IRJET- Scheduling of Independent Tasks over Virtual Machines on Computational Cloud Environment

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 07 Issue: 01 | Jan 2020 www.irjet.net p-ISSN: 2395-0072
© 2020, IRJET | Impact Factor value: 7.34 | ISO 9001:2008 Certified Journal | Page 299
Scheduling of Independent Tasks over Virtual Machines on
Computational Cloud Environment
Shivam Bhagwani1, Dr. Lokesh Kumar R2, Akshat Shrivastava3, Arpan Shrivastava4
1,3,4 Student, Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, India
2Professor, School of Computer Science and Engineering, Vellore Institute of Technology, Tamil Nadu, India
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Abstract - Cloud computing is emerging with the
advancements in technology. Whether itistheneedofstorage,
infrastructure, platform or software, major cloud providers
are facilitating all of them to the needy. Not just that, cloud
computing also plays a role in resource sharing, result
aggregation, etc. with the help of distributed and parallel
computing. But the main challenge that this field faces is the
efficient use of resources granted for tasks. In order to
regulate and utilize these resources efficiently, it is needed
that the tasks and processes that the machines participate in,
are scheduled and executed in a proper manner so that each
and every machine is utilized to its maximum potential
possible. This paper addresses this problem over virtual
machines in computational cloud environment. Aim is to
schedule and execute independent tasks over these machines
using different algorithms available and compare their
performances. This will be done with the help of cloud
simulators.
Key Words: Cloud, Cloudlets, FCFS, SJF, Round Robin, PSO,
Java, Cloudsim.
1. INTRODUCTION
It is known that resources are very necessary for a plan to
successfully execute. Other than resources, it is very
important to move ahead with them with a proper planning.
This paper discusses some similar scenario in the cloud
computing based environment. Cloud computing, as one of
the newest and swiftly developing computer technologies,
needs some similar resources and planning.
In cloud computing, resources are nothing but virtual
machines, the CPUs which are part of those machines,
memory and storage capacity of the executors on which
tasks are to be executed, cloudlet schedulers, etc. All these
resources are accessed by tasks which are nothing but
cloudlets in the cloud environment.
Every task has its characteristics like task length, size,
estimated memory required, estimated time required, etc.
Similarly, these terms are used for cloudlets being cloudlet
length, cloudlet size, memory required by the cloudlet, time
required by the cloudlet, etc.
For the purpose of implementation, the paper uses two java
frameworks namely CloudSim and WorkflowSim. CloudSim
is well structured and robust set of packages which help in
simulation and modelling of cloud computing infrastructure
and services. WorkflowSim on the other hand is set of
packages which are extended to provide implementation of
planning algorithms before actually scheduling cloudletsfor
service on virtual machines.
Few components which play important role for thispurpose
include-
1. Cloudlet- This is similar to a task that has to be
executed on cloud based environment with its own
length that is similar to instruction length. Apart
from this, it has properties such as that of image
size and processing unit requirements.
2. Data Center- This is responsible for allocating core
services at the level of infrastructure. This brings
together all the configurations of resources which
are going to execute cloudlets. Data Center also
plays role in setting up of policies for memory and
storage devices.
3. Data Center Broker-Acts as a mediator between
user and service providers in a cloud ecosystem.
With the help of Cloud Information Service (CIS) it
recognizes suitable serviceprovidersforanytask or
set of tasks that are pending and are to be taken
care of by some executor.
4. Host- This is a model for physical component on
cloud based ecosystem. It has memory, a guided
policy, bandwidth for virtual machines and of
course list of processing elements.
5. VM Scheduling Policy- It is defined at two levels
being Host Level at which specification for overall
processing power is defined. At VM Level, the
machine distributes its own processing power to
tasks (or cloudlets) depending upon their
characteristics. [1]
Using all the components listed above and including some
more, this paper provides an analysis of four of the most
popular algorithms in use. This set of algorithms includes
a. FCFS
b. SJF
c. Round Robin; and
d. Particle Swarm Optimization (A stochastic
population-based algorithm)

