A scheduling algorithm with dynamic properties in mobile grid

JongHyuk LEE, SungJin CHOI, JoonMin GIL, Taeweon SUH, HeonChang YU

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Front. Comput. Sci. ›› 2014, Vol. 8 ›› Issue (5) : 847-857. DOI: 10.1007/s11704-014-3223-6
RESEARCH ARTICLE

A scheduling algorithm with dynamic properties in mobile grid

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Abstract

Mobile grid is a branch of grid computing that incorporates mobile devices into the grid infrastructure. It poses new challenges because mobile devices are typically resource-constrained and exhibit unique characteristics such as instability in network connections. New scheduling strategies are imperative in mobile grid to efficiently utilize the devices. This paper presents a scheduling algorithm that considers dynamic properties of mobile devices such as availability, reliability, maintainability, and usage pattern in mobile grid environments. In particular, usage patterns caused by voluntarily or involuntarily losing a connection, such as switching off the device or a network interruption could be important criteria for choosing the best resource to execute a job. The experimental results show that our scheduling algorithm provides superior performance in terms of execution time, as compared to the other methods that do not consider usage pattern. Throughout the experiments, we found it essential to consider usage pattern for improving performance in the mobile grid.

Keywords

mobile grid / scheduling / dynamic properties / availability / reliability / maintainability / usage pattern

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JongHyuk LEE, SungJin CHOI, JoonMin GIL, Taeweon SUH, HeonChang YU. A scheduling algorithm with dynamic properties in mobile grid. Front. Comput. Sci., 2014, 8(5): 847‒857 https://doi.org/10.1007/s11704-014-3223-6

References

[1]
Foster I, Kesselman C. The Grid 2: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, 2004
[2]
Muthuvelu N, Chai I, Chikkannan E, Buyya R. Batch resizing policies and techniques for fine-grain grid tasks: the nuts and bolts. The Journal of Information Processing Systems, 2011, 7(2): 299-320
CrossRef Google scholar
[3]
Kurdi H, Li M, Al-Raweshidy H. A classification of emerging and traditional grid systems. IEEE Distributed Systems Online, 2008, 9(3). Article No. 0001
CrossRef Google scholar
[4]
Lee J, Song S, Gil J, Chung K, Suh T, Yu H. Balanced scheduling algorithm considering availability in mobile grid. In: Proceedings of the 4th Intern<?Pub Caret?>ational Conferemce on Advances in Grid and Pervasive Computing. 2009, 211-222
CrossRef Google scholar
[5]
Park S M, Ko Y B, Kim J H. Disconnected operation service in mobile grid computing. In: Proceedings of the International Conference on Service Oriented Computing. 2003, 499-513,
[6]
Balazinska M, Castro P. Characterizing mobility and network usage in a corporate wireless local-area network. In: Proceedings of the 1st International Conference on Mobile Systems, Applications, and Services. 2003, 303-316
CrossRef Google scholar
[7]
Casanova H, Legrand A, Quinson M. SimGrid: a generic framework for large-scale distributed experiments. In: Proceedings of the 10th IEEE International Conference on Computer Modeling and Simulation. 2008, 126-131
[8]
Yeo J, Kotz D, Henderson T. A community resource for archiving wireless data at dartmouth. ACM SIGGOMM Computer Communication Review, 2006, 36(2): 21-22
CrossRef Google scholar
[9]
Rodrigues J M, Zunino A, Campo M. Introducing mobile devices into grid systems: a survey. International Journal ofWeb and Grid Services, 2011, 7(1): 1-40
[10]
Huang C Q, Zhu Z T, Wu Y H, Xia Z H. Power-aware hi-erarchical scheduling with respect to resource intermittence in wireless grids. In: Proceedings of the 5th International Conference on Machine Learning and Cybernetics. 2006, 693-698
[11]
Li C, Li L. Collaboration among mobile agents for efficient energy allocation in mobile grid. Information Systems Frontiers, 2012, 14(3): 711-723
CrossRef Google scholar
[12]
Lee J, Choi S, Suh T, Yu H, Gil J. Group-based scheduling algorithm for fault tolerance in mobile grid. Communications in Computer and Information Science, 2010, 78: 394-403
CrossRef Google scholar
[13]
Farooq U, Khalil W. A generic mobility model for resource prediction in mobile grids. In: Proceedings of the International Symposium on Collaborative Technologies and Systems. 2006, 189-193
CrossRef Google scholar
[14]
Ghosh P, Roy N, Das S K. Mobility-aware efficient job scheduling in mobile grids. In: Proceedings of the 7th IEEE International Symposium on Cluster Computing and the Grid. 2007, 701-706
[15]
Xu Y Q, Yin M. A mobility-aware task scheduling model in mobile grid. Applied Mechanics and Materials, 2013, 336-338: 1786-1791
CrossRef Google scholar
[16]
Jiang Q, Wu X, Yang H. Task scheduling based on genetic algorithm in mobile grid. In: Proceedings of the Computer Science & Service System. 2012, 719-722
[17]
Litke A, Skoutas D, Tserpes K, Varvarigou T. Efficient task replication and management for adaptive fault tolerance in mobile grid environments. Future Generation Computer Systems, 2007, 23(2): 163-178
CrossRef Google scholar

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg
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