Performance Parameters Review of Workflow Scheduling in ...

Performance Parameters Review of Workflow

Scheduling in Public Cloud 1M. Shyamala Devi,

2Aparna S. Joshi and

3V. Usha

1Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and

Technology,

Chennai.

[email protected] 2Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and

Technology,

Chennai.

[email protected] 3Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and

Technology,

Chennai.

[email protected]

Abstract Workflow scheduling algorithm is used for load balancing in a cloud

computing environment. It performs efficient load balancing. This paper

takes a review of research carried out to improve performance of workflow

scheduling algorithm. The review is broadly analyzed considering

important performance parameters such as makespan and cost, energy

consumption, execution time and resource utilization. It was found that

most algorithms perform scheduling based on one or two parameter. A

better scheduling algorithm can be developed from the existing method by

considering more number of evaluation parameters.

Key Words:Cloud computing, workflow scheduling algorithm, load

balancing, performance parameters.

International Journal of Pure and Applied MathematicsVolume 119 No. 16 2018, 5005-5017ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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1. Introduction

Cloud computing technology (1) is gaining immense popularity in the last few

years due to its ability to utilize software, hardware, infrastructure and

computational resources as per user requirements on rent basis. However, cloud

computing faces many challenges which include security, load balancing,

resource scheduling, Quality of Service(QoS) management, energy

consumption, data lock-in and performance monitoring (2) (3). Main challenge

in cloud computing environment is load balancing (4). It is the process of

assigning and reassigning the load among available resource. (5) (6) (7).

Workflow scheduling algorithm is one of the scheduling algorithm in load

balancing. It is used to reduce makespan, cost, energy consumption, execution

time and maximizes resource utilization. It decompose bigger task into several

smaller subtask. This generated work flow must be scheduled to realize the

application execution. This benefits both cloud user and service provider as well

as minimizes throughput, cost and response time, improves performance and

resource utilization as well as energy saving.

This paper takes a review of research related to workflow specific scheduling

algorithm. The review is broadly analyzed considering important performance

parameters such as makespan and cost, energy consumption, execution time and

resource utilization. The paper is structured as follows: the introduction to

workflow scheduling algorithm is presented in section 2. Analysis of workflow

scheduling algorithm is presented in section 3 and section 4 concluded the work

carried out.

2. Introduction to Workflow Scheduling

Workflow scheduling is one of the prominent issue in cloud computing. It aims

at execution of workflows by considering their Quality of Service (QoS)

requirements such as deadline and budget constraints. In workflow scheduling,

bigger task is divided into different sub task. Resources are allocated to sub task

in such a way that predefined criteria’s are met. In a workflow application,

series of steps are executed. These steps have parent child relationship. This is

important particularly in problems of Bioinformatics, astronomy and business

enterprise (8) where to carry out bigger task, a set of sub task is executed in a

particular sequence. The next section describes workflow structure and various

parameters involved.

A. Structure of Workflow Scheduling

Workflow structure describes relationship among the tasks of the workflow.

Workflow structure is represented by Directed Acyclic Graph (DAG) and Non-

Directed Acyclic Graph (N-DAG) (9) as shown in figure1. Further it is

subdivided by a sequence of task execution, see Figure 1. First is Sequence

Structure where tasks are executed in series and new task is executed only after

completion of previous task. Second is Parallelism Structure where tasks are

International Journal of Pure and Applied Mathematics Special Issue

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executed concurrently. Third is a Choice Structure where tasks are executed in

series or concurrently.

Figure 1: Classification of Workflow Structure

A workflow application is generally represented as Directed Acyclic Graph

(DAG) such as 𝐺 (𝑉,𝐸)where 𝑉 is number of task and 𝐸 is the information

among the task. In Directed Acyclic Graph (DAG), there are two tasks: entry

task and exit task. Entry task is a root task and exit task is a task which does not

have any further task. It is also called child task. This is further illustrated using

figure 2. Parent task A called as entry task which executes before child task B,

C and D. Task E which is called as exit task executes after execution of task B,

C and D.

