assigning tasks to the various resources. As, Hereditary
calculations deal with the Chromosome, which is encoded
rendering of potential arrangements' parameters, rather the
parameters themselves. It seeks parallel from a populace of
focuses.
B. Our Contribution
 We have proposed a new hybrid approach that is
combination of GA and MINMIN algorithm. With this we
are trying to balance the load on different VM’s and
reducing the response time of each task.
 New feature that is introduced by this algorithm is that we
have run all the virtual machines in parallel. This
effectively reduced waiting time of each task and because
of this more than one task will get the virtual machine at a
time.
 Our algorithm also vanishes the chances of starvation. As
we have terminated our process only when all the tasks get
executed.
The remaining content of this article is organized in
different sections. In section II related work has been
demonstrates. The next unit is ‘‘Problem Formulation’’
which converses the problem statement. After that, Section
IV that is ‘‘Proposed Approach: Workflow Scheduling’’
describes proposed algorithm in detail. Now in Section V
‘‘Experimental Results: Proposed Algorithm’’ portrays the
experimental details and imitation outcomes. Lastly,
section VI contains ‘‘Conclusion and Future scope’’ which
is concluding this article.
II. RELATED WORK
Work process booking issues has been viewed as one of the
fundamental difficulties in cloud conditions. Different
scientists are chipping away at the booking of work processes
on their different compels like restructuring the due date, cost,
adjusting the heap on assets and so forth. Numerous
calculations were proposed for work process booking and
planning the undertakings inside the work processes. A
coevolution approach was utilized by Rajkumar buyya [2] to
International Journal of Computer Science and Information Security (IJCSIS),
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alter the hybrid approach that is CGA algorithm, which could
quicken the meeting and keep the haste. In addition to that,
R.Buyya has also projected a versatile penalty work for the
striated limitations and other hereditary algorithms. He has
utilized an adaptable to address the issue of untimely meeting
penalty function without any parameter tuning. In recent years,
huge number of scientists has been worked on the same issue.
Due to which, some more calculations like Hybrid GA-PSO
has planned by Ahmad M. Manasrah [1] to decrease the
makespan and the cost and also to adjust the heap of the needy
assignments over the heterogonous assets in distributed
computing conditions. As per his evaluation, this calculation
enhances the heap adjusting of the work process application
over the accessible assets. Additionally, Pooja Nagpal [4]
exhibits new anticipated calculation which is utilizing
advantages of both Enhanced Max-Min and Max-Min
calculations together. Test outcomes show that the new
proposed calculation speaks to upgraded asset usage with
better makespan. One of the examinations of Rajkumar buyya,
[3], suggests an asset provisioning and booking technique for
logical work processes on Infrastructure as a Service (IaaS)
mists. He exhibits a calculation in view of the meta-heuristic
enhancement system, molecule swarm advancement (PSO),
which plans to limit the general work process execution cost
while meeting due date requirements.
III. PROBLEM FORMULATION
In workflows we utilize DAG structure which contains a chain
of undertakings. In this pecking order level savvy tasks will be
performed. A workflow is representing as a graph, where
{T_1, T_2,….., T_n}are the tasks and presence of edges
demonstrates the dependency between two errands. As, shown
in figure 1, there is an edge exist amongst T_1 and T_3which
focuses towards T_3 assignments imply that T_1 is a parent
and will executed before T_3.
Key goal is to plan these assignments though consider that
each errand should actualize on scarcely single VM however
each virtual machine can perform more than one task. In
WorkflowSim, level wise errands are passed to and scheduled
that is, after the finishing of first level of undertakings, second
level of assignments will be passed for incitement that is, first
parents are planned then infant. In our trial we are evaluating
the strength of each assignment based on their makespan. We
presume that for each VM kind, the handling limit as far as
MIPS is accessible either from the bringer or can be assessed
[3]. This data is utilized as a part of our calculation to
ascertain the execution time of an endeavor on a given VM.
IV.PROPOSED APPROACH: WORKFLOW SCHEDULING
In our proposed approach, we have performed GA and
MINMIN calculations for booking of work processes. With
this we have enhance the execution time and makespan of the
undertakings allotted on various assets. Here, we have utilized
GA for distributing undertakings on various virtual machines
and for adjusting the heap on each VM. Next, we have
connected MINMIN on each virtual machine which lessened
the reaction time of undertakings and afterward, rather than
running virtual machines one by one, we have run at that point
in parallel, with this holding up time was decreased.
