Review and comparison of tasks scheduling in cloud computing

International Journal in Foundations of Computer Science & Technology (IJFCST) Vol.6, No.1, January 2016
DOI:10.5121/ijfcst.2016.6107 87
REVIEW AND COMPARISON OF TASKS SCHEDULING
IN CLOUD COMPUTING
Yousef Tohidirad1
, Zahed Soltani aliabadi2
, Abdolsalam Azizi3
, Siamak Abdezadeh4
and Mohammad Moradi5
1
Department of Computer Engineering, Electronic Branch, Islamic Azad University,
Tehran, Iran
2,3
Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia,
Iran
4
Department of Computer, Boukan Branch, Islamic Azad University, Boukan, Iran
5
Department of Computer, Baneh Branch, Islamic Azad University, Baneh, Iran
ABSTRACT
Recently, there has been a dramatic increase in the popularity of cloud computing systems that rent
computing resources on-demand, bill on a pay-as-you-go basis, and multiplex many users on the same
physical infrastructure. It is a virtual pool of resources which are provided to users via Internet. It gives
users virtually unlimited pay-per-use computing resources without the burden of managing the underlying
infrastructure. One of the goals is to use the resources efficiently and gain maximum profit. Scheduling is a
critical problem in Cloud computing, because a cloud provider has to serve many users in Cloud
computing system. So scheduling is the major issue in establishing Cloud computing systems. The
scheduling algorithms should order the jobs in a way where balance between improving the performance
and quality of service and at the same time maintaining the efficiency and fairness among the jobs. This
paper introduces and explores some of the methods provided for in cloud computing has been scheduled.
Finally the waiting time and time to implement some of the proposed algorithm is evaluated.
KEYWORDS
Cloud Computing, scheduling algorithms, Scheduling Management, Virtual Machine (VM), CloudSim.
1. INTRODUCTION
Modern communication has been termed as ‘viral exchange of information’. This follows to the
effect that the contemporary world is subject to internet communication, which sees people share
information in a ‘ghostly’ manner. In the actual sense, the ‘people’ who communicate during this
process are the machines involved, mostly the computers and servers [1]. Users of the internet
have seen continuous manipulation of data that they share. This aspect is controlled by the fact
that users are many, thereby making the computers to be subject to heavy task scheduling
protocols. This aims to have many people access the internet with ease and in the required
bandwidth. The interconnection of these processes, despite the difference in the computer
processors, is termed as Cloud Computing.
Cloud computing is a technique that uses the most elemental issues of information sharing. In this
manner, there is a rightly developed infrastructure as well as the interrelating services. Different
relating concepts are described within the aspects of the process. A number of computers are
always connected in the network in real-time [2]. Users have the ability to reach the GUI features
without knowing the applied expertise on the same. This is well laid under the principle of

88
visualization. Visualization is the main intention of the technology with the intention of running
numerous VMs on a single machine with the required specifications of load balancing. In this
way, a good task scheme is used to expand the ability for the load balancing under task
scheduling within the specified distribution system. There is an ABC presence inspired by the life
of the honeybee. To improve the performance of the process, there are created set of behaviours
and techniques designed by the required personnel to utilize the given designs under the designs
of the ABC algorithm [3].
2. MATERIALS AND METHODS
2.1. Basics of ABC
As a concern of VM scheduling, ABC algorithm has stood as the ideal measure for its success for
most special implementations [4].While being used from time to time, the algorithm has been
effective for the establishment of the right value. While working under the principle of the
honeybees, information sharing on machines is based on source details, destination, network
quality, player of the network, threats and design tricks of the network. This can be describes by
the ABC pseudo code shown in Figure 1 [1].
Figure 1. ABC pseudo code/ basic algorithm of ABC aimed at having tasks scheduled [1]
2.2. ABC in application
The major intention of Cloud Computing is the existence of self-motivated a pool of VM
resources [5]. The VM requirements are defined according to the system’s users. This is done in
accordance with the expected design specifications. Using the acceptable Cloud Management
policies, possibility for routing specifications is established. This aspect is done within the limits
of the available servers while looking at the cloud management. Under the same specifications, it
is possible have different degrees of scheduling management while considering the load levels
within VMs. The balancing along this line should leave the best room for the reduction of
maskespan and response time.

89
Since the machines have the general outlook of virtual presence, it is good to represent them
under a set of numbers for the resultant or overall virtuosity as shown in the equation (1) [1].
VM = {VM1, VM2, VM3,… ,VMN} (1)
These machines are subject to individual tasks that result into an overall system task as shown in
the equation (2) [1].
Tack = {tack1, tack2, tack3,… ,tackK} (2)
The machines work in tandem (parallel) using different processors. In this way, the scheduling
ability is non-preventive. As a result, figure 2 shows the diagram a flowchart of the task
scheduling under the ABC instructions [1].
Figure 2. ABC instruction and the scheduling modality [1]

