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Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism

Preet Kawal Kaur1 , Kamaljit Kaur2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-5 , Page no. 1351-1359, May-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i5.13511359

Online published on May 31, 2019

Copyright © Preet Kawal Kaur, 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.

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IEEE Style Citation: Preet Kawal Kaur, Kamaljit Kaur, “Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1351-1359, 2019.

MLA Style Citation: Preet Kawal Kaur, Kamaljit Kaur "Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism." International Journal of Computer Sciences and Engineering 7.5 (2019): 1351-1359.

APA Style Citation: Preet Kawal Kaur, Kamaljit Kaur, (2019). Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism. International Journal of Computer Sciences and Engineering, 7(5), 1351-1359.

BibTex Style Citation:
@article{Kaur_2019,
author = {Preet Kawal Kaur, Kamaljit Kaur},
title = {Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2019},
volume = {7},
Issue = {5},
month = {5},
year = {2019},
issn = {2347-2693},
pages = {1351-1359},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4412},
doi = {https://doi.org/10.26438/ijcse/v7i5.13511359}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i5.13511359}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4412
TI - Reliability Optimization Using Distance Check pointing Through Dynamic Request And Requirement Aware Mechanism
T2 - International Journal of Computer Sciences and Engineering
AU - Preet Kawal Kaur, Kamaljit Kaur
PY - 2019
DA - 2019/05/31
PB - IJCSE, Indore, INDIA
SP - 1351-1359
IS - 5
VL - 7
SN - 2347-2693
ER -

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Abstract

In cloud computing the datacenters are utilized to coordinates the distinct tasks where tasks may require more resources and source of these cloudlets could be from thousands of users. These datacenters aims to deliver reliable services. But the equal reliability to all the users at the same time according to their requirements may be a difficult task and may vary. So in this paper we purpose a technique that considers optimized elastic reliability in cloud computing using distance based server selection policy. In our scheme reliability enhancement through distance dependent check pointing with resource maximization mechanism is distance aware checkpoint. Resource maximization mechanism uses division policy which divides the jobs by looking at the capacity of virtual machine on which load is to be dispersed. This operation can efficiently solve the problem of reliability. it improves the resource usage in the datacenters and also gives optimized reliability to the user.

