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Machine Translation has gained popularity over the years and has become one of the promising areas of research in computer science. Due to a consistent growth of internet users across the world information is now more versatile and dynamic available in almost all popular spoken languages throughout the world. From Indian perspective importance of machine translation become very obvious because Hindi is a language that is widely used across India and whole world. Many initiatives have been taken to facilitate Indian users so that information may be accessed in Hindi by converting it from one language to other. In this paper we have studied various available automatic metrics that evaluate the quality of translation correlation with human judgments.

Machine Translation, Corpus, bleu, Nist, Meteor, wer, ter, gtm

[1] Philipp Koehn, Christof Monz ,”Manual and Automatic Evaluation of Machine Translation between European Languages” School of Informatics University of Edinburgh ,Department of Computer Science Queen Mary, University of London. Proceeding StatMT `06 Proceedings of the Workshop on Statistical Machine Translation Pages 102-121
[2] Michael Denkowski and Alon Lavie Language ,”Choosing the Right Evaluation for Machine Translation: an Examination of Annotator and Automatic Metric Performance on Human Judgment Tasks” Proceedings of the Ninth Biennial Conference of the Association for Machine Translation in the Americas https://www.cs.cmu.edu/~mdenkows/pdf/mteval-amta-2010.pdf
[3] Matthew Snover Bonnie Dorr Richard Schwartz, Linnea Micciulla, and John Makhoul, “A Study of Translation Edit Rate with Targeted Human Annotation” Proceedings of association for machine translation in the Americas, pp 223-231.
[4] Aditi Kalyani, Hemant Kumud Shashi Pal Singh Ajai Kumar,” Assessing the Quality of MT Systems for Hindi to English Translation” International Journal of Computer Applications (0975 – 8887) Volume 89 – No 15, March 2014
[5] Klakow, Dietrich; Jochen Peters (September 2002). "Testing the correlation of word error rate and perplexity". Speech Communication. 38 (1-2): 19–28. doi:10.1016/S0167-6393(01)00041-3. ISSN 0167-6393
[6] Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu,” BLEU: a Method for Automatic Evaluation of Machine Translation “. Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics (ACL), Philadelphia, July 2002, pp. 311-318
[7] Jason Brownlee “A Gentle Introduction to Calculating the BLEU Score for Text in Python “.November 20, 2017 in Natural Language Processing” Online https://machinelearningmastery.com/calculate-bleu-score-for-text-python/
[8] Xingyi Song and Trevor Cohn and Lucia Specia,” BLEU deconstructed: Designing a Better MT Evaluation Metric” University of Sheffield Department of Computer Science Proceedings of the 14th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING)
[9] Doddington, George. (2002),”Automatic evaluation of machine translation quality using n-gram co-occurrence statistics”.138-145. 10.3115/1289189.1289273
[10] Satanjeev Banerjee Alon Lavie ,”METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments” Institute Language Technologies Institute Carnegie Mellon University. Proceedings of Workshop on Intrinsic and Extrinsic Evaluation Measures for MT and/or Summarization at the 43rd Annual Meeting of the Association of Computational Linguistics (ACL-2005), Ann Arbor, Michigan, June 2005.
[11] Ankush Gupta and Sriram Venkatapathy and Rajeev Sangal ,” METEOR-Hindi : Automatic MT Evaluation Metric for Hindi as a Target Language”. Language Technologies Research Centre, IIIT-Hyderabad, Hyderabad, India. Proceedings of ICON-2010:8th International conference on Natural language processing, Macmillan Publishers, India.
[12] Xinlei Chen, Hao Fang, Tsung-Yi Lin, Ramakrishna Vedantam Saurabh Gupta, Piotr Dollar, C. Lawrence Zitnick ,”Microsoft COCO Captions: Data Collection and Evaluation Server”. CoRR 2015 Vol: abs/1504.00325

K. Sourabh, S. M Aaqib, V. Mansotra, "A Study of metrics for evaluation of Machine translation", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.1-4, 2018.

Security has always been a major concern for authentication over networking. Fingerprints are one of the biometrics which plays an important role in identifying a person based on some minutiae features. This is one of the most commonly used algorithms for extracting features that characterizes a fingerprint. This biometrics has several applications like e-governance, commercial and forensic. The fingerprint biometric offers a higher degree of security and personal privacy. Emerging privacy concerns with the database acquisition and lack of availability of large scale fingerprint databases have posed challenges in exploring this technology for large scale applications. Fingerprint recognition looks for the unique patterns of ridges and valleys that are present in an individual’s fingerprint. These patterns are unique to every individual and thus help to identify individuals from an entire population. Verification and identification are the two ways in which an individual’s identity can be determined using biometric technology. This research also developed user-friendly software to synthesize fingerprint databases, which could help to advance further research in fingerprint biometrics.

