framework of meta-task scheduling algorithms in cloud...

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

[email protected] 2

[email protected]

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

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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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Reference

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[9] T. Kokilavani and D. I. George Amalarethinam, “Load Balanced Min-Min Algorithm for static Meta-Task

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[12] Poonam Singh, Maitreyee Dutta, Naveen Aggarwal, “A review of task scheduling based on meta-heuristics

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