A. Scientific Applications: Workflow Scheduling
Recently, most of the logical applications with their diverse
work have process structures. These are represented as DAG
structures which are excessively intricate and required
enhancement before uploading to the cloud. Montage is a
Cloud able galactic and high-vitality material science
application that has been utilized to reproject the info pictures,
tenacity their experiences, and mosaic them into a solitary
picture. The span of a Montage work process relies on the
zone of the sky secured by the yield. It is a push to send a
convenient, process stern, custom picture mosaicking
administration for the space science group [6]. The structure
of montage workflow with 25 tasks is shown in Figure 2(a).
The CyberShake application is utilized by the Southern
Calfornia Earthquake Center to portray tremor perils in a
locale. These work processes are from the 2011 Production
runs which incorporate high recurrence codes. Basic structure
of CyberShake workflow has been discussed in Figure 2(b).
Figure 2(a): Standard Structure of Montage
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Figure 2(b): Standard Structure of CyberShake
B. General Flow of Proposed Approach:
For the above problem we have proposed a solution, a
combination of an evolutionary approach with MINMIN
algorithm is used. Genetic Algorithm is a meta-heuristic
algorithm, with which we are going to allocate tasks to
different virtual machines. After initializing the arbitrary set
of solution that is chromosomes, operator’s selection,
crossover, and mutation are performed on the introduced
populace till number of cycles finished.
ALGORITHM 1: GA_MINMIN Approach
Input: Workflow assets for different scientific applications
and set of resources
Output: scheduled task
For i = 0 to popsize
 randomize ();
 fitness ();
End For
While not reach n do
 selection ();
 selection ();
 crossover ( , )
 mutation ( )
 fitness ();
If < least fitted chromosome
Swap ( , least fitted chromosome)
End If
Repeat
Select fittest chromosome
While not reach vmSize do
Sort(tasks)
Repeat
Run all virtual machines in parallel
Figure 3: Flow chart of GA and MINMIN algorithm
C. Initialization:
An arbitrarily started populace is passed as a contribution of
GA. Populace contains number of chromosomes and every
chromosome comprises of number of genes. In work process
scheduling, chromosomes are the quantity of errands which
we must calendar and traits are the virtual machines.
Figure 2: Random population [1]
Figure 2 demonstrates that population has five chromosomes
and every chromosome comprises of eight errands and at first
which undertakings is assigned to which specific VM. Here,
we are taking five virtual machines for instance. Along these
lines, eight undertakings are booked on any of the VM at
introductory stage. From that point forward, facilitate GA
operators are performed on these chromosomes.
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D. Fitness Function
The fundamental point of this issue is to upgrade the
execution time. So, the size of each undertaking is
contemplated and the MIPS vale of the VM on which that
errands is doled out. The wellness estimation of every
chromosome is figured portrayed in equation1.
1
E. Selection
Selection is the initial step which goes for choosing people for
multiplication. With a specific end goal to pick the best and
the fittest people, choice is done based on wellness of every
individual taking part in determination. Determination is the
way toward picking two guardians from the populace for
intersection. The reason for choice is to underline fitter
chromosomes in the populace with the goal that the posterity's
henceforth created have higher wellness. For selection,
Roulette Wheel Selection (RWS) is used in this we have to
select two chromosomes with the help of RWM and perform
crossover between them.
F. Crossover
In hybrid activity, two chose chromosomes associate with
each other and created an offspring by exchanging their
qualities. Here, we are utilizing single point crossover on
which the hybrid will happen. An arbitrary number will be
produced and utilized as a separation point. As, in crossover
genes are exchanged, in workflows tasks exchange their VM’s
for producing the new individual. New offspring will contain
the first half of the one parent and second half of the next
parent.
G. Mutation
The yield of the crossover operator has taken as input in
mutation. In mutation, genes were swapped within the
chromosome, lead to the generation of new offspring.
Swapping of qualities implies undertakings changed their
virtual machine. For change we have utilized Swap
transformation technique for influencing the change to process
less composite.
H. Sorting using MINMIN Approach
Check the wellness of out coming about posterity with the
instated populace if the wellness is less than the minimum
fittest chromosome at that point supplants it with the new
offspring. Continue this till the end of iterations and get the
fittest population. Now select one best chromosome out of the
population and apply MIN-MIN algorithm on it. Now order in
which tasks are performed will take into consideration. On
each virtual machine sort, the tasks in ascending order of their
size. So that task with least size will get the machine first.