90
In the second process, evaluation of the population’s fitness is a calculation based on the formula
(3) [1].
j
n
i
ij
ij
VMofcapacitytheofevalution
lengthtask
fit
∑=
= 1
_
(3)
In this case, the fitness of the process numbers are shown by fit within the capacity of VM within
the tasks of descriptions task length. To obtain the overall capacity, equation (4) is applicable [1].
jjj bwvmmipspenumpecapacityj __*_ += (4)
In this case, the number of processors in the system is indicated by _ within the
instruction limits shown by _ in the required bandwidth of _ as per the ability of
the virtual machines. The identification and the allocation of these elements lead to the fitness
selection of the required neighbourhood VMs. When the process recruitment reaches, the
bounding element is definable by the formula (5) [1].
j
n
i
i
ij
VMofcapacitytheofevalution
lengthinputfilelengthtask
fit
∑=
+
= 1
__
(5)
In this case, the input File length becomes the size of the task with pending execution details.
Once this is done, the overshadowing fitness is chosen in line with the assign task VM. This
allows the calculation of load balance within the workload VMs as per the information obtained
from the datacentre [6]. It becomes easier to get the standard deviation with respect to the actual
load VMs as formula (6) [1].
2^)(
1
..
0
∑=
−=
n
j
j XX
n
DS (6)
Any VM now has a processing time indicated by the equation (7) [1].
j
k
i
i
j
capacity
lengthtask
X
∑=
= 1
_
(7)
The mean processing time for all the VMs is also indicated as formula (8) [1].
n
X
X
n
j
j∑=
=
1
(8)
From the above conclusive equations, it can be stated that whenever S.D. of the full VM becomes
less or equal to mean, a balanced state of the system is experienced. In case S.D. is higher than
the mean, the imbalance state prevails [7].

91
2.3. Results and discussion
By the use of a CloudSim tool (any version from 3.0.1); the simulation platform is subject to the
table 1 [1].
TABLE 1. Parameters for the CloudSim setting [1]
For setting the ABC algorithm, the parameters are shown in TABLE 2 [1].
TABLE 2. Parameters for improved ABC setting [1]

92
As possible to establish, the assessment of the procedure is describable under three algorithmic
instructions. There is the presence of First Come First Served (FCFS) that works on the
chronological reach of the given task. There is the Shortest Job First (SJF) that considers the
sequencing by making the selection of the first job in short terms stated [8]. The Longest Job First
(LJF) ensures that the heavy job is selected first. The results of the first and the second
experiments are shown in Figures 3 and 4[1, 5].
Figure 3. Comparison of makespan and the tasks
Figure 4. Comparison of makespan and the number of VMs

93
As shown in figure 3, there are a number of increasing requests. As a result, the average numbers
of requests being made are progressing under the same design specifications [9, 10, 11, and 12].
Consequently, ABC_LJF can be said to be effective within the required scheduling on the full
system [12, 13]. As for the second experiment, there is an exponential decay of makespan with
the number of VMs. In the same design show, the previous conclusion still prevails.
3. CONCLUSION
As the paper indicates, ABC controlled algorithm is seen to be suitable for solutions surrounding
VM scheduling management so that the tasks are scheduled in the right manner and needs. This
goes under the adjustable features in Cloud Computing. As these parameters are optimized, the
stability of the system still prevails. This allows the system to operate with optimum utility. It is
then imperative to state that ABC algorithm is good for Cloud Computing and its environment
under the utility of the required parameters in a manner that the tasks are handled with utmost
utility. This stability makes the system to works without crashing in the manners of ABC_LJF
needs and designs.
REFERENCES
[1] B. Kruekaew, W. Kimpan, “Virtual Machine Scheduling Management on Cloud Computing Using
Artificial Bee Colony”, Proceedings of the International MultiConference of Engineers and Computer
Scientists, pp.978-988, 2014.
[2] D.H. Khan, D. Kapgate, P.S. Prasad, “A Review on Virtual Machine Management Techniques and
Scheduling in Cloud Computing” International Journal of Advanced Research in Computer Science
and Software Engineering, 3(12), pp.838-845, 2013.
[3] S. Hadi, “Advantages, Challenges and Optimizations of Virtual Machine Scheduling in Cloud
Computing Environments”, International Journal of Computer Theory and Engineering, 4(2), 2012.
[4] G. TARUN, “Host Scheduling Algorithm Using Genetic Algorithm In Cloud Computing
Environment”, International Journal of Research in Engineering & Technology, 1(1), 2013.
[5] K. Kiran, “An Adaptive Algorithm For Dynamic Priority Based Virtual Machine Scheduling In
Cloud”, International Journal of Computer Science Issues, 9(6), 2012.
[6] I. Moschakis, H. Karatza, “Performance and Cost evaluation of Gang Scheduling in a Cloud
Computing System with Job Migrations and Starvation Handling”, Thessaloniki: Aristotle University
of Thessaloniki, 2013
[7] N. Ramkumar, “Efficient Resource Utilization Algorithm (ERUA) for Service Request Scheduling in
Cloud”, International Journal of Engineering and Technology, 5(2), 2013.
[8] V. Vignesh, K.K. Sendhil, N. Jaisankar, “Resource management and scheduling in cloud
environment”, International Journal of Scientific and Research Publications, 3(6), 2013.
[9] K. Rasmi, V. Vive, “Resource Management Techniques in Cloud Environment - A Brief Survey”,
International Journal of Innovation and Applied Studies, 2(4), 2013.
[10] S.P. Abirami, R. Shalini, “Linear Scheduling Strategy for Resource allocation in Cloud
Environment”, International Journal on Cloud Computing and Architecture, 2(1), 2012.
[11] S. Paulus, U. Riemann, “An Approach for a Business-driven Cloud-compliance Analysis Covering
Public Sector Process Improvement Requirements”, International Journal of Managing Public Sector
Information and Communication Technologies (IJMPICT), Vol. 4, No. 3, pp. 1-13, 2013.
[12] R. Saini, Indu, “EFFICIENT JOB SCHEDULING OF VIRTUAL MACHINES IN CLOUD
COMPUTING”, International Journal of Advanced Research in Computer and Communication
Engineering, pp.3349-3354, 2013.
[13] S. Hashemi, “Cloud computing technology for E-government architecture”, International Journal in
Foundations of Computer Science & Technology (IJFCST), Vol. 3, No.6, pp. 15-23, 2013.

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