Key-Words / Index Term

Reliability, Checkpointing, datacenters, cloudcomputing

References

[1] S. Ranga, “A Survey for Secure Live Migration of Virtual Machines in Cloud Computing Platform,” pp. 110–115.
[2] D. Jain and V. Singh, “Feature selection and classification systems for chronic disease prediction: A review,” Egypt. Informatics J., 2018.
[3] C. Feng, H. Xu, and B. Li, “An Alternating Direction Method Approach to Cloud Traffic Management,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 8, pp. 2145–2158, 2017.
[4] J. Wang, W. Bao, X. Zhu, L. T. Yang, and Y. Xiang, “FESTAL: Fault-Tolerant Elastic Scheduling Algorithm for Real-Time Tasks in Virtualized Clouds,” IEEE Trans. Comput., vol. 64, no. 9, pp. 2545–2558, 2015.
[5] M. R. Chinnaiah and N. Niranjan, “Fault tolerant software systems using software configurations for cloud computing,” J. Cloud Comput., vol. 7, no. 1, 2018.
[6] A. Zhou, S. Wang, C. H. Hsu, M. H. Kim, and K. seng Wong, “Virtual machine placement with (m, n)-fault tolerance in cloud data center,” Cluster Comput., pp. 1–13, 2017.
[7] Y. Wang, Q. He, D. Ye, and Y. Yang, “Formulating Criticality-Based Cost-Effective Fault Tolerance Strategies for Multi-Tenant Service-Based Systems,” IEEE Trans. Softw. Eng., vol. 44, no. 3, pp. 291–307, 2018.
[8] G. Li, J. Wu, J. Li, K. Wang, and T. Ye, “Service Popularity-based Smart Resources Partitioning for Fog Computing-enabled Industrial Internet of Things,” IEEE Trans. Ind. Informatics, vol. PP, no. c, p. 1, 2018.
[9] 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.
[10] A. Zhou, S. Wang, B. Cheng, Z. Zheng, F. Yang, R. N. Chang, M. R. Lyu, and R. Buyya, “Cloud service reliability enhancement via virtual machine placement optimization,” IEEE Trans. Serv. Comput., vol. 10, no. 6, pp. 902–913, 2017.
[11] S. M. Abdulhamid, M. S. Abd Latiff, S. H. H. Madni, and M. Abdullahi, “Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm,” Neural Comput. Appl., vol. 29, no. 1, pp. 279–293, 2018.
[12] A. S. Perspective, R. Jhawar, G. S. Member, and V. Piuri, “Fault Tolerance Management in Cloud Computing :,” IEEE Syst. J., vol. 7, no. 2, pp. 288–297, 2013.
[13] C. A. Chen, M. Won, R. Stoleru, and G. G. Xie, “Energy-efficient fault-tolerant data storage and processing in mobile cloud,” IEEE Trans. Cloud Comput., vol. 3, no. 1, pp. 28–41, 2015.
[14] R. Jhawar and V. Piuri, “Chapter 9 - Fault Tolerance and Resilience in Cloud Computing Environments,” Comput. Inf. Secur. Handb. (Third Ed., vol. 2, pp. 165–181, 2017.
[15] L. P. Saikia and Y. L. Devi, “Fault tolererance techniques and algorithms in cloud system,” Int. J. Comput. Sci. Commun. Networks, vol. 4, no. 1, pp. 1–8, 2014.
[16] T. J. Charity and G. C. Hua, “Resource reliability using fault tolerance in cloud computing,” Proc. 2016 2nd Int. Conf. Next Gener. Comput. Technol. NGCT 2016, no. October, pp. 65–71, 2017.
[17] J. Liu, S. Wang, S. Member, A. Zhou, S. A. P. Kumar, S. Member, and R. Buyya, “Using Proactive Fault - Tolerance Approach to Enhance Cloud Service Reliability,” IEEE Access, pp. 1–13, 2016.
[18] D. Boru, D. Kliazovich, F. Granelli, P. Bouvry, and A. Y. Zomaya, “Energy-efficient data replication in cloud computing datacenters,” IEEE Access, vol. 18, no. 1, pp. 385–402, 2015.
[19] D. Ardagna, G. Casale, M. Ciavotta, J. F. Pérez, and W. Wang, “Quality-of-service in cloud computing : modeling techniques and their applications,” IEEE Access, pp. 1–17, 2014.
[20] B. S. Taheri, “ACCFLA : Access Control in Cloud Federation using Learning Automata,” vol. 107, no. 6, pp. 30–40, 2014.
[21] K. B. Ferreira, R. Riesen, P. Bridges, D. Arnold, and R. Brightwell, “Accelerating incremental checkpointing for extreme-scale computing,” Futur. Gener. Comput. Syst., vol. 30, no. 1, pp. 66–77, 2014.
[22] 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.
[23] M. V Santiago, S. E. P. Hernández, L. A. M. Rosales, and H. H. Kacem, “Checkpointing Towards Dependable Business Processes,” vol. 14, no. 3, pp. 1408–1415, 2016.
[24] R. Luo, S. Member, W. Liao, S. Member, H. Zhang, and S. Member, “Fusion of Hyperspectral and LiDAR Data for Classification of Cloud-Shadow Mixed Remote,” IEEE Access, pp. 1–14, 2017.
[25] R. M. Systems, T. Wei, P. Mishra, K. Wu, and H. Liang, “Fixed-Priority Allocation and Scheduling for Energy-Efficient Fault Tolerance in Hard,” vol. 19, no. 11, pp. 1511–1526, 2008.
[26] S. Kannan and S. Rajendran, “Energy Efficient Cloud Computing,” pp. 157–171.
[27] G. R. Kalanirnika and V. M. Sivagami, “Fault Tolerance in Cloud Using Reactive and Proactive Techniques,” IEEE, vol. 3, no. 3, pp. 1159–1164, 2015.
[28] G. Yao, Y. Ding, S. Member, and K. Hao, “Using imbalance characteristic for fault - tolerant workflow scheduling in Cloud systems,” vol. 9219, no. c, 2017.
[29] J. Liu, S. Wang, A. Zhou, S. Kumar, F. Yang, and R. Buyya, “Using Proactive Fault-Tolerance Approach to Enhance Cloud Service Reliability,” IEEE Trans. Cloud Comput., pp. 1–1, 2016.
[30] M. Amoon, C. Science, and P. O. B. R. Arabia, “Adaptive Framework for Reliable Cloud Computing Environment,” ACM, vol. 3536, no. c, 2016.