This will reduce the waiting time of each task.
I. VM Working in Proposed Approach
The key advance that assistance in making our execution
much viable was running every virtual machine in parallel.
This was done to decrease the response time of undertakings
on each VM. As portrayed in Figure 4, at first, a condition has
been connected on each VM, that if asset was IDLE, change
the status of that VM and make it BUSY and allocate the
errand was available in the line in arranged frame. This same
procedure will be rehashed till all errands got executed.
Figure 4: Running VM’s in Parallel
V. EXPERIMENTAL RESULTS: PROPOSED APPROACH
Through our test, we were attempting to limit the execution
time and reaction time of each assignment in a work process.
Essentially, we have dealt with two logical applications, to be
specific, Montage and CyberShake. The two applications have
different and much complex structures. For assessing logical
work processes, we have utilized WorkflowSim to execute
these workflows. Here, we have connected our proposed
calculation for the planning of youngster hubs by utilizing one
of work process scheduler strategy. Additionally, contrasted
our proposed calculation and the current methodologies like
GA, PSO and Hybrid of GA-PSO.
A. Environment Setup
For assessing our examination, particular parameters have
been thought about. As, appeared in Table 1, set of errands
between 25 to 1000 have been appointed on 16 virtual
machines having distinctive setups for breaking down the
proficiency of proposed calculation. The MIPS of each VM
differ from 500 to 1500. RAM of every asset has been taken
inside the scope of 512 to 2048 MB. Transmission capacity of
utilized virtual machines was settled that is 1000. In our
proposed algorithm GA algorithm was used for scheduling the
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tasks on different virtual machines. GA has been appraised
based on different parameters such as different methods used
while running different GA operators. As advanced in Table 2,
for an instance we have utilized 100 arrangement of populace
and connected 100 cycles on it. Two chromosomes were
chosen out of 100 populaces with the assistance of Roulette
Wheel choice technique. Single point crossover was
performed on the selected chromosomes. Then, swap mutation
was used to advance the fitness of the chromosome.
Table 1: Prompt Parameters
Factor Value
Tasks used 25 – 1000
Number of Virtual Machine
used
16
MIPS 500 – 1500
RAM 512 – 2048
Bandwidth 1000
Table 2: GA parameters
Parameter of GA Value
Size of Population 100
Number of Iterations 100
Selection method Roulette Wheel
Crossover Single point Crossover
Mutation Swap mutation
B. Proposed Approach on Scientific Applications
Case 1: GA_MINMIN Algorithm on Montage Workflow
On montage work process, execution time was assessed based
on specific situations. On the basis the number of tasks,
different structures of Montage workflow have been assessed
for estimating their execution time. In first situation, we have
passed work process structure with less number of
assignments that is with 25 undertakings. GA_MINMIN
algorithm completed 25 tasks in 82.09 seconds. what's more,
in next situations we have are expanding the span of structure
by expanding the quantity of assignments with the goal that
our proposed calculation will we assessed in a proficient way,
publicized in Table 3. Presently, our calculation was assessed
with 50 errands and gets executed in 115.97 seconds. Further,
100 and 1000 undertakings were likewise viewed as and get
executed in 228.98 and 1580.17 seconds.
Table 3: GA_MINMIN algorithm execution time for
Montage workflow
Workflows tasks Execution Time
Montage_25 82.09
Montage_50 115.97
Montage_100 228.98
Montage_1000 1580.17
A diagram (Figure 5) was plotted utilizing Table 3 which
obviously speaks to the pictorial perspective of number of
undertakings and their execution time on montage work
process.
Figure 5: Execution time for Montage workflow using
GA_MINMIN algorithm
Case 2: GA_MINMIN Algorithm on CyberShake Workflow
CyberShake is one of the complex and huge structure of
logical applications. We were endeavouring to limit the
execution time of errands with the assistance of our proposed
calculation. GA_MINMIN was connected on various
arrangements of assignments of CyberShake work process, as
depicted in Table 4. 30 assignments were executed in 275.22
seconds. At that point, we increment the unpredictability of
work process by expanding the quantity of undertakings.