Biometric, Authentication System, Fingerprint recognition, Minutiae Based, Biometric security system, Identification access control

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Samiksha Suri, "Biometric Based on Fingerprint", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.5-13, 2018.

Credit risk remains till date one of the biggest and most challenging issue in the lending financial institutions. Credit risk refers to the probability of default which may occur if the liabilities are not fulfilled under the terms of the contract, resulting into the loss of the financial institution or banks (the creditor). Difficulties in credit risk management arise because the credit default occurs mostly, unexpectedly. The databases of the banks around the world have accumulated large quantities of information about clients and their financial and credit history. These databases can be used for the credit risk assessment, but they are generally high dimensional and traditional data analytics may not be able to handle such large volume of high dimensional data. How to develop a high-performance platform to efficiently analyze the Big Data Analytics (BDA) that can lead to better and more informed credit decisions? This study seeks to answer this question by discussing a macroscopic view of emerging Big Data techniques for addressing the vital issues of credit risk across the various sectors of finance and aim to identify the suitable BDA tools for the purpose of managing Credit Risk.

Credit, Big, Risk, Hadoop, Finance

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Deepika Sharma , "Credit Risk Management through Big Data Analytics", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.14-18, 2018.

The present research was conducted to assess Internet Gratification among youth in the domains of Cognitive, Affective, Personal Integration, Social Integration and Escape. A sample of 400 youth (200 females and 200 males) in the age group of 20-25 years, seeking higher education, was selected from four Tehsils of Jammu District namely Bhalwal, Nagrota, Jourian, and Arnia. Multistage sampling technique was used to select the sample for the study. The tools used for the study were Self Devised Screening Device and Internet Gratification Questionnaire developed by Joorabchi et al (2013). The results of the study show that the mean age of the sample was 22.5 years. Results further revealed that most of the respondents showed High Level of Internet Gratification. Females show ‘High’ level, whereas males show ‘Moderate’ Level of Internet Gratification. Significant sex differences were observed on Cognitive, Affective, Escape and Overall Score of Internet Gratification.

Youth, Internet Gratification, Cognitive, Affective, Escape, Personal Integration, Social-Integration, Gratification

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Sunali Sharma, Neeru Sharma, "A Study of Internet Gratification among Youth", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.19-24, 2018.

Migration of Virtual Machines is one of the efficient ways to manage resources in a Cloud Data Centre, dynamically, and reduce various runtime costs. But, sometimes, rigorous movement of virtual machines from over-utilized or under-utilized physical machines, results in performance degradation and service level agreement violation. Hence, it must be done carefully. A new virtual machine selection policy has been proposed in this paper which uses the concept of least deviation and resource satisfaction aspect for selection of a virtual machine which need to be migrated from overloaded servers in a cloud data centre. The proposed policy has been evaluated via extensive simulations by performing experiments on real workload traces from PlanetLab. The performance of proposed policy has been compared with already existing traditional policies for selection of virtual machine from over-utilized or under-utilized machines like Minimum Migration Time (MMT), Minimum Utilization (MU) and Random Selection (RS) available in CloudSim toolkit. The results show that the proposed policy outperforms the above mentioned policies on the basis of parameters like Power Consumption, SLA violation, No. of migrations, Energy Violation Metric.

Cloud Data Centre, VM Selection, Energy Efficiency, Resource Satisfaction Aspect, QoS, SLAs, VM Consolidation and Redistribution

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Minu Bala, "Least Migration Load Based Virtual Machine Selection Policy for Migration Process in Clouds", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.25-31, 2018.

An appearance cum feature based iris center gaze detection model viz. Iris Center Based Gaze Estimation (ICGE) is proposed to detect the direction of gaze quadrants to overcome certain limitations like dependency on light sources, multiple glint formation, no formation of glint etc, observed in glint based gaze quadrant detection models. The model works on the adaptive thresholding technique for the detection of the iris center coordinates using more than two hundred images from an indigenous database of different subjects on a five quadrants map screen. The model works with the Circular Hough Transform (CHT) for localising circles in the eye images and then center coordinates on the iris edge for further detection of gaze quadrants. Gaze directions of five different positions of iris are estimated on a mapped screen within the eye region. The model generates almost ninety percent accurate results for correct iris and gaze quadrant detection. The distinguishing features of the low cost, non intrusive proposed model include non IR and affordable ubiquitous hardware designing, large subject-camera distance and screen dimensions, no glint dependency etc. The proposed model also shows significantly better results in the lower periphery corners of the quadrant map. The proposed model may be more suitable for interactive applications for healthy users who cannot use head and hands freely while doing other tasks or disabled users who have no movement in their hands and head etc.