Figure 2: A Workflow Representation in the form of DAG

B. Performance Parameters in Workflow Scheduling

In Workflow scheduling algorithms, various parameters are used to evaluate

system performance (9). These parameters are described below:

Execution Time: This is exact time taken to execute the given task

Makespan: Maximum time required for completion of job i.e. from job

submission to job completion

Energy Consumption: Amount of energy consumed by all nodes

Cost: Cost for usages of resources

Resource Utilization: Maximum utilization of computing resources


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Fault Tolerance: It determine the capability of algorithm even after the failure of

one or more component pieces in any layer

Throughput: Number of process completed per unit time

Migration time: Time required to transfer one task from one node to another

anode

Performance: Overall efficiency of load balancing algorithm

Carbon emission: Amount of carbon produce by all resources

The research in workflow scheduling algorithm was studied considering the

above parameters. Next section describes the analysis of existing work flow

scheduling algorithm considering above parameters.

3. Analysis of Existing Workflow Scheduling Algorithm

Number of authors has done work in the area of workflow scheduling

algorithm. This survey work is presented in three parts. First subsection

describes research work that is mainly focused to minimize makespan and cost.

Second subsection describes research work that is mainly focused to reduce

energy consumption. Third subsection describes the research work which is

focused to minimize execution time and efficient resource utilization.

A. Makespan and Cost

Time period of job execution along with cost of execution are very

important performance parameters. Therefore, researchers concentrated

their efforts to reduce it.

Rizos Sakellarious et al. (10) introduced hybrid heuristic Directed Acyclic

Graph(DAG) algorithm to minimize cost. In this algorithm, one heuristic is used

for DAG scheduling, and another one for scheduling independent tasks. Both

heuristics improved performance behavior in the forms of Dynamic List

Scheduling, Heterogeneous Earliest Finish Time, Critical path on Processor,

Fastest Critical path and leveled Min Time.

Zhangjun Wu et al. (11) introduced Particle Swarm Optimization for Cloud

Workflow Scheduling algorithm to minimize makespan and optimize cost. In

this algorithm, the candidate solution is presented by the set of task-service

pairs, each particle not only learns from different exemplars, but also learns the

other feasible pairs for different dimensions.

This algorithm yields outstanding performance on scheduling workflow

applications in cloud environment.

Marek Wieczorek et al. (12) introduced Genetic, HEFT, and Myopic algorithm

to minimize makespan. This algorithm was compared using balanced and

unbalanced workflows on the basis of execution time. HEFT performs better as

compared with Myopic and Genetic.


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Z. Wu et al. (13)introduced market-oriented hierarchical scheduling strategy for

workflow scheduling to minimize makespan and optimize cost. Market-oriented

hierarchical scheduling strategy is divided into service-level scheduling and task

level scheduling. The service-level scheduling deals with the Task-to-Service

assignment and the task-level scheduling deals with the optimization of the

Task-to VM assignment in local cloud data centers.

Bogdan Simion et al. (14)introduced Improved Critical Path using Descendant

Prediction(ICPDP) to minimize makespan and optimize resource utilization.

This algorithm has quadratic polynomial time complexity. It finds the schedule

that results in makespan minimization and improve the utilization of resources.

Kianpisheh, S., Charkari et al. (15)introduced Ant Colony based constrained

workflow scheduling to minimize makespan and optimize cost. Probability of

Violation (POV) of constraint is proposed as a criterion for the schedule

robustness. Simulation result shows that it reduces the probability of violation

of workflow constraint, reducing makespan and cost.

Moschakisa et al. (16)introduced Bag-of-Task application on heterogeneous

interlinked clouds to minimize makespan and optimize cost. Simulated

annealing and thermodynamic simulated annealing is evaluated with virtual

machines of heterogeneous performance serving Bag-of-Tasks applications. A

state-of-the-art algorithm for Bag-of-Task scheduling and Fastest Processor

Largest Task was used for comparison.

B. Energy Management

To reduce the amount of energy consumed by node is very important.

Therefore, researchers concentrated their efforts to minimized energy

consumption.

Sonia Yassa et al. (17) introduced Multi-Objective Approach for Energy-Aware

Workflow Scheduling to minimize makespan, optimize cost and minimize

energy consumption. This algorithm is used to optimize the scheduling

performance. Method is based on the Dynamic Voltage and Frequency Scaling

(DVFS) technique to minimize energy consumption which allows processors to

operate in different voltage supply levels.

F. Coutinho et al. (18) introduced Workflow Scheduling Algorithm for

Optimizing Energy-Efficient Grid Resources usage to minimize energy

consumption.

This algorithm schedules the heavier task on maximum green resources.

Simulation results have proved that algorithm can significantly reduce the

power consumption in global grids.

Eugen Feller et al. (19) introduced Ant Colony Based Workload Placement in

Clouds, Grid Computing to minimize energy consumption. Novel dynamic

workload placement algorithm based on the Ant Colony Optimization (ACO) is


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introduced. The simulation results prove Ant Colony Optimization algorithm

provides superior energy gains than greedy algorithm.

Rajarathinam Jeyarani et al. (20) introduced Self Adaptive Particle Swarm

Optimization(SAPSO) to minimize energy consumption.

A novel approach for optimal placement of virtual machine in cloud is

proposed. The experimental results show that SAPSO was compared with

Multi-Strategy Ensemble Particle Swarm Optimization (MEPSO) outperforms

the power aware adaptive VM provisioning in a large scale, heterogeneous and

dynamic cloud environment.

Jiandun Li et al (21) introduced energy efficient workflow scheduling algorithm

to minimize energy consumption, optimize resource utilization and cost, and

minimize execution time.

An Energy Efficient Scheduling Approach was used to schedule workflow on

Private Clouds. The Simulation result shows that it can save more time for users

conserve more energy and achieve higher level of load balancing.

Jiachen Yang et al. (22) introduced a task scheduling algorithm considering

game theory to minimize energy consumption. Proposed a task scheduling

algorithm for energy management in cloud computing for big data by

considering game theory. The Simulation result shows that the proposed method

can perform better scheduling result over the task scheduling algorithm such as

Ant Colony Optimization, Genetic Algorithm, and Multi stage algorithm.

Mohamed Abu Sharkh et al. (23) introduced mathematical optimization mode

for workflow scheduling in cloud environment to minimize energy

consumption.

Problem of energy efficiency in a cloud data center is handled by a proposed

method. Smart VM Over Provision (SVOP), offers a major advantage to cloud

providers when live migration of VMs is not preferred.

C. Execution Time and Resource Utilization

To minimize the time taken to execute the given task and to maximum

utilization of computing resources are very important issues in load

balancing. Therefore, researchers concentrated their efforts to optimize it.

C. Lin et al. (24) introduced Scheduling Scientific Workflows in cloud

environment to optimize execution time. To schedule a workflow elastically on

a Cloud computing environment Scalable-Heterogeneous-Earliest-Finish-Time

algorithm (SHEFT) is proposed.

Experimental result shows that SHEFT outperforms several representative

workflow scheduling algorithms in optimizing workflow execution time.


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Soumi Ghosh et al. (25) introduced Priority Based Modified Throttled

Algorithm to minimize execution time, optimize cost and resource utilization. A

new enhanced algorithm is proposed and implemented in cloud computing

environment which adds a new feature like priority basis service of each

request.

Firstly determine the priorities of a request and allocate request to Virtual

Machines. A switching queue has proposed to hold the low priority requests due

to the arrival of high priority request. Simulation results show that response

time is improved in comparison with Throttled and Round Robin algorithm.

G.Suryadevi et al. (26) introduced Distributed Dynamic Load Balancing

Algorithm to minimize execution time and optimize cost and resource

utilization. For the scheduling of virtual machines on cloud environment

efficient distributed dynamic Load balancing Algorithm in Eucalyptus platform

has proposed.

All the incoming requests from the clients have been automatically redirected to

virtual machines based on priority and also the new virtual machines are created

and redirected when the requests are overloaded. Simulation results show that

allocating the resources on virtual machines based on priority achieves the

better response time and processing time.

Mainak Adhikari et al. (27) introduced workflow scheduling in cloud

environment to minimize execution time and optimize resource utilization and

cost.

To handle large number of application simultaneously, an Efficient Workflow

Scheduling Algorithm (EWSA) is proposed. The objective of the algorithm is to

estimate the execution time of all the tasks dynamically. The algorithm also

creates suitable VMs with minimum resources such that the entire application

can be executed within its deadline. Simulation results show that the proposed

algorithm maximize the resource utilization and execute the workflow within its

deadline.

Harmandeep Singh Brar et al. (28) introduced priority based load balancing

algorithm to minimize execution time. Proposed a priority based load balancing

algorithm mainly focuses on assigning tasks to virtual machines in such a way

that high priority tasks can be completed earlier which is done based on

execution length.

Simulation results show that it is good to use this algorithm for real time

applications as it shows a great improvement in execution time of the cloudlets.

Work done in various workflows scheduling algorithm is summarized in

TABLE 1 in terms of parameter used, reference, scheduling algorithm,

algorithm type and tools used to carry out experiment.


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Table 1: Workflow Scheduling Algorithms used for Load Balancing

Parameters Authors & Year Scheduling Algorithms Algorithm

Types

Tools Used

Makespan

and

Cost

Rizos Sakellarious

et al. 2004(10)

Hybrid Heuristic DAG

scheduling

Heuristics Real world workflow

Zhangjun Wu et al.

2010 (11)

Particle Swarm

Optimization for Cloud

Workflow Scheduling

Meta-

Heuristics

Amazon EC2

Marek Wieczorek

et al.

2005 (12)

Genetic,HEFT,Myopic

algorithm


Z. Wu et al.

2011 (13)

market-oriented

hierarchical scheduling

strategy

Meta-

Heuristics

SwinDeW-C

Bogdan Simion et

al

2007. (14)

Improved Critical Path

using Descendant

Prediction


Kianpisheh S. et

al.

2016 (15)

Ant Colony based

constrained workflow

scheduling

Meta-

Heuristics

Real world workflow

Moschakisa et al.

2015 (16)

Bag-of-Task application on

heterogeneous interlinked

clouds

Meta-

Heuristics

Scientific Workflow

Luiz Fernando

Bittencourt et al.

2011 (29)

Optimized Cost Algorithm Heuristics CloudSim

Energy

Management

Sonia Yassa et al.

2013 (17)

Multi-Objective Approach

for Energy-Aware

Workflow Scheduling

Meta-

Heuristics

Scientific Workflow

F. Coutinho et al.

2011 (18)

Workflow Scheduling

Algorithm for Optimizing

Energy-Efficient Grid

Resources usage

Heuristics GridSim

Eugen Feller et al.

2011 (19)

Ant Colony Based

Workload Placement in

Clouds, Grid Computing

Meta-

Heuristics

Java based toolkit

Rajarathinam

Jeyarani et al.

2011 (20)

Self-Adaptive Particle

Swarm Optimization

Meta-

Heuristics

CloudSIM

Jiandun Li et al.

2011 (21)

An Energy Efficient

Scheduling

Meta-

Heuristics

Real world workflow

Jiachen Yang et al.

2017 (22)

A task scheduling

algorithm considering

game theory


Mohamed Abu

Sharkh et al. 2017

(23)

mathematical optimization

mode

Meta-

Heuristics

Discrete simulator

Execution

Time

and

Resource

Utilization

C. Lin et al.

2011 (24)

Scheduling Scientific

Workflows

Heuristics CloudSim

Soumi Ghosh et al.

2016 (25)

Priority Based Modified

Throttled Algorithm

Heuristics CloudSim3.0

G.Suryadevi et al.

2014 (26)

Distributed Dynamic Load

Balancing Algorithm

Meta-

Heuristics

Eucalyptus

Mainak Adhikari

et al.

2016 (27)

Workflow scheduling Meta-

Heuristics

Montag,LIGO,CyberShake

Harmandeep Singh

Brar et al. 2014

(28)

Priority Based Load

balancing algorithm

Heuristics CloudSim


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TABLE 2 describes parameters considered by researchers in evaluating system

performance. It has been observed that only few of all the parameters were

considered at a time for evaluation.

Table 2: Parameters to Evaluate the Performance of Workflow Scheduling

Algorithm

Parameters

Scheduling

Algorithms

Makespan Budget

or

Cost

Energy

Management

Execution

Time

Resource

Utilization

Hybrid Heuristic DAG

scheduling (10)

Yes No No No No

Particle Swarm

Optimization for Cloud

Workflow Scheduling (11)

Yes Yes No No No

Genetic, HEFT, Myopic

algorithm (12)

Yes No No No No

Market-oriented hierarchical

scheduling strategy (13)

Yes Yes No No No

Improved Critical Path

using Descendant Prediction

(14)

Yes No No No Yes

Ant Colony based

constrained workflow

scheduling (15)

Yes Yes No No No

Bag-of-Task application on

heterogeneous interlinked

clouds [16]

Yes Yes No No No

Optimized Cost Algorithm

(29)

No Yes No Yes No

Multi-Objective Approach

for Energy-Aware

Workflow Scheduling (17)

Yes Yes Yes No No

Workflow Scheduling

Algorithm for Optimizing

Energy-Efficient Grid

Resources usage (18)

No No Yes No No

Ant Colony Based

Workload Placement in

Clouds, Computing(19)

No No Yes No No

Self-Adaptive Particle

Swarm Optimization (20)

No No Yes No No

An Energy Efficient

Scheduling (21)

No Yes Yes Yes Yes

A task scheduling algorithm

considering game theory

(22)

No No Yes No No

mathematical optimization

mode (23)

No No Yes No No

Scheduling Scientific

Workflows (24)

No No No Yes Yes

Priority Based Modified

Throttled Algorithm (25)

No Yes No Yes Yes

Distributed Dynamic Load

Balancing Algorithm (26)

No Yes No Yes Yes

Workflow scheduling (27) No Yes No Yes Yes

Priority Based Load

balancing algorithm (28)

No No No Yes No


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4. Conclusions

This paper analyzes various existing workflow scheduling algorithm with their

objectives to optimize solution. The present research is summarized based on

performance parameters. It was found that most algorithms perform scheduling

based on one or two parameter. A better scheduling algorithm can be developed

from the existing method by adding more number of metrics. This will help to

enhance the performance of system.

References

[1] Tharam Dillon, Chen Wu, Elizabeth Chang.Cloud Computing: Issues and Challenges..24th IEEE International Conference on Advanced Information Networking and Application, 2010.

[2] Kaur R, Luthra P. Load Balancing in Cloud Computing, International Conference on Recent Trends in Information. Telecommunication and Computing, ITC, 2014.

[3] R.R, Malladi. An approach to load balancing In cloud computing.: International Journal of Innov. Res. Sci. Engg. Technology, 2015, Vol. 4.

[4] Jadeja Y, Modi K.Cloud Computing - Concepts, Architecture and Challenges. International Conference on Computing, Electronics and Electrical Technologies [ICCEET], 2012.

[5] Singh, P., Baaga, P., Gupta, S.Assorted load-balancing algorithms in cloud computing: a survey. Int. J. Comput. Appl., 2016, Vol. 7.

[6] Goyal S, Verma M.K.Load balancing techniques in cloud computing environment: a review. Intnternational Journal of Advance Research Computer Science and Software Engg, 2016, Vol. 6.

[7] Einollah Jafarnejad Ghomi, Amir Masoud Rahmani, Nooruldeen Nasih Qade.Load balancing algorithms in cloud computing: A survey. Journal of Network and Computer Application, 2017, Vol. 88.

[8] Y. Du, X. Li.Application of Workflow Technology to Current Dispatching Order System. International Journal of Computer Science and Network Security, 2008, Vol. 8.

[9] N. Mohanapriya, Dr. G.Kousalya, Dr. P. Balakrishnan.Cloud Workflow Scheduling Algorithms:A Survey. International Journa of Advanced Engineering Technology, 2016, Vol. 7.

[10] Rizos Sakellariou, Henan Zhao. Hybrid Heuristic for DAG Scheduling on Heterogeneous Systems. Parallel and Distributed


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Processing Symposium, 18th IEEE International Conference, 2004.

[11] Z. Wu, Z. Ni, L. Gu.A Revised Discrete Particle Swarm Optimization for Cloud Workflow Scheduling. Computational Intelligence and Security (CIS), 2010, Vols. 184-188.

[12] Marek Wieczorek, Radu Prodan,Thomas Fahringer.Scheduling of Scientific Workflows in the ASKALON Grid Environment.. ACM SIGMOD, 2005, Vols. 34,Pg.56-62.

[13] Z. Wu, X. Liu, Z. Ni, D. Yuan, Y. Yang.A Market-Oriented Hierarchical Scheduling Strategy in Cloud Workflow Systems. Journal of Supercomput, 2011.

[14] Bogdan Simion, Catalin Leordeanu, Florin Pop, Valentin Cristea.A Hybrid Algorithm for Scheduling Workflow Applications in Grid Environments. Berlin Heidelberg : OTM Confederated International Conferences, Springer, Pg. 1331-1348,, 2007.

[15] Kianpisheh S, Charkari N.M, kargahi M.Ant Colony based constrained workflow scheduling for heterogeneous computing systems. 1053-1070, Clust.Comput, 2016, Vol. 19.

[16] Moschakisa I.A, Karatzaa H.D. Multi-criteria scheduling of Bag-of-Tasks applications on heterogeneous interlinked clouds with simulated annealing. Journal of Software System, 2015, Vol. 101.

[17] Sonia Yassa, Rachid Chelouah, Hubert Kadima, Bertrand Granado.Multi-Objective Approach for Energy-aware workflow scheduling in cloud computing environment. Article ID 350934, The Scientific World Journal, 2013, Vol. 10.

[18] F. Coutinho, L. A. V. Decarvalho, R. Santana.A Workflow Scheduling Algorithm for Optimizing Energy Efficient Grid Resources Usage, Dependable. Automic and Secure Computing, 9th IEEE International Conference, 2011.

[19] Eugen Feller, Louis Rilling, Christine Morin.Energy-Aware Ant Colony Based Workload Placement in Clouds, Grid Computing. 12th IEEE International Conference, Pg. 26-33, 2011.

[20] Rajarathinam Jeyarani, N. Nagaveni, Vasanth Ram.Self Adaptive Particle Swarm Optimization for Efficient Virtual Machine Provisioning in Cloud. International Journal of Intelligent Information Technologies, 2011, Vol. 7.

[21] Jiandun Li, Junjie Peng, Zhou Lei, Wu Zhang.An Energy Efficient Scheduling Approach Based on Private Clouds. Journal of Information and Computational Science, Pg. 716-724, 2011.


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[22] Jiachen Yang, Bin Jiang , Zhihan Lv , Kim-Kwang Raymond Choo.A task scheduling algorithm considering game theory designed for energy management in cloud computing. Journal of Future Generation Computer Systems, 2017.

[23] Mohamed Abu Sharkh, Abdallah Shami.An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Journal of Vehicular Communications, 2017, Vol. 9.

[24] C. Lin, S. Lu.Scheduling Scientific Workflows Elastically for Cloud Computing. IEEE 4th International Conference on Cloud Computing, pp.246-247, 2011.

[25] Soumi Ghosh, Chandan Banerjee.Priority Based Modified Throttled Algorithm in Cloud Computing. International Conference on Inventive Computation Technologies (ICICT), 2016.

[26] G.Suryadevi, D.Vijayakumar, R.Sabari Muthu Kumar, Dr. K .G. Srinivasagan. An Efficient Distributed Dynamic Load Balancing Algorithm for Private Cloud Environment.. International Journal of Innovative Research in Science, Engineering and Technology, 2014, Vol. 3.

[27] Mainak Adhikari, Tarachand Amgoth.Efficient Algorithm for Workflow Scheduling in Cloud Computing Environment. IEEE Ninth International Conference on Contemporary Computing (IC3), 2016.

[28] Harmandeep Singh Brar, Vivek Thapar. An Efficient Priority Based Load Balancing Algorithm for Cloud Environment.. International Journal of Computer Science Trends and Technology (IJCST), 2014, Vol.

[29] Luiz Fernando Bittencourt, Edmundo Roberto, Mauro Madeira.HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds. .International Journal of Internet Services and Applications, 2011, Vol. 2. pp. 207-227.


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