Skip to main content
SpringerLink
Log in
Book cover

International Conference on Internet of Things and Connected Technologies

ICIoTCT 2019: 4th International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2019 pp 54–68Cite as

Task Allocation in Distributed Real Time Database Systems in IoT

  • Conference paper
  • First Online:

  • 699 Accesses

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1122))

Abstract

In present scenario, distributed and parallel systems in the form of grid, cloud and even cloud based Internet of things (IoT) are cater the needs of demand for computing capacity. Internet of Things (IoT) is a new come up to connect objects/things and therefore transmit information between a variety of entities of the corporeal world or to the control centers where interpret this information. By use of available resources are play very crucial role to ensure systems schedule. In distributed (Real time) database system, data allocation is one of the major problems. It affects the efficiency of the access to the requested data and thereby has large impact on the performance of the whole system. The data allocation involves data splitting, fragment replication, allocation choice to name a few issues. The distributed database system design putting all these factors together into consideration is complex and a Non-deterministic Polynomial (NP) hard. By applying Genetic Algorithm (GA), this work presents a virtual machine (VM) scheduling model to address the job allocation problem aiming to minimize the turnaround time. GA helps to attain a reasonable time for the query execution. The results of experiments have been examined to appraise the efficiency of our approach by comparing with best fit VM scheduling approach.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Choudhary, S.R., Jha, C.K.: Performance evaluation of real time database systems in distributed environment. Int. J. Comput. Technol. Appl. 4(5), 785–792 (2013)

    Google Scholar 

  2. Singh, K.V., Raza, Z.: A GA based job scheduling strategy for computational grid. In: International Conference on Advances in Computer Engineering and Applications (ICACEA), pp. 29–34, IMS Engineering College, Ghaziabad, India, March 2015

    Google Scholar 

  3. Kumar, D., Raza, Z.: A PSO based VM resource scheduling model for cloud computing. In: IEEE International Conference on Computational Intelligence & Communication Technology, pp. 213–219 (2015)

    Google Scholar 

  4. Abraham, A., Carretero, J., Xhafa, F.: Genetic algorithm based schedulers for grid computing systems. Int. J. Innov. Comput. Inf. Control 3(5), 1053–1071 (2007)

    Google Scholar 

  5. Abraham, A., Buyya, R., Nath, B.: Nature’s heuristics for scheduling jobs on computational grids. In: The 8th IEEE International Conference on Advanced Computing and Communications (ADCOM 2000), India (2000)

    Google Scholar 

  6. Baruah, A.: A GA approach to static task scheduling in grid based systems. Int. J. Comput. Sci. Eng. (IJCSE) 4(01), 54 (2012)

    Google Scholar 

  7. Ramachandram, A.J.S., Al Jadaan, O., Abdulal, W.: An improved rank-based genetic algorithm with limited iterations for grid scheduling. In: 2009 IEEE Symposium on Industrial Electronics and Applications (ISIEA2009), pp. 215–220, Malaysia, Kuala Lumpur, October 2009

    Google Scholar 

  8. Yarkhan, A., Dongarra, J.: Experiments with scheduling using simulated annealing in a grid environment. In: 3rd International Workshop on Grid Computing (GRID2002), pp. 232–242 (2002)

    Chapter  Google Scholar 

  9. Raza, Z., Vidyarthi, D.P.: GA based scheduling model for computational grid to minimize turnaround time. Int. J. Grid High Perform. Comput. I(IV), 70–90 (2009)

    Article  Google Scholar 

  10. Ma, P.-Y.R., Lee, E.Y.S., Tsuchiya, M.: A task allocation model for distributed computing systems. IEEE Trans. Comput. C-31(1), January 1982

    Google Scholar 

  11. Shen, C.-C., Tsai, W.-H.: A graph matching approach to optimal task assignment in distributed computing systems using a minimax criteria. IEEE Trans. Comput. C-34(3), 197–203 (1985)

    Article  Google Scholar 

  12. Yu, D.J., Buyya, R.: Workflow scheduling algorithms for grid computing. Technical report, GRIDSTR-2007-10, Grid Computing and Distributed Systems Laboratory, The University of Melbourne, Australia (2007)