Iris Center Based Gaze Estimation (ICGE) model, adaptive thresholding, iris center, gaze quadrant detection

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[16] N. Avazpour and M. Emadi, “Iris recognition methods: A Review” International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 11, pp. 527-534, 2016.
[17] S. M. Zadeh and A. Harimi, “Iris localization by means of adaptive thresholding and Circular Hough Transform ”, Journal of Artificial Intelligence and Data Mining, Vol. 5, No. 1, pp. 21-28, 2017, [doi: 10.22044/jadm.2016.731]
[18] M. Abdullah, S. Dlay, W. Woo and J. Chambers, “Robust iris segmentation method based on a new active contour force with a non-ideal normalization”, IEEE Transactions on Systems, Man, and Cybernetics: Systems, Vol. 99, pp. 1-14, 2016, [doi: 10.1109/ TSMC.2016.2562500].
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[20] A. Sharma and P. Abrol, “Evaluating Interactivity with respect to Distance and Orientation Variables of GDE Model,” In the Proceedings of the Springer International Congress on Information and Communication Technology (Advances in Intelligent Systems and Computing), 438, Springer Singapore, pp. 177-187, 2015, [doi: 10.1007/978-981-10-0767-5_20].
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Anjana Sharma, Pawanesh Abrol, "Gaze Direction and Estimation Model Based on Iris Center Coordinates", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.32-37, 2018.

Biometric systems are contemporary tools for human recognition, where the identity of the user is established by using his/her biological, behavioral or chemical characteristics. Biometric based systems are used to overcome many challenges of the traditional based systems. The important information of the biological traits of a user is stored into a database. In the recent past, it has been observed that the security of such biometric systems may be breached in many ways. In this paper, a brief review of various feature template security schemes has been presented. The presented study shows that template security scheme for a particular biometric system may not be suitable for its counterparts. Furthermore, it is revealed that the design of an efficient and accurate template security schemes for a biometric-based application satisfying all the ideal characteristics is still a challenge for the research community.

Biometrics, Security, Feature Template, Biometric-based applications, Biometric Cryptosystems

[1] A. K. Jain, A. Ross, and S. Pankanti, “Biometrics: A tool for information security,” IEEE Trans. Inf. Forensics Secur., vol. 1, no. 2, pp. 125–143, 2006.
[2] N. K. Ratha, J. H. Connell, and R. M. Bolle, “An Analysis of Minutiae Matching Strength,” Audio- Video-Based Biometric Pers. Authentication, vol. 2091, pp. 223–228, 2001.
[3] K. Nandakumar, A. K. Jain, and A. Nagar, “Biometric template security,” EURASIP J. Adv. Signal Process., vol. 2008, 2008.
[4] K.-Y. Chee et al., “Cancellable Speech Template via Random Binary Orthogonal Matrices Projection Hashing,” Pattern Recognit., 2017.
[5] D. Sadhya and S. K. Singh, “Providing robust security measures to Bloom filter based biometric template protection schemes,” Comput. Secur., vol. 67, pp. 59–72, 2017.
[6] M. Gomez-Barrero, E. Maiorana, J. Galbally, P. Campisi, and J. Fierrez, “Multi-biometric template protection based on Homomorphic Encryption,” Pattern Recognit., vol. 67, pp. 149–163, 2017.
[7] A. Selwal, S. K. Gupta, and Surender, “Low overhead octet indexed template security scheme for the multi-modal biometric system,” J. Intell. Fuzzy Syst., vol. 32, no. 5, pp. 3325–3337, 2017.
[8] M. H. Lim, S. Verma, G. Mai, and P. C. Yuen, “Learning discriminability-preserving histogram representation from unordered features for multibiometric feature-fused-template protection,” Pattern Recognit., vol. 60, pp. 706–719, 2016.
[9] A. Selwal, S. K. Gupta, Surender, and Anubhuti, “Template security analysis of multimodal biometric frameworks based on fingerprint and hand geometry,” Perspect. Sci., vol. 8, pp. 705–708, 2016.
[10] F. S. Assaad and G. Serpen, “Transformation-based Score Fusion Algorithm for Multi-modal Biometric User Authentication through Ensemble Classification,” Procedia Comput. Sci., vol. 61, pp. 410–415, 2015.
[11] L. Lu and P. J., “Finger multi-biometric cryptosystem using feature-level fusion,” Int. J. Signal Process. Image Process. Pattern Recognit., vol. 7, no. 3, pp. 223–236, 2014.
[12] M. V. N. K. Prasad and C. Santhosh Kumar, “Fingerprint template protection using multiline neighboring relation,” Expert Syst. Appl., vol. 41, no. 14, pp. 6114–6122, 2014.
[13] Z. Jin, A. B. Jin Teoh, T. S. Ong, and C. Tee, “Fingerprint template protection with minutiae-based bit-string for security and privacy-preserving,” Expert Syst. Appl., vol. 39, no. 6, pp. 6157–6167, 2012.
[14] A.GHANY Kareem Kamal, A.Moneim Mahmood, Ghali Neveen I., Hassanien Aboul Ella, and Hefny Heshfile, “A Symmetric Bio-Hash Function Based On Fingerprint Minutiae And Principal Curves Approach,” 3rd Int. Conf. Mech. Electr. Technol. ( ICMET2011), no. February 2014, 2011.
[15] B. J. Kang and K. R. Park, “Multimodal biometric method based on vein and geometry of a single finger,” IET Comput. Vis., vol. 4, no. 3, p. 209, 2010.

