Wei Liu; Feiyan Shi; Wei Du; Hongfeng Li

A Cost-Aware Resource Selection for Dataintensive Applications in Cloud-oriented Data Centers

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Author(s)

Wei Liu ^1,3,4,* Feiyan Shi ¹ Wei Du ^1,2 Hongfeng Li ¹

1. College of Computer Science & Technology, Wuhan University of Technology, Wuhan 430063, China

2. School of Computer Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China

3. State Key Lab of Software Engineering, Wuhan University, Wuhan 430072, China

4. State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, P.R. China

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2011.01.02

Received: 24 Jun. 2010 / Revised: 25 Sep. 2010 / Accepted: 2 Dec. 2010 / Published: 8 Feb. 2011

Index Terms

Resource selection, cost-aware, WSCP, cloud computing

Abstract

As a kind of large-scale user-oriented dataintensive computing, cloud computing allows users to utilize on-demand computation, storage, data and services from around the world in a pay-as-you-go model. In cloud environment, applications need access to mass datasets that may each be replicated on different resources (or data centers). Mass data moving influences the execution efficiency of application to a large extent, while the economic cost of each replica itself can never be overlooked in such a model of business computing. Based on the above two considerations, how to select appropriate data centers for accessing replicas and creating a virtual machine(VM for short) to execute applications to make execution efficiency high and access cost low as far as possible simultaneously is a challenging and urgent problem. In this paper, a cost-aware resource selection model based on Weighted Set Covering Problem (WSCP) is proposed, according to the principle of spatial locality of data access. For the model, we apply a Weighted Greedy heuristic to produce an approximately optimal resource set for each task. Finally, verifies the validity of the model in simulation environment, and evaluate the performance of the algorithm presented. The result shows that WSCP-based heuristic can produce an approximately optimal solution in most cases to meet both execution efficiency and economic demands simultaneously, compared to other two strategies.

Cite This Paper

Wei Liu, Feiyan Shi, Wei Du, Hongfeng Li, "A Cost-Aware Resource Selection for Dataintensive Applications in Cloud-oriented Data Centers", International Journal of Information Technology and Computer Science(IJITCS), vol.3, no.1, pp.10-17, 2011. DOI: 10.5815/ijitcs.2011.01.02

Reference

[1] I. Foster, Y Zhao, I. Raicu, and S. Lu, “Cloud Computing and Grid Computing 360-degreecompared[C]”, in Grid Computing Environments Workshop, 2008, pp. 1-10.

[2] Daniel Nurmi, Rich Wolski, Chris Grzegorczyk, Graziano Obertelli, Sunil Soman, Lamia Youseff, Dmitrii Zagorodnov, “The Eucalyptus Open-source Cloudcomputing System”, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, CCGRID 2009, pp: 124-131.

[3] Michael Armbrust, Armando Fox, Rean Griffith, Anthony D. Joseph, Randy H. Katz, Andrew Konwinski, Gunho Lee, David A. Patterson, Ariel Rabkin, Ion Stoica, Matei Zaharia, “Above the Clouds: A Berkeley View of Cloud Computing”, Technical Report No. UCB/EECS-2009-28, 2009.

[4] Michael Miller, Cloud Computing, BeiJing, Publitions House of Electronic Industry, 2009, pp:35-40.

[5] Markus Winter, “Data Center Consolidation: A Step towards Infrastructure Clouds”, CloudCom 2009, LNCS 5931, 2009, pp: 190–199.

[6] Milan Milenkovic, Enrique Castro-Leon, James R. Blakley, “Power-Aware Management in Cloud Data Centers”, CloudCom 2009, LNCS 5931, pp:668–673.

[7] Ying Song, Hui Wang, Yaqiong Li, Binquan Feng, Yuzhong Sun, “Multi-Tiered On-Demand Resources Scheduling for VM-Based Data Center”, 9th IEEE/ACM International Symposium on Cluster Computing and the Grid, 2009,pp:147-155.

[8] Srikumar Venugopal, Rajkumar Buyya, “An SCP-based heuristic approach for scheduling distributed dataintensive applications on global grids”, Journal of Parallel and Distributed Computing, Vol. 68, No. 4, 2008, pp:471-487.

[9] Srikumar Venugopal, Scheduling Distributed Data-Intensive Applications on Global Grids, The University of Melbourne, Australia 2006.

[10] Lilian Noronha Nassif, José Marcos Nogueira, Flávio Vinícius de Andrade, “Distributed Resource Selection in Grid Using Decision Theory”, in Seventh IEEE International Symposium on Cluster Computing and the Grid(CCGrid'07).

[11] Tyng-Yeu Liang Siou-Ying Wang I-Han Wu, “Using Frequent Workload Patterns in Resource Selection for Grid Jobs”, DOI 10.1109/APSCC.2008.217.

[12] D.G. Cameron, R. Carvajal-Schiaffino, A.P. Millar, C. Nicholson, K.Stockinger, F. Zini, Evaluating scheduling and replica optimisation strategies in OptorSim, in: Proceedings of the Fourth International Workshop on Grid Computing (Grid2003), IEEE CS Press, Los Alamitos,CA, USA, Phoenix, AZ, USA, 2003.

[13] William H. Bell1and David G. Cameron1, “Evaluation of an Economy-Based File Replication Strategy for a Data Grid”, in Proceedings of the 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'03).

[14] Rui-bing Chen, Wen-qi Huang, “A Heuristic Algorithm for Set Covering Problem”, in Computer Sicience, 2007Vol134 ¹14.

[15] Fubao Feng, “The research on Set Covering Problem”, ShanDong University, 2006.

[16] ZHANG Yong, ZHU Hong, “Approximation Algorithms for the Problems of Weak Set Cover”, in CHINESE OF COMPUTERS, 2005, 28(9), pp: 1497-1500.

[17] H. Casanova, “Simgrid: a toolkit for the simulation of application scheduling”, in: Proceedings of the First International Symposium on Cluster Computing and the Grid (CCGRID ’01), IEEE CS Press, Los Alamitos, CA, USA, Brisbane, Australia, 2001.

[18] M. Maheswaran, S. Ali, H.J. Siegel, D. Hensgen, R.F. Freund, “Dynamic mapping of a class of independent tasks onto heterogeneous computing systems”, J. Parallel Distributed Comput. 59(1999) 107–131.

[19] Rajkumar Buyya, Rajiv Ranjan, Rodrigo N. Calheiros, “Modeling and Simulation of Scalable Cloud Computing Environments and the CloudSim Toolkit: Challenges and Opportunities”, in The 2009 International Conference on High Performance Computing and Simulation, HPCS 2009, pp:1-11.

[20] The CloudSim Project, http://www.gridbus.org/cloudsim/.

[21] Rajkumar Buyya, Chee Shin Yeo, Srikumar Venugopal, “Market-Oriented Cloud Computing:Vision, Hype, and Reality for Delivering IT Services as Computing Utilities”, J. Parallel Distrib. Comput. 68 (2008) pp:471 – 487.

[22] Juefu Liu, Peng Liu, “Status and Key Techniques in Cloud Computing”, in Proceedings of 2010 3rd International Conference on Advanced Computer Theory and Engineering(ICACTE), pp: V4-285–V4-288.

[23] Sulistio, A., Poduvaly, G., Buyya, R., and Tham, “Constructing a Grid Simulation with Differentiated Network Service Using GridSim”, in Proceedings of the 6th International Conference on Internet Computing (2005) pp: 437–444

International Journal of Information Technology and Computer Science (IJITCS)