Figure 1: Cloudsim Architecture
This paper mainly focuseson comparisonofthreetraditional
algorithms with an advanced algorithm, make and produce
an observation about how and to what extent PSO algorithm
can deliver extra-ordinary performance which can’t be
achieved by the traditional ones discussed.
2. LITERATURE SURVEY
Many surveys have been conducted to come up with
conclusions which all indicate an increment in the usage of
cloud-based services over the last decade. Cloud services are
considered as very essential. One of the key features of cloud
computing is that it provides direct and remote access to
many computing services. The examples of the services
include servers, applications,network,etc.Withthisdemand,
it has been realised that scheduling of these services has
becomea very important issue. In year2018,Anushreealong
with other co-authors completed a detailed analysis of
variety of taskschedulingalgorithms.Thealgorithmsinclude
priority-based performance algorithm, template based
genetic algorithm, hybrid multi objective PSO algorithm,
intelligentwaterdropalgorithm,improvedgeneticalgorithm,
etc. and found none to be satisfying on all parameters. [2]
Cloud computing is considered to be a computing skill that
requires allocation of the computing resources as well as its
accompanying services which are based on the pay per use
model. In order to access the computers which are remotely
located, scheduling is considered to be the most important
task. Scheduling of the task is considered to be an NP
completeproblem.Inordertoachieveanoptimumandbetter
performance for the cloud resources, we need some
successful and also some proficient methodologies of
scheduling. The referenced paper discuses, a scheduling
algorithm“PrioritybasedPerformanceImprovedAlgorithm”.
This algorithm takes into consideration the priority of the
meta-tasks of users. The resultant high priority meta-task is
scheduled using the Min-Min algorithm whereas the normal
priority one is scheduled using Max-Min algorithm. It
concluded that the proposed algorithm is found to give
minimum make span with better resource usage. [3]
Another publication by Gajera, Vatsal, Rishabh Gupta and
Prasanta K Jana, an algorithm Min-Max which is widely used
fornormalization of data in the field of data mining is usedas
the basis for tuning the functionality to adjust with cloud
computing. This came out to be named as Normalized Multi
Objective Min-Min Max-Min Scheduling. It outperformed
simple Min-Min and Max-Min algorithms. [4]
Toktam GhafarianandBahmanJavadiinyear2015,proposed
a cloudaware data intensiveworkflowschedulingsystem.As
the current volunteer computing systems have the best
infrastructure to execute high performance jobs however,
they consume high computing power to run large data
intensive scientific workflows.Withanaimtosolvetheabove
problem by making a hybrid of the volunteer computing
system and cloud resourcespossibilityofenhancementinthe
efficiency in usage of these systems saw an increment. [5]
With the help of CloudSim, this process of analysis became
very popular. Weighted Round Robin, Start Time Fair
Queuing and Borrowed Virtual Time were among the many
algorithms that got introduced and proposed. BVT proved to
outperform the other two in a comparison provided by
Jambigi, Murgesh V, VinodDesaiandShrikanthAthanikar.[6]
Implementingalgorithms forthe purpose was not enoughso
few stepped forward to develop different frameworks. Li,
Feng, Lin Zhang and Lei Ren introduced an idea of four
layered model of task scheduling which included process
layer, productlayer, partlayer and component layer. Genetic
algorithms proved to perform well and many mutations and
crossovers were tested for the purpose of queuing tasks. [7]
The scheduling of number of errands of processes, while
handling with cloud assets, in a most beneficial way for
example minimum computation time still happens to be an
appealing examinationregion.Therefore,SandeepSinghBrar
and Sanjeev Rao illustrated the MaxMin calculation for
scheduling work process undertakings that is centred on the
thought of dependent and independent tasks and process
independent tasksinparallelthatlegitimatelygivesbenefitin
minimizing computation time. [8]
3. SETUP AND EXPERIMENT
Before starting with the scheduling, planning is the step that
has to be taken in order to prepare the environment for
execution of task to take up the load of completion beforethe
deadline. The difference between the current time and
deadline guides the service provider to look up for a suitable
scheduling heuristic and maintain cost and power.
A. VM Characteristics
Here, a very concise and to the point comparison of four
different heuristic is presented. All these algorithms are

tested over three differently capable virtual machines by
tuning the number of CPUs embedded.
1. VM-1
RAM: 512MBs
MIPS: 1000
Bandwidth: 1000
CPU Cores: 1 (Single Core)
2. VM-2
RAM: 512MBs
MIPS: 1000
Bandwidth: 1000
CPU Cores: 2 (Dual Core)
3. VM-3
RAM: 512MBs
MIPS: 1000
Bandwidth: 1000
CPU Cores: 4 (Quad Core)
B. Pseudocodes for Algorithms Implemented
1. First Come First Serve
Step1: Put cloudlets in a queue data structure
Step2: First task waiting time will be 0.
Wt[0]=0;
Then calculating the whole waiting time
wt[i] = bt[i-1] + wt[i-1];
Step3: Next is to calculate turn around time
Tat[i]=bt[i]+wt[i];
Step4: Calculate the average time
total_wt/n
total_tat/n
averagetime(processes,n,burst_time)
2. Shortest Job First
Step1: Create a heterogeneous cloud computing
environment
Step2: Input all the required tasks and calculate the
of tasks length for all the cloudlets
Step3: Sort them in ascending order using a sorting
algorithm
Step4: Start with the first process and put other
tasks in queue
For (process_id=0;process_id<n;process_id++)
Execute(cloudlet_process);
3. Round Robin
Step1: Initialise one array for keeping track of
remaining execution time for cloudlets.
Step 2: Initialise another array for storing the
waiting time of different cloudlets.
Step 3: Initialize time(t)=0
Step 4: Traverse through cloudlets while all ofthem
are not processed. For any process:
If remaining time > time slice
t=t+time slice
remaining time=-time slice
Else (For the last cycle)
t=t+ remaining time
remaining time=0
4. Particle Swarm Optimization
Step 1: Define objective function f(x).
Step 2: Generate initial population of particles.
Step 3: Compute fitness of each particle and set
particle best for individual particles.
Step 4: while (t<MaxGeneration) || (!stop)
Select the GBest particle in swarm
(minimum fitness value)
Step 5: Traverse through population
Calculate fitness
Calculate rank or position
Step 6: Traverse through population
Assign new fitness value
Assign new rank
Step 7: Find best particle.