    Google Scholar 

  13. Vidyarthi, D.P., Tripathi, A.K., Sarkar, B.K.: Cluster based task allocation in distributed systems. In: Proceedings of 18th International Parallel and Distributed Processing Symposium. IEEE (2004)

    Google Scholar 

  14. Vidyarthi, D.P., Tripathi, A.K., Sarkar, B.K.: Multiple task management in distributed computing system. J. CSI 31(1), 19–25 (2001)

    Google Scholar 

  15. Cornell, D.W., Yu, P.S.: On optimal site assignment for relations in the distributed database environment. IEEE Trans. Softw. Eng. 5(8), 1004–1009 (1989)

    Article  Google Scholar 

  16. Falzon, G., Li, M.: Enhancing genetic algorithms for dependent job scheduling in grid computing environments. J. Supercomput. 62(1), 290–314 (2012). Springer

    Article  Google Scholar 

  17. Ritchie, G., Levine, J.: A fast, effective local search for scheduling independent jobs in heterogeneous computing environments. Technical report, Centre for Intelligent Systems and their Applications, School of Informatics, University of Edinburgh (2003)

    Google Scholar 

  18. Iordache, G.V., Boboila, M.S., Pop, F., Stratan, C., Cristea, V.: A decentralized strategy for genetic scheduling in heterogeneous environments. Multiagent Grid Syst. 3(4), 355–367 (2007)

    Article  Google Scholar 

  19. Topcuoglu, H., Hariri, S., Wu, M.Y.: Performance-effective and lowcomplexity task scheduling for heterogeneous computing. IEEE Trans. Parallel Distrib. Syst. 13(3), 260–274 (2002)

    Article  Google Scholar 

  20. Foster, I., Kesselman, C.: The grid - blueprint for a new computing infrastructure. Morgan Kaufmann Publishers (1998)

    Google Scholar 

  21. Foster, I.: What is the Grid? A three point checklist (2002)

    Google Scholar 

  22. Ahmad, I., Dhodhi, M.K., Ghafoor, A.: Task Assignment in Distributed Computing Systems, pp. 49–53. IEEE (1995)

    Google Scholar 

  23. Carretero, J., Xhafa, F.: Using genetic algorithms for scheduling jobs in large scale grid applications. J. Technol. Econ. Dev. Res. J. Vilnius Gediminas Technical University 12(1), 11–17 (2006)

    Google Scholar 

  24. Gonçalves, J.F., de M. Mendes, J.J., Resende, M.G.C.: A Hybrid Genetic Algorithm for the Job Shop Scheduling Problem, AT&T Labs Research Technical Report TD-5EAL6J, September 2002

    Google Scholar 

  25. Yu, J., Buyya, R.: Scheduling scientific workflow applications with deadline and budget constraints using genetic algorithms. Sci Program 14, 217–230 (2006)

    Google Scholar 

  26. Yu, J., Buyya, R.: A budget constrained scheduling of workflow applications on utility grids using genetic algorithms. In: Proceedings of the 15th IEEE International Symposium on High Performance Distributed Computing. IEEE CS Press, Paris (2006)

    Google Scholar 

  27. Liu, L., Xi, Y.: A hybrid genetic algorithm for job shop scheduling problem to minimize makespan. In: Proceedings of the Sixth World Congress on Intelligent Control and Automation, pp. 3709–3713 (2006)

    Google Scholar 

  28. Dowdy, L.W., Foster, D.V.: Comparative model of the file assignment problem. ACM Comput. Surv. 2, 287–314 (1982)

    Article  Google Scholar 

  29. Wang, L., Siegel, H.J., Chowdhury, V.R., Maciejewski, A.: Task matching and scheduling in heterogeneous computing environments using a genetic-algorithm-based approach. J. Parallel Distrib. Comput. 47(1), 8–22 (1997)

    Article  Google Scholar 

  30. Mililotti, M., Martino, V.D.: Scheduling in a Grid computing environment using Genetic Algorithms, 0-7695-1573-8/02/ (C) IEEE (2002)