Sheikh Imroza Manzoor, Arvind Selwal, "Biometric Feature Template Security Schemes: An Overview", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.38-42, 2018.

Clone research community has described several techniques to detect code duplicates present in the code base, mainly categorized into four classes viz. textual or text-based techniques, lexical or token-based techniques, syntactic techniques (including tree-based and metrics-based approaches) and semantic techniques. Literature lists various clone detector tools based on each category capable of detecting clones in batch mode as well as in real-time development environment. But, most of the tools use tokens as their intermediate representation of the source code upon which clone detection algorithms are applied. Thus, this paper will focus on this token-based intermediate representation and its pragmatic aspects towards code duplication detection. By discussing the practical process of converting source code into tokens as an intermediate code representation and how code duplicates are detected, authors will put light on the obscured pros and cons of this token-based approach that will help researchers to select as well as implement, or reject this approach as an intermediate representation for their duplication detection algorithms.

Code Clone Detection, Clone Detection Techniques, Token-based Clone Detection Technique

[1] Ira D. Baxter, Andrew Yahin, Leonardo Moura, Marcelo Sant` Anna, and Lorraine Bier, "Clone Detection Using Abstract Syntax Tree," in Proceedings of 14th International Conference on Software Maintenance(ICSM`98), Bethesda, Mayland, 1998, pp. 368 - 377.
[2] Stefan Bellon, Rainer Koschke, Giuliano Antoniol, Jens Krinke, and Ettore Merlo, "Comparision and Evaluation of Clone Detection Tools," IEEE Transaction on Software Engineering, vol. 33, no. 9, pp. 577 - 591, 2007.
[3] Chanchal K. Roy and James R. Cordy, "A Survey on Software Clone Detection Research," Queen`s University, Kingston, Technical Report 2007-541, 2007.
[4] Miryung Kim, Lawrence Bergman, Tessa Lau, and David Notkin, "An Ethnographic Study of Copy and Paste Programming Practices in OOPL," in Proceedings of the 2004 International Symposium on Empirical Software Engineering (ISESE’04), Redondo Beach, CA, USA, USA, 2004.
[5] Minhaz F. Zibran, Ripon K. Saha, Muhammad Asaduzzaman, and Chanchal K. Roy, "Analysing and Forecasting Near-miss Clones in Evolving Software: An Empirical Study," in Proceedings of the 16th IEEE International Conference on Engineering of Complex Computer Systems, Las Vegas, USA, 2011, pp. 295-304.
[6] M. F. Zibran and Chanchal Kumar Roy, "The Road to Software Clone Management: A Survey," Department of Computer Science, University of Saskatchewan, Canada, Technical Report 2012.
[7] Toshihiro Kamiya, Shinji Kusumoto, and Katsuro Inoue, "CCFinder: A Multilinguistic Token-Based Code Clone Detection System For Large Scale Source Code," IEEE Transactions on Software Engineering, vol. 28, no. 7, pp. 654-670, July 2002.
[8] Brenda Baker, "On Finding Duplication and Near Duplication in Large Software Systems," in Proceedings of the 2nd Working Conference on Reverse Engineering (WCRE`95), 1995, pp. 86 - 95.



