Post on 24-Jul-2020
Framework of Meta-Task Scheduling
Algorithms in Cloud Environment
Dr. D. I. George Amalarethinam1
, S. Kavitha2
1
Bursar, Director MCA, Department of Computer Science, Jamal Mohamend College, Tiruchirappalli (Affiliated
to Bharathidasan University, Tiruchirappalli), India. 2
Research Scholar, Department of Computer Science, Jamal Mohamed College, Tiruchirappalli (Affiliated to
Bharathidasan University, Tiruchirappalli), India. 1
di_george@ymail.com 2
kavidharan2000@gmail.com
Abstract – Scheduling is an important issue in Cloud computing for the both Cloud User and Cloud Service Provider.
Scheduling of meta-task is difficult in Cloud environment because of its heterogeneity nature. Cost, makespan, fault
tolerance, response time, waiting time and resource utilization are the parameters of Meta-task scheduling. Three
algorithms namely, Rescheduling Enhanced Min-Min (REMM) algorithm, Priority based Resource allocation (PRA)
algorithm and Ant Colony Optimization based on Dual Objectives (ACODO) algorithms are proposed for Meta-task
scheduling in efficient manner in Cloud environment. In this paper, a new Framework of Meta-task Scheduling
Algorithms in Cloud Environment is proposed to integrate all the three proposed algorithms and to make the meta-task
scheduling easier for the Cloud users.
Keywords: Meta-task, REMM, PRA, ACODO, makespan, resource utilization.
I. INTRODUCTION
A set of tasks that do not depend on each other to complete their execution are called meta-
task or independent tasks [1]. Scheduling a Meta-task is a big deal in cloud environment because
of the task heterogeneity, resource heterogeneity and priority of the tasks [2]. The primary goal
of meta-task scheduling is to execute the meta-task set with minimum cost, makepsan and
maximum resource utilization. To achieve this goal, two heuristic algorithms namely,
Rescheduling Enhanced Min-Min (REMM) algorithm [3], Priority based Resource Allocation
(PRA) algorithm [4] and one meta-heuristic algorithm, namely, Ant Colony Optimization based
on Dual Objective (ACODO) algorithm [5] are designed, implemented and tested in CloudSim.
All three algorithms are tested for both arbitrary meta-task set and also Bag-of-Task (BoT) meta-
task set [6]. This paper proposed a Framework to integrate all the three algorithms for the
convenience of the Cloud users, who need to execute their meta-task set in Cloud environment.
II. RESCHEDULING ENHANCED MIN-MIN (REMM) ALGORITHM
Rescheduling Enhanced Min-Min algorithm is proposed for the users who never worried about
the resource type, execution time of the task and priorities. REMM algorithm has two steps.
Enhanced Min-Min (EMM) algorithm [7] is followed in first step. EMM algorithm is used to
balance the load for all the resources. Rescheduling strategy is followed in step two. This is used
to minimize the makespan by rescheduling the task with higher execution time to the fastest
resource based on maximum completion time of the resources. The proposed REMM algorithm
The International journal of analytical and experimental modal analysis
Volume XI, Issue XI, November/2019
ISSN NO: 0886-9367
Page No:2588
is implemented and tested in CloudSim. The result is compared with Min-Min algorithm [8] and
Load Balancing Min-Min (LBMM) algorithm [9]. The proposed algorithm outperforms the Min-
Min and LBMM algorithms. Image processing, video rendering are the meta-task applications
that does not concern about the type of the resource, execution time of the task and priority of the
tasks. REMM algorithm is suitable for such applications.
III. PRIORITY BASED RESOURCE ALLOCATION (PRA) ALGORITHM
Priority based Resource Allocation algorithm is proposed for the user who needs to execute the
part of meta-task set faster by paying higher cost. Remaining meta-task set is executed in normal
mode. User can submit the meta-task set with user priority [10]. PRA algorithm separates the
Expected Time to Compute (ETC) matrix as ETC1 for meta-tasks with priority and ETC2 for
meta-tasks without priority. The prioritized meta-tasks are allocated to the instances of the fastest
resource using REMM algorithm. The non-prioritized meta-task set is assigned to the
heterogeneous resources using REMM algorithm. The makespan, overall execution cost and
resource utilization of PRA is compared with the Min-Min algorithm. PRA algorithm minimizes
the makespan and overall execution cost and maximizes the resources utilization over Min-Min
algorithm. PRA algorithm is suitable for the user who wants to execute their meta-task
application that contains both priority and non-priority tasks in cloud environment.