    Google Scholar 

  31. Garey, M.R. Johnson, D.S.: Computers and Intractability - A Guide to the Theory of NP Completeness. W.H. Freeman and Co. (1979)

    Google Scholar 

  32. Raj, J.S., Thomas, R.M.: Genetic based scheduling in grid systems: a survey. In: Computer Communication and Informatics (ICCCI), International Conference on IEEE (2013)

    Google Scholar 

  33. March, S.T., Rho, S.: Allocating data and operations to nodes in DDB design. IEEE Trans. Knowl. Data Eng. 7(2), 305–317 (1995)

    Article  Google Scholar 

  34. Zhou, W., Bu, Y.P.: An adaptive genetic algorithm for the grid scheduling problem. In: Control and Decision Conference (CCDC), pp. 730–734 (2012)

    Google Scholar 

  35. Chu, W.W., Lan, L.M.-T.: Task allocation and precedence relations for distributed real time systems. IEEE Trans. Comput. 36(6), 667–679 (1987)

    Article  Google Scholar 

  36. Lee, Y.H., Leu, S., Chang, R.S.: Improving job scheduling algorithms in a grid environment. Future Gen. Comput. Syst. 27(8), 991–998 (2011)

    Article  Google Scholar 

  37. Hwang, K., Dongarra, J., Fox, G.: Distributed and Cloud Computing: From Parallel Processing to the Internet of Things. Elsevier Pvt. Ltd, Singapore (2012). ISBN 978-0-12-385880-1

    Google Scholar 

  38. Karimi, K., Atkinson, G.: What the Internet of Things (IoT) Needs to Become a Reality. http://www.eetimes.com/document.asp?doc_id=1280077

  39. Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of Things (IoT): a vision, architectural elements, and future directions. Fut. Gen. Comput. Syst. 29(7), 1645–1660 (2013)

    Article  Google Scholar 

  40. Atzori, L., Iera, A., Morabito, G.: The Internet of Things: a survey. Int. J. Comput. Telecommun. Netw. 54(15), 2787–2805 (2010)

    Article  Google Scholar 

  41. Zanella, A., Bui, N., Vangelista, L., Zorzi, M.: Internet of Things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)

    Article  Google Scholar 

  42. Choudhary, S.R., Jha, C.K.: Task (Transaction) allocation in distributed real time database systems in cloud computing. Int. J. Trend Res. Dev. (IJTRD) 05(01), 160–167 (2018)

    Google Scholar 

  43. Kumar, S., Raza, Z.: Internet of Things: possibilities and challenges. Int. J. Syst. Service-Oriented Eng. 7(3), 32–52 (2017)

    Article  Google Scholar 

  44. Petrolo, R., Loscri, V., Mitton, N.: Cyber-physical objects as key elements for a smart cyber-city. In: Management of Cyber Physical Objects in the Future Internet of Things, pp. 31–49. Springer International Publishing (2016)

    Google Scholar 

Download references

Acknowledgement

The authors would like to acknowledge the suggestions and contributions made by Dr. Zahid Raza with numerous discussions held during the course of this work.

Author information

Authors and Affiliations

  1. Bioinformatics Centre, Banasthali Vidyapith, P.O. Banasthali Vidyapith, Banasthali, 304 022, Rajasthan, India

    Shetan Ram Choudhary

  2. Department of Computer Science, Banasthali Vidyapith, P.O. Banasthali Vidyapith, Vanasthali, 304 022, Rajasthan, India

    C. K. Jha

Authors
  1. Shetan Ram Choudhary
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. C. K. Jha
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Shetan Ram Choudhary or C. K. Jha .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Neeta Nain

  2. Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, India

    Santosh Kumar Vipparthi

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Choudhary, S.R., Jha, C.K. (2020). Task Allocation in Distributed Real Time Database Systems in IoT. In: Nain, N., Vipparthi, S. (eds) 4th International Conference on Internet of Things and Connected Technologies (ICIoTCT), 2019. ICIoTCT 2019. Advances in Intelligent Systems and Computing, vol 1122. Springer, Cham. https://doi.org/10.1007/978-3-030-39875-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-39875-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39874-3

  • Online ISBN: 978-3-030-39875-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation