A Task Scheduling Algorithm in Cloud Computing

ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Vol. 2, Issue 1, pp: (310-313), Month: April 2015 – September 2015, Available at: www.paperpublications.org
Page | 310
Paper Publications
A Task Scheduling Algorithm in Cloud
Computing
Ali Bagherinia
Department of Engineering, Dehdasht branch, Islamic Azad University, Dehdasht, Iran
Abstract: Efficient task scheduling method can meet users' requirements, and improve the resource utilization,
then increase the overall performance of the cloud computing environment. Cloud computing has new features,
such as flexibility, Virtualization and etc., in this paper we propose a two levels task scheduling method based on
load balancing in cloud computing. This task scheduling method meet user's requirements and get high resource
utilization that simulation results in Cloud Sim simulator prove this.
Keywords: cloud computing; task scheduling; virtualization.
I. INTRODUCTION
Cloud computing dates back to the 1960’s when John McCarthy opined that “computation may someday be
organized as a public utility”. Amazon played a key role in cloud computing development by launching Amazon web
service on utility basis in 2006. Many users share cloud resources by submitting their computing task to the cloud
system. Scheduling these tasks is a challenge to cloud computing environment. Task scheduling is an important part of
cloud computing, which is a mechanism that maps users’ tasks to appropriate resources to execute, its efficiency will
directly affect the performance of the whole cloud computing environment. Compared with grid computing, it has some
properties such as virtualization and flexibility for cloud computing that grid computing doesn't have [1]. By using
virtualization technology, all users have their own virtual machine and don’t affect each other. Furthermore utilization of
resources is improved effectively, and the users’ application is running independency and security of system and service
availability is improved. Flexibility is the dynamically increasing or decreasing of resource provided by cloud computing
environment according to users’ demand. Cause these features, the task scheduling approaches for grid computing cannot
work effectively in the cloud computing environment. Different scheduling methods are proposed in [2], [3], [4], [5],
[6], [7], [8], [9] and [10].
In this paper, a task scheduling method based on the two levels of load balance is proposed, which consider the flexibility
and virtualization in cloud computing to meet improve the utilization of resources and the dynamic task requirements of
users.
II. SCHEDULING MODEL
Cloud Computing Architecture includes three layers, infrastructure layer, platform layer and application layer [11]. The
infrastructure layer is a set of virtual hardware resources and related management function. The platform layer is a set of
software resources, which can provide an environment for developing, running, managing and monitoring cloud
application to. The application layer is user oriented; it implements the interaction mechanism between user and service
provider with the support of platform Layer. Users can submit tasks through the application layer. According to the above
architecture, we propose two levels scheduling model [12] in this paper as shown in Fig. 1.

ISSN 2350-1022
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Paper Publications
Fig. 1 Two levels scheduling Model
As shown in Fig. 1, the first scheduler create the task description of a virtual machine, including the task of computing
resources, network resources, storage resources, and other configuration information, according to resource demand of
tasks. Then the second scheduler finds appropriate resources for the virtual machine in the host resources under certain
rules, based on each task description of virtual machine. Via the two levels scheduling, the task can obtain the required
resources, and it would not lead to the resource allocated to some tasks is less than requirement and increase the
implemental time while others are more than their requirements and lead to the waste of resources.
Here all tasks are considered computational, and the tasks are independent each other, and the execution of the task
replication is also not considered.
III. SCHEDULING ALGORITHM
The load of virtual machine discussed in this paper is expressed by the predicted executing time of tasks running in the
virtual machine, named as VLi[13].And the load of host is expressed by the average load of the virtual machine that run
on it, named as HLi, the average load value and load balancing evaluating value B of cloud computing environment
can be defined as follows:
∑ ⁄ ,
∑ ⁄ and
√∑ ⁄ ,where the n is the number of virtual machines that run on
the host and m is the number of hosts ism, the smaller value B the better load balancing and the bigger value B the worse
load balancing.
In order to meet users’ requirements and increase the utilization of resources, a scheduling algorithm based on load
balancing is proposed in this paper. The algorithm is based on the former scheduling model discussed, considering the
flexibility and virtualization features of cloud computing, it is divided into two levels scheduling, one is the mapping from
task to a virtual machine, another is mapping from the virtual machine to host resources. Generally, for the requirement of
the task, users want to get the best response time. Therefore, only task response time and the demand for resources are
considered in this paper. At the same time, because tasks are dynamic, they may arrive randomly. If the tasks arrive at
same time, they will be sorted ascending according to the resource applied by users. And if they arrive at different time,
they will be sorted according to the time sequence arrived. The steps of this algorithm are described as follows:
Step1: sort host resources ascending order of their processing power.
Host Host
VM VM VM
VM VM
App App
App