GA_MINMIN takes 352.98 sec for executing 50 tasks of
CyberShake workflow. Moreover, 100 and 1000 errands are
additionally assessed and executed inside 583.89 and 2328.76
sec separately. Figure 6 gives the distinctive perspective of
execution time of various tasks on CyberShake workflow.
Table 4: GA_MINMIN algorithm execution time for
CyberShake workflow
Workflows tasks Execution Time
CyberShake_30 275.22
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Figure 6: Execution time for CyberShake workflow using
GA_MINMIN algorithm
C. Comparison between existing and proposed algorithm
Case 1: GA with GA_MINMIN
Presently, our proposed calculation has been contrasted and
existing calculation by taking montage work process in
thought. Table 5 demonstrates that GA_MINMIN calculation
is more productive then GA as the execution time of our
proposed calculation is significantly less than that of GA. In
GA algorithm, tasks assigned to each VM were executed in
random manner which might increase the waiting time of the
task with small duration. But GA_MINMIN algorithm tasks
were first sorted in ascending order of their size and the
executed. This minimize the waiting time of tasks.
For comparing GA with the proposed calculation the
execution time of GA is taken from the existing analysis [1].
Table 5: Execution time of GA and GA_MINMIN algorithm
Algorithm Number of tasks Execution
time
GA_MINMIN
GA
Montage_25 82.09
197.65
GA_MINMIN
GA
Montage_50 115.97
250.89
GA_MINMIN
GA
Montage_100 228.98
345.72
GA_MINMIN
GA
Montage_1000 1580.17
2402.28
Utilizing Table 5 a diagram has been plotted which represent
the distinction of execution time between two calculations at
various number of assignments. In figure 7 x-hub speaks to
the execution time and y-hub speaks to the quantity of
undertakings.
Figure 7: Comparison of GA and GA_MINMIN algorithm
based on their execution time.
Case 2: Compare PSO and GA-PSO with GA_MINMIN
GA_MINMIN count was in like manner differentiated and the
other transformative estimations like PSO and Hybrid of GA-
PSO in view of their execution time. The right examining is
depicted in Table 6. The proposed approach gave preferred
outcomes over PSO and crossover of GA-PSO calculation
proposed in existing analyses [1]. As in GA_MINMIN
calculation, usage has been done such that every single virtual
machine was running in parallel which diminish the reaction
time of the current undertakings. Distinctive situations that
diverse number of errands has been utilized for demonstrating
our announcement.
Table 6: Execution time of GA_MINMIN, GA-PSO and PSO
algorithm
Algorithm Number of tasks Execution time
GA_MINMIN
GA-PSO
PSO
Montage_25 82.09
95.09
101.21
GA_MINMIN
GA-PSO
PSO
Montage_50 115.97
116.01
155.31
GA_MINMIN
GA-PSO
PSO
Montage_100 228.98
233.78
253.44
GA_MINMIN
GA-PSO
PSO
Montage_1000 1580.17
1585.06
1802.31
As appeared in Table 6 there is less contrast amongst
GA_MINMIN and GA-PSO calculation yet at the same time
the execution time of GA_MINMIN was less than GA-PSO.
In figure 8 we have plotted their comparison.
Figure 8: Comparison of GA_MINMIN, GA-PSO and PSO
algorithm based on their execution time.
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VI.CONCLUSION AND FUTURE WORK:
In this paper, a GA-MINMIN calculation was projected and
executed utilizing the WorkflowSim test system for work
process assignment planning and for cloud situations. The
execution of the proposed calculation was additionally
contrasted when referred to these calculations, for example,
GA, PSO and GA-PSO. With this we can infer that by
actualizing GA and MINMIN calculation in path, it has
decreased the response time or execution time of errands on
various virtual machines.
In future, we can change the execution stream by moving the
undertakings on various VM's at run time. As though any VM
is occupied in running a specific errand and another VM is
idle without moving around then that task will be moved on
the VM which is idle. So the holding up time of the task will
be decreased. This will be done at running time. Secondly,
inspite of setting irregular portion of assignment to the VM's,
we would first be able to check the span of VM and
undertaking ought to be apportioned by that.
VII. ACKNOWLEGEMENT
We acknowledge the Council of Science and Industrial
Research (CSIR) Government of India, for funding project
(No. 22/693/15/EMR-II) and providing resources to carry out
this research work.
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Simulation Based Workflow Scheduling for Scientific Application

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