C. Experiment
1)Running algorithms on VM-1
With single core processing capability, following results
were obtained. It can be seen from the graph presented that
PSO takes around 5th of the time taken by traditional
algorithms.
Figure 1: Graph for make-span for different
algorithms on VM-1
It is inferred that among standard algorithms, FCFS has
emerged to be better performing as compared to other
algorithms. SJF faces the issue of overhead incurred by the
sorting of the cloudlets in increasing order of their length
which is directly proportional to time of execution required
by them. On the other hand, cloudlets in round robin
sometimes wait for longer duration due to pre-emptive
nature of the algorithm.
2) Running algorithms on VM-2
With dual core processing elementsintheCPU,itisobserved
yet again that PSO brings out the best when the aim is to
minimise the total execution time in the remote
environment.
In this scenario, FCFS, SJF and Round Robin have performed
almost as good as each other. There is nothing wrong in
commenting that none of these have competed well enough
with other two to outperform. Main reason for why Round
Robin and SJF stay behind FCFS remains the same.
algorithms on VM-2
3) Running algorithms on VM-3
Interesting outcomes were gained when these algorithms
were tested on quad core CPUs which are highly prevalent
these days in laptops, smartphones, computers and various
other technical devices.
PSO doesn’t show much improvement in this case but what
is worth noting is the sudden increase ofmakespanin round
robin algorithm. This can be assigned to the collision of
multiple CPU cores in order to process the same cloudlet.
This is where some kind of lock should come into role for
avoiding this collision.
algorithms on VM-3
Another observation that attracted glances is that PSO still
manages to perform as good as earlier not allowing the
number of cores affecting its own results. It still completes
managing and executing cloudlets in 5th ofthetimetakenby
SJF and FCFS.
Figure 4: Round Robin execution pattern through line
chart

4. RESULTS AND DISCUSSIONS
The table summarizes all the results that were gained from
the experiments performed onheuristicsovermachines that
happen to act and process differently on changing
configurations.
ALGORITHM
NO OF CPU
CORES
FCFS SJF ROUND
ROBIN
PSO
1 50.05 52.55 54.27 13.93
2 25.08 26.41 27.48 4.16
4 13.09 13.84 34.26 3.1
TABLE 1: Overview of the results obtained through cross
implementation of algorithms and Virtual Machines
5. REFRENCES
[1] Mondal, Choudhary."Islam“PerformanceAnalysisofVM
SchedulingAlgorithmofCloudsiminCloudComputing”."
International Journal of Electronics & Communication
Technology (2015): 49-53.
[2] Anushree, B., andVMArul Xavier."ComparativeAnalysis
of Latest Task Scheduling Techniques in Cloud
Computing environment." 2018 Second International
Conference on Computing Methodologies and
Communication (ICCMC). IEEE, 2018.
[3] Amalarethinam, DI George, and S. Kavitha. "Priority
based performance improved algorithm for meta-task
scheduling in cloud environment." 2017 2nd
International Conference on Computing and
Communications Technologies (ICCCT). IEEE, 2017.
[4] Gajera, Vatsal, Rishabh Gupta, and Prasanta K. Jana. "An
effective multi-objectivetask schedulingalgorithmusing
min-max normalization in cloud computing." 2016 2nd
International Conference on Applied and Theoretical
Computing and Communication Technology (iCATccT).
IEEE, 2016.
[5] Ghafarian, Toktam, and Bahman Javadi. "Cloud-aware
data intensive workflow scheduling on volunteer
computing systems." Future Generation Computer
Systems 51 (2015): 87-97.
[6] Jambigi, Murgesh V., Vinod Desai, and Shrikanth
Athanikar. "Comparative Analysis of different
Algorithms for scheduling of tasks in Cloud
Environments." 2018 International Conference on
Computational Techniques, Electronics and Mechanical
Systems (CTEMS). IEEE, 2018.
[7] Li, Feng, Lin Zhang, and Lei Ren. "A Production-Based
Scheduling Model for Complex Products in Cloud
Environment." 2017 5th International Conference on
Enterprise Systems (ES). IEEE, 2017.
[8] Brar, Sandeep Singh, and Sanjeev Rao. "Optimizing
workflow scheduling using Max-Min algorithm in cloud
environment." International Journal of Computer
Applications 124.4 (2015).

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