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Paper Publications
Step2: According to the task model the first level scheduler establish the virtual machine description according to the
properties of task, providing configuration information for allocation of resources and creation of the virtual machine.
Step3: According to the virtual machine description of Task ti, select a host resource hj that can meet the required
resources and the load is lightest. If the host exists, create the virtual machine and allocate the required resource for it,
then update the available resources h with total service ability Fcap of Host hj , otherwise take the Task t to the tail of the
task queue and waiting for the next scheduling.
Step4: If the resource requirements of the Task ti increase, find whether the host whose virtual machine of Task ti run on
can meet the additional required resources, if it exists, allocate the additional required resources for it, reconfigure the
virtual machine, and then update the host's available resources. Otherwise, the virtual machine is migrated to the host with
lightest load and the additional required resources to execute continuously.
Step5: If the resource requirements of the Task ti reduce, release the excess resources that the virtual machine occupied,
and update the available resources hold by the host.
Step6: If Task I t has been completed, then destroy the virtual machine of Task ti and release the occupied resources for
the other unfinished tasks.
Step7: Calculate the load balancing evaluating value Bin current environment, if B is greater than the threshold value Bthr,
that indicates the load balancing state is worse, select a virtual machine with lightest load and migrate it to the host which
can meet the resource requirement with the lightest load.
Step8: Repeat step3 to 7 until all tasks are completed.
In the above algorithm, the virtual machine is scheduled to the host with lightest load each time. The advantage is to avoid
overloading for the host hold more resources. If the current virtual machine is scheduled to a host, as the computational
amount increase, leading to the virtual machine’s load is heavy, resulting in load imbalance, then take the dynamic
migration operation, keeping load balance in current environment.
IV. SIMULATION
We simulate proposed algorithm and Job Scheduling Using Fuzzy Neural Network Algorithm [ 14] in the CloudSim
toolkit [15]. This simulation mainly shows the advantage of the proposed algorithm compared to Fuzzy Neural Network
Algorithm in make span term based on the various number of tasks.
Fig. 2.Makespan Comparing
As shown in Fig. 2, with the task number increasing, the makespan in our algorithm less than Fuzzy Neural networks
algorithm, which is due to the flexibility of our algorithm.
0
20
40
60
80
100
120
0 200 400 600 800 1000
make span
Task numbers
Fuzzy alg.
proposed alg.

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REFERENCES
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[4] Huang Qi-yi, Huang Ting-lei, "An optimistic job scheduling strategy based on QoS for Cloud Computing",
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[5] Meng Xu, Lizhen Cui, Haiyang Wang, Yanbing Bi, "A Multiple QoS Constrained Scheduling Strategy of
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[8] Gunho Leey, Byung-Gon Chunz, Randy H. Katzy, "Heterogeneity-Aware Resource Allocation and
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[9] Shu-Ching Wang, Kuo-Qin Yan, Shun-Sheng Wang, Ching-Wei Chen, "A Three-Phases Scheduling in a
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[10] Peixoto, M.L.M., Santana, M.J., Estrella, J.C., Tavares, T.C., Kuehne, B.T., Santana, R.H.C., "A Metascheduler
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[11] Armbrust, M.: Above the Clouds: A Berkeley View of Cloud Computing. In: EECS Department, University of
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[12] Sadhasivam Dr., S., Jayarani, R.: Design and Implementation of an efficient Two-level Scheduler for Cloud
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[13] Xiang-hui, P., Er-hu, Z., Xue-yi, W., Guang-feng, L.: Load balancing algorithm of multicluster grid. Computer
Enineering and Applications 45(35), 107–110 (2009).
[14] Venkatesa Kumar and K. Dinesh, Job Scheduling Using Fuzzy Neural Network Algorithm in Cloud Environment,
Bonfring International Journal of Man Machine Interface, Vol. 2, No. 1, March 2012.
[15] The CLOUDS Lab.CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing
Infrastructures and Services [EB/OL],http://www.gridbus.org/cloudsim.

A Task Scheduling Algorithm in Cloud Computing

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