[9] Zhenmin Li, Shan mar, Yuanyuan ZohuLu, and Suvda Myag, "CP-Miner: Finding Copy Paste and Related Bugs in Large Scale Software Code," IEEE Transaction on Software Engineering, vol. 32, no. 3, pp. 176 - 192, March 2006.
[10] Wikipedia.[Online]. https://en.wikipedia.org/wiki/Lexical_analysis

[11] Alfred V. Aho, Monica S. Lam, and Jeffrey D. Ullman Ravi Sethi, Compilers: Principles, Techniques, and Tools, 2nd ed.: Pearson.
[12] Raimer Falke, Pierre Frenzel, and Rainer Koschke, "Empirical Evaluation of Clone Detection using Syntax Suffix Trees," Empirical Software Engineering, vol. 13, no. 6, pp. 601 - 643, July 2008.
[13] Elizabeth Burd and John Bailey, "Evaluating Clone Detection Tools for Use during Preventative Maintenance," in Proceedings of the Second IEEE International Workshop on Source Code Analysis and Manipulation (SCAM `02), Montreal, Canada, 2002, pp. 36-43.
[14] M. Rieger, "Effective Clone Detection without Language Barriers," University of Bern, Switzerland, Dissertation 2005.
[15] Chanchal Kumar Roy, James Cordy, and Rainer Koschke, "Comparison and Evaluation of Code Clone Detection Techniques and Tools: A Quantitative Approach," Science of Computer Programming, vol. 74, no. 7, pp. 470 - 495, March 2009.

S. Bharti, H. Singh, "Pragmatic Aspects of Token-based Technique in Detecting Source Code Duplicates", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.43-49, 2018.

Cloud computing is a vibrant technology in today’s world. It delivers a platform independent service which is reliable, scalable, on demand etc. Cloud computing rely on allocating resources to achieve reliability and economy of extent similar to utility, Virtualization is key concept of cloud computing, it is a technique which allow multiple operating system run simultaneously on single physical server, it is used for balancing the loads on servers, managing the fault occurred and uses comprehensive strategy for energy consumption and system maintenance. There are two type of techniques used in migration i.e., pre copy and post copy, this research paper discusses study of various types of VM live migration, their classifications and relative scrutinizing of these methodologies and challenges in migrating virtual machine.

Pre-copy, virtualization, post-copy, cloud computing

[1] C. Clark et al., “Live migration of virtual machines,” NSDI’05 Proc. 2nd Conf. Symp. Networked Syst. Des. Implement., no. Vmm, pp. 273–286, 2005.
[2] M. M. R. Hines, U. Deshpande, and K. Gopalan, “Post-copy live migration of virtual machines,” ACM SIGOPS Oper. Syst., vol. 43, no. 3, p. 14, 2009.
[3] M. S. Lain, “Optimizing the Migration of Virtual Computers,” pp. 377–390.
[4] W. Huang, Q. Gao, J. Liu, and D. K. Panda, “High Performance Virtual Machine Migration with RDMA over Modern Interconnects,” pp. 11–20, 2015.
[5] Y. Luo, B. Zhang, X. Wang, Z. Wang, Y. Sun, and H. Chen, “Live and Incremental Whole-System Migration of Virtual Machines Using Block-Bitmap,” pp. 99–106, 2008.
[6] R. Bradford, E. Kotsovinos, A. Feldmann, and H. Schi, “Live Wide-Area Migration of Virtual Machines Including Local Persistent State,” pp. 169–179, 2007.
[7] W. Voorsluys, J. Broberg, and S. Venugopal, “Cost of Virtual Machine Live Migration in Clouds : A Performance Evaluation,” pp. 1–12.
[8] M. R. Desai and H. B. Patel, “Efficient virtual machine migration in cloud computing,” Proc. - 2015 5th Int. Conf. Commun. Syst. Netw. Technol. CSNT 2015, no. Vm, pp. 1015–1019, 2015.
[9] Z. Liu, W. Qu, T. Yan, H. Li, and K. Li, “Hierarchical Copy Algorithm for Xen live migration,” Proc. - 2010 Int. Conf. Cyber-Enabled Distrib. Comput. Knowl. Discov. CyberC 2010, pp. 361–364, 2010.
[10] H. Jin, L. Deng, S. Wu, X. Shi, and X. Pan, “Live Virtual Machine Migration with Adaptive Memory Compression,” 2009.
[11] F. Ma, F. Liu, and Z. Liu, “Live Virtual Machine Migration based on Improved Pre-copy Approach,” pp. 230–233, 2010.

