Open Access   Article Go Back

Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud

Jaspreet Singh1 , Kamaljit Kaur2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 538-547, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.538547

Online published on May 31, 2019

Copyright © Jaspreet Singh, Kamaljit Kaur . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Jaspreet Singh, Kamaljit Kaur, “Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.538-547, 2019.

MLA Style Citation: Jaspreet Singh, Kamaljit Kaur "Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud." International Journal of Computer Sciences and Engineering 7.5 (2019): 538-547.

APA Style Citation: Jaspreet Singh, Kamaljit Kaur, (2019). Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud. International Journal of Computer Sciences and Engineering, 7(5), 538-547.

BibTex Style Citation:
@article{Singh_2019,
author = {Jaspreet Singh, Kamaljit Kaur},
title = {Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {538-547},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4277},
doi = {https://doi.org/10.26438/ijcse/v7i5.538547}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.538547}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4277
TI - Dynamic Fault Tolerance Job Allocation Mechanism to Conserve Resources in Vehicular Cloud
T2 - International Journal of Computer Sciences and Engineering
AU - Jaspreet Singh, Kamaljit Kaur
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 538-547
IS - 5
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
398 242 downloads 97 downloads
  
  
           

Abstract

Today sensing resources are widely increased in terms of vehicles and it affects the cloud computing systems. This technology is used for predicting traffic and for road safety. These systems usually share resources and collaborate with sensing devices for processing data and propagate results. In this paper we proposed Vehicular cloud based fault tolerance mechanism that considers cost matrix and dynamic fault tolerance. The allocation of resources depends critically on the cost associated with virtual machine. It considers exponential residency of VC and execution time along with bandwidth utilization. Bandwidth consumption and cost of execution is reduced greatly by the effect of proposed mechanism.

Key-Words / Index Term

Vehicular Cloud, Cloud computing, Fault tolerance, Resource scheduling

References

[1] R. K. Naha, S. Garg, D. Georgakopoulos, P. P. Jayaraman, L. Gao, Y. Xiang, and R. Ranjan, “Fog computing: Survey of trends, architectures, requirements, and research directions,” IEEE Access, vol. 6, no. c, pp. 47980–48009, 2018.
[2] B. Brik, N. Lagraa, N. Tamani, and A. Lakas, “Renting out Cloud Services in Mobile Vehicular,” Res. Gate, no. December, 2018.
[3] M. R. Jabbarpour, A. Marefat, A. Jalooli, and H. Zarrabi, “Correction to : Cloud-based vehicular networks : a taxonomy , survey , and conceptual hybrid architecture Could-based vehicular networks : a taxonomy , survey , and conceptual hybrid architecture,” Wirel. Networks, no. November, 2017.
[4] R. Yu, X. Huang, J. Kang, J. Ding, S. Maharjan, S. Gjessing, and Y. Zhang, “Cooperative resource management in cloud-enabled vehicular networks,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7938–7951, 2015.
[5] T. Mori, Y. Utsunomiya, X. Tian, and T. Okuda, “Queueing theoretic approach to job assignment strategy considering various inter-Arrival of job in fog computing,” 19th Asia-Pacific Netw. Oper. Manag. Symp. Manag. a World Things, APNOMS 2017, pp. 151–156, 2017.
[6] K. Zheng, H. Meng, P. Chatzimisios, L. Lei, and X. Shen, “An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems,” IEEE Trans. Ind. Electron., vol. 62, no. 12, pp. 7920–7928, 2015.
[7] K. Zhang, Y. Mao, S. Leng, Q. Zhao, L. Li, and X. Peng, “Energy-efficient Offloading for Mobile Edge Computing in 5G Heterogeneous Networks,” IEEE Access, vol. 3536, no. c, pp. 1–10, 2016.
[8] W. Zhang, Z. Zhang, and H. Chao, “Cooperative Fog Computing for Dealing with Big Data in the Internet of Vehicles : Architecture and Hierarchical Resource Management,” IEEE Access, no. December, pp. 60–67, 2017.
[9] J. Fan, R. Li, and X. Zhang, “Research on fault tolerance strategy based on two level checkpoint server in autonomous vehicular cloud,” Proc. 2017 IEEE 7th Int. Conf. Electron. Inf. Emerg. Commun. ICEIEC 2017, no. 61363079, pp. 381–384, 2017.
[10] Y. Sharma, B. Javadi, W. Si, and D. Sun, “Reliability and energy efficiency in cloud computing systems: Survey and taxonomy,” J. Netw. Comput. Appl., vol. 74, pp. 66–85, 2016.
[11] H. S. Y. Lin, “EAFR: An Energy-Efficient Adaptive File Replication System in Data-Intensive Clusters,” IEEE Trans. Parallel Distrib. Syst., pp. 1017–1030, 2017.
[12] B. Shrimali and H. Patel, “Performance Based Energy Efficient Techniques For VM Allocation In Cloud Environment,” IEEE Access, pp. 477–486, 2017.
[13] H. . Z. D. . Zhao B.a Aydin, “Reliability-Aware dynamic voltage scaling for energy-constrained real-time embedded systems,” 26th IEEE Int. Conf. Comput. Des. 2008, ICCD, vol. 546244, pp. 633–639, 2008.
[14] H. M.-R. Mahdi Ghamkhari, “Energy and Performance Management of Green Data Centers: A Profit Maximization Approach,” IEEE Trans. Smart Grid, pp. 1017–1025, 2017.
[15] M. Salehi, M. K. Tavana, S. Rehman, S. Member, M. Shafique, and A. Ejlali, “Two-State Checkpointing for Energy-Efficient Fault Tolerance in Hard Real-Time Systems,” pp. 1–12, 2016.
[16] S. Ben Alla and A. Ezzati, “Hierarchical adaptive balanced energy efficient routing protocol (HABRP) for heterogeneous wireless sensor networks,” Ieee, 2011.
[17] P. Handa, B. Singh Sohi, and N. Kumar, “Energy efficient hybrid routing protocol for underwater acoustic sensor network,” 2016 Int. Conf. Electr. Electron. Optim. Tech., pp. 2573–2578, 2016.
[18] B. Mills, T. Znati, R. Melhem, K. B. Ferreira, and R. E. Grant, “Energy consumption of resilience mechanisms in large scale systems,” Proc. - 2014 22nd Euromicro Int. Conf. Parallel, Distrib. Network-Based Process. PDP 2014, pp. 528–535, 2014.