IV. ANT COLONY OPTIMIZATION BASED ON DUAL OBJECTIVES (ACODO) ALGORITHM
Ant Colony Optimization based on Dual Objectives algorithm is proposed for the user like
scientist who needs optimized schedule to execute their meta-task applications without any
priority. The existing optimization algorithm namely, ACO in which single objective namely,
completion time is used [11]. The proposed ACODO algorithm used two objectives namely,
completion time of the task and resource utilization to get better result. The probability function
is used to select the next resource for task allocation. After getting one schedule, fitness function
is calculated. Fitness function is designed based on linear weighting method. The updating the
pheromone is done based on the value of fitness function. This process continues till the result
convergences. There are several scheduling parameters [12] exists in the literature. In this paper,
Makespan, Resource utilization and Cost are parameters used to compare the results of ACODO
with ACO. The results show that the performance ACODO is better than the existing ACO.
ACODO algorithm is suitable for the meta-task applications like DNA analysis, disease
causing DNA searching and application of high energy physics need optimized schedule without
having any priority.
V. THE PROPOSED FRAMEWORK OF META-TASK SCHEDULING ALGORITHMS
This framework is proposed for the cloud users who are expecting to execute their meta-task
applications in efficient manner. The proposed framework of meta-task scheduling is shown in
figure 1.
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Figure 1. Framework of Meta-Task Scheduling Algorithms
From figure 1, it is well known that the cloud user enters into this framework by entering the
username and password. Then the user has to select the type of application (Arbitrary meta-task
application/BoT application) by entering the option 1 or option 2. This is shown in figure 2.
Figure 2. Selection of type of application
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After application selection, the user selects one of the types, namely, normal execution;
priority based execution or optimized execution as depicted in the Figure 3.
Figure 3. Selection of user requirement
In the next step, user has to enter the number of tasks with size in meta-task application. This
is shown by the figure 4.
Figure 4. Submission of tasks with size
Rescheduling Enhanced Min-Min algorithm is called after entering the number of tasks with
size. Expected Time to Compute (ETC) matrix is generated by REMM algorithm and the final
schedule is produced with makespan, resource utilization and over all execution cost. Figure 5
shows the generated ETC by REMM.
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Figure 5. ETC generation by REMM algorithm
The output generated by EMM and final output of the submitted meta-task application by
REMM are shown by the figure 6.
Figure 6. Output of REMM algorithm
In the second scenario, the user selects arbitrary meta-task set with priority, the total number of
tasks in arbitrary meta-task set and number of priority tasks in that given meta-task set are
entered by the user. After submitting the task details with priority, the ETCs are generated. The
generated ETCs for priority tasks set and non-priority tasks set are shown in the figure 7.
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Figure 7. Generation of Priority and Non-priority ETCs
The output produced by REMM for the submitted priority based arbitrary meta-task
application is shown by the figure 8.
Figure 8. Output of Priority based Arbitrary meta-task application by REMM
In the third scenario the users select the BoT application and select the optimization as
requirement. The number of bags and the number of tasks in each bag are entered by the user.
The generated ETC of BoT for optimization after submitting the meta-task set details is shown in
the figure 9 and the corresponding result of ACODO algorithm is shown in the figure 10.
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Figure 9. ETC of BoT for optimization
Figure 10. Output of ACODO
VI. CONCLUSION
The proposed algorithms have been executed and integrated as a framework in CloudSim for
the convenient of cloud users. The proposed algorithms are specifically used for meta-task
applications by specifying the requirements (non-priority, priority and optimization) of the users.
The proposed framework is to be implemented in real cloud environment by registering this
framework as a service in Universal Description and Integration (UDDI) in future.
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ISSN NO: 0886-9367
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Reference
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ISSN NO: 0886-9367
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