Sheetal Kumar, Deepti Malhotra, "Live Virtual Machine Migration Techniques, Survey and Research Challenges", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.50-53, 2018.

Cloud is large, complex and distributed in nature. It has many features like availability, Reliability and performance of the cloud. Due to its large size and complex nature it is prone to various types of fault and failure like data unavailable, data deletion or corruption etc. Cloud are made as fault tolerant system that tolerate any imminent fault or failure, but still sometime fault happens that disrupts the normal service of cloud. Many researchers gave different technique like replication, checkpointing , Retry ,resubmission etc. to tolerate the failure. In this paper, study of various reactive fault tolerance techniques has been done and after analysis conclusion is presented with some future scope.

Replication, Checkpointing, Resubmission, Reliability

[1] S. Prathiba and S. Sowvarnica, “Survey of failures and fault tolerance in cloud,” in 2017 2nd International Conference on Computing and Communications Technologies (ICCCT), 2017, pp. 169–172.
[2] G. R. Kalanirnika and V. M. Sivagami, “Fault

Tolerance in Cloud Using Reactive and Proactive

Techniques,” Int. J. Comput. Sci. Eng. Commun., vol. 3, no. 3, pp. 1159–1164, 2015.
[3] S. Kadekodi, “A Compression in Checkpointing and Fault Tolerance Systems,” ACM Ref. Format Saurabh Kadekodi ACM Trans. Em-bedd. Comput. Syst. V, N, Artic. A, vol. 8, 2013.
[4] A. Rajalakshmi, D. Vijayakumar, and K. G.

Srinivasagan, “An improved dynamic data replica selection and placement in cloud,” 2014 Int. Conf.
Recent Trends Inf. Technol. ICRTIT 2014, vol. 3, no.
3, 2014.
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[6] Y. Kwon, M. Balazinska, and A. Greenberg, “Fault-tolerant Stream Processing using a Distributed,
Replicated File System.”
[7] T. Nadu, “Fault tolerant workflow scheduling based on replication and resubmission of tasks in Cloud

Computing,” Int. J. Comput. Sci. Eng., vol. 4, no. 6,
pp. 996–1006, 2012.
[8] L. M. M. Visuwasam, “Checkpoint-based Intelligent

Fault tolerance For Cloud Service Providers,” no. 2,
pp. 59–64, 2012.
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Cluster,” in 2010 IEEE International Conference on Cluster Computing, 2010, pp. 188–196.
[10] G. Yao, Y. Ding, and K. Hao, “Using Imbalance

CharacteristicforFault-TolerantWorkflow

[11] Scheduling in Cloud Systems,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 12, pp. 3671–3683, Dec. 2017.
[12] E. AbdElfattah, M. Elkawkagy, and A. El-Sisi, “A reactive fault tolerance approach for cloud computing,” in 2017 13th International Computer Engineering Conference (ICENCO), 2017, pp. 190– 194.
[13] M. K. Gokhroo, M. C. Govil, and E. S. Pilli,“Detecting and mitigating faults in cloud computing environment,” 3rd IEEE Int. Conf. , 2017.
[14] V. B. Souza, X. Masip-Bruin, E. Marin-Tordera, W.Ramirez, and S. Sanchez-Lopez, “Proactive vs reactive failure recovery assessment in combined Fog-to-Cloud (F2C) systems,” in 2017 IEEE 22nd International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD), 2017, pp. 1–5.
[15] P. Padmakumari, A. Umamakeswari, and M.Akshaya, “Hybrid Fault Tolerant Scheme to Manage VM Failure in the Cloud,” Indian J. Sci. Technol. ISSN, no. 948, pp. 974–6846, 2016.
[16] O. Subasi, G. Yalcin, F. Zyulkyarov, O. Unsal, and J.Labarta, “Designing and Modelling Selective Replication for Fault-Tolerant HPC Applications,” Proc. - 2017 17th IEEE/ACM Int. Symp. Clust. Cloud Grid Comput. CCGRID 2017, pp. 452–457, 2017.

Atul Kumar, Deepti Malhotra, "Study of Various Reactive Fault Tolerance Techniques in Cloud Computing", International Journal of Computer Sciences and Engineering, Vol.06, Issue.05, pp.54-58, 2018.