REVIEW PAPER on Scheduling in Cloud Computing

A Review on Scheduling Algorithms for Workflow Application in Cloud Computing

Author :Author : Co-Author : Co-Author :

JailalitaJailalita Dr. Maitreyee Dutta Dr. Maitreyee Dutta

RollNo- 132409RollNo- 132409 Professor & HOD Professor & HOD

ME-CSE(Regular)ME-CSE(Regular) Dept of Computer Science Dept of Computer Science NITTTR, ChdNITTTR, Chd NITTTR, Chd NITTTR, Chd

Introduction Characteristics of Cloud Computing Cloud Computing Deployment Model Cloud Computing Service Model Scheduling Literature Review Conclusion References

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Cloud computing is an emerging technology for enabling convenient, on-demand network access to a shared pool of configurable computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction[1]

Uses pay-per-use model



On-demand self-Service

* Cloud service provider provides huge services to the users on

their request [2] Broad Network Access

* Computing resources are delivered over the network (e.g

Internet)

* Used by various client applications with different platforms

(such as laptops and mobile phones) [2] Resource Pooling

* Cloud provider provide pool of resource that can be

dynamically assigned to multiple consumers [3]


Rapid Elasticity

* Cloud resources can be dynamically provisioned and released

automatically with user demand [2] Measured Service

* Cloud systems automatically control and manage the resources

depending on the needs of users [3]



Private Cloud

* Used by the IT industry’s to provide the more security of data and

application [3] Public Cloud

* Elasticity

* Reducing operation cost of IT Infrastructure [4] Community Cloud

* Infrastructure shared by several organizations Hybrid Cloud

* Combination of two or more deployment models [4]


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SaaS (Software as a Service)

* Application is hosted on the cloud as a service to the customers [3] PaaS (Platform as a Service)

* Provides and manages programming languages, libraries, services,

programming frameworks and inbuilt tools [4] IaaS (Infrastructure as a Service)

* Provide, manage and control the underlying infrastructure

including data storage, network resources and computing servers

[4]

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Maps and manages execution of inter-dependent tasks on distributed resources [5]

Types Independent Task Scheduling Workflow Scheduling

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Many users are competing for the shared resources on the cloud Scheduler has no control over the resources Workflow applications are either computation-intensive or data-

intensive. These applications required large data transferred between the multiple sites [5]

Different resources have different processing power

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Author Scheduling Parameters

Tools Findings

Xiao Li Fang et al.( 2014)[6]

• Makespan• Resource

Utilization

CloudSim Minimize makespan & implement load balancing

N. chopra and S. Singh(2013)[7]

• Deadline• Cost

WorkflowSim Complete workflow within deadline and reduce cost

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Tools Findings

T Amudha, T T Dhivyaprabha(2011) [8]

• Utilization rate

• Makespan• Priority

CloudSim Solve load balancing problem and reduce makespan as compare to WMTM, Min-Min

Yifei Zhang Yan-e Mao (2010) [9]

• Makespan GridSim Generate 14% less makespan thangeneric algorithms

Qi Cao et al. (2009) [10]

• Cost CloudSim Measure cost more accurate and performance of the activities

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Tools Findings

Mustafizur Rahman, RajKumar Buyya(2007)[11]

• Priority• Makespan

GridSim Generate better schedule and perform better than HEFT, Min-Min & Max-Min

Sakellariou Rizos, et al.(2004)[12]

• Priority• Time

CloudSim Perform better than Min-Min and Max-Min

He Xiao Shan, et al. (2003) [13]

• Makespan• Bandwidth

Grid Environment

Outperform than traditional Min-Min

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Scheduling is mapping of the tasks submitted by user to the available and efficient resources as per the service level agreement

In cloud computing, scheduling of tasks and resources are the biggest problem

In this review paper, we analyzed different scheduling algorithm considers different scheduling parameters like cost, makespan, priority of tasks, load balancing and resource utilization rate

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[1] Zhang Qi, Lu Cheng and Raouf Boutaba, “Cloud computing: state-of-the-art and research Challenges,” Journal of Internet Services and Applications, Vol.1, Issue No.1, pp.7-18, 2010.

[2] Peeyush Mathur and Nikhil Nishchal , “ Cloud Computing: New Challenge to the entire computer Industry,” International Conference on Parallel, Distributed and Grid Compuitng, pp.223- 228,2010.

[3] Bhaskar Prasad Rimal, Eunmi Choi, “A taxonomy and survey of cloud computing systems,” International Joint Conference on INC, IMS and IDC, pp.44-51, 2009.


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[4] Yashpalsinh Jadej, Kriti Modi, “Cloud Computing –Concepts, Architecture and Challenges ,”International Conference on Computing, Electronics and Electrical Technologies, pp. 887-890, 2012.[5] Bittencourt, Luiz Femando and Edmundo Roberto Mauro Madeira, “HCOC: a cost optimization algorithm for workflow scheduling in hybrid clouds,” Journal of Internet Services and Applications, Vol. 2, Issue No. 3, pp. 207-227, 2011.

[6] Xiao Fang Li, Yingchi Mao, Xianjian Xiao and Yanbin Zhuang, “An Improved Max-Min Task-Scheduling Algorithm for Elastic Cloud,” International Symposium on Computer, pp.340-343, 2014.

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[7]Nitish Chopra, Sarbjeet Singh, “HEFT based Workflow Scheduling Algorithm for Cost Optimization within Deadline in Hybrid Clouds,” International Conference on Computing Communications and Networking Technologies, pp.1-6, 2013.

[8] T Amudha, T T Dhivyaprabha, “QoS Priority Based Scheduling and Proposed Framework for Task Scheduling in a Grid environment,” International Conference on Recent Trends in Information Technology, pp.650-655, 2011.

[9]Yifei Zhang,Yan-e Mao, “A SCP BASED Critical Path Scheduling Strategy for Data-Intensive Workflows,” International Conference on Fuzzy Systems and Knowledge Discovery, pp.1735-1739, 2010.

[10]Qi Cao, Zhi Bo Wei and Wen Mao Gong, “An optimized

Algorithm for Task Scheduling Based on Activity Based Costing in Cloud Computing,” International Conference on Bioinformatics and Biomedical Engineering, pp.1-3, 2009.

[11] Mustafizur Rahman, Srikumar Venugopal, Rajkumar Buyya,“A Dynamic Critical Path Algorithm for Scheduling Scientific Workflow Applications on Cloud Grids,” International Conference on e-Science and Grid Computing, pp.35-42, 2007.

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[12] Sakellariou, Rizos, and Henan Zhao, “A hybrid heuristic for DAG scheduling on heterogeneous systems,” Parallel and Distributed Processing Symposium, pp.111-116, 2004.

[13] He XiaoShan, Sun XianH and Gregor von Laszewski, “QoS Guided Min-Min Heuristic for Grid Task Scheduling ” Journal of Computer Science and Technology, Vol.18, Issue No.4, pp.442-451, 2003.

[14] S.Devipriya and C.Ramesh, “Improved Max_Min Heuristic Model for Task Scheduling in Cloud, ”International Conference on Green Computing, Communication and Conservation of Energy, pp.883-

888, 2013.04/13/15 NITTTR, CHD 24

[15] Zhcheng Cai, Xiaoping Li, Jatinder N.D. Gupta, “Critical Path- Based Iterative Heuristic for Workflow Scheduling in Utility and Cloud Computing,” International Conference on Service Oriented Computing, pp.207-221, 2013.

[16] Juan J. Durillo, Hamid Mohammadi Fard, Radu Prodan, “

MOHEFT: A Multi-Objective List-based Method for Workflow

Scheduling,” International Conference on Cloud Computing

Technology and Science, pp.185-192, 2012.

.

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