Volume-6 , Issue-6 , Jun - 2018 Go Back

In the current era of technology, evolution of medical sciences becomes an active field of research as people have more curiosity towards their health. Different techniques of data mining are used to mine the information from various data patterns. Prior, PC was used to manufacture an information based clinical result which utilizes learning from therapeutic specialists and moves this information into PC calculations physically. This process takes lot of time and gives subjective results as this information only depends on medical professional only. To overcome these type of problems various techniques of machine learning are used to extract important medical patterns from the raw data. In this paper, we have critically analyzed various data mining techniques to gather informative patterns from data sets in medical sciences.

Informative Patterns, Clinical Databases, Data Mining, Prediction, Machine Learning

[1] Emrana Kabir Hashi, Md. Shahid Uz Zaman and Md. Rokibul Hasan, “An Expert Clinical Decision Support System to Predict Disease Using Classification Techniques”, International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 396-400, February 16-18, 2017.
[2] P. K. Anooj, “Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules,” J. of King Saud Uni. Comput. and Inform. Sci., ELSEVIER, Vol. 24, pp. 27-40, 2012.
[3] Purushottam, K.Saxena and R. Sharma, “Efficient Heart Disease Prediction System,” Proced. Comput. Sci., ELSEVIER, Vol. 85, pp. 962 – 969, 2016.
[4] Parthiban Latha and Subramanian R., “Intelligent Heart Disease Prediction System using CANFIS and Genetic Algorithm” International Journal of Biological and Life Sciences 3:3 2008.
[5] Uppin ShravanKumar and M A Anusuya, “Expert System design to predict Heart and Diabetes Diseases”, International Journal of Scientific Engineering and Technology Vol: 03, 2014.
[6] Shouman Mai, Tumer Tim, Stocker Rob, “Using Data Mining Techniques in Heart Disease Diagnosis and Treatment”, International Conference on Electronics, Communications and Computers, 2012, IEEE, Northcott Drive, Canberra.
[7] Ioannis Kavakiotis, Olga Tsave, Athanasios Salifoglou, Nicos Maglaveras, Ioannis Vlahavas, Ioanna Chouvarda, “Machine Learning and Data Mining Methods in Diabetes Research“,Comput Struct Biotechnol J. Vol. 15, pp. 104–116, 2017.
[8] Sheena Angra, Sachin Ahuja, “Machine Learning and its Applications: A Review”, IEEE, International Conference On Big Data Analytics and computational Intelligence, pp. 57-60, 2017.
[9] N. Yuvaraj, K. R. SriPreethaa, “Diabetes prediction in healthcare systems usingmachine learning algorithms on Hadoop cluster”, Springer, Cluster Computing, 2017.
[10] Mac Dougall Candice, Percival Jennifer and Mc Gregor Carolyu, “Integrating Health Information Technology into Clinical Guidelines”, Annual International Conference of the IEEE, EMBS Minneapolis, Minnesota, USA, September 2-6, 2009.
[11] Srinivas K, Kavihta Rani B. and Dr. Govrdhan A., “Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks”, International Journal on Computer Science and engineering Vol. 02, No. 02, pp. 250-255, 2010.
[12] Sundar V Bata and Tevi T, Saravanan N, “Development of a Data Clustering Algorithm for Predicting Heart”, International Journal of Computer Applications(0975-888) Volume 48, No. 7, June 2012, Coimbatore, India.
[13] M Nirmala Devi, Balamurugan.S Appavu alias, U.V Swathi, “An amalgam KNN to predict Diabetes Mellitus”, IEEE International Conference on Emerging Trends in Computing, Communication and Nanotechnology, Madurai, Tamil Nadu, India, 2013.
[14] Thangarasu Gunasekar and Assoc. Prof. Dr. Dominic P.D.D, “Prediction of Hidden Knowledge from Clinical Database using Data mining Techniques”, IEEE 978-1-4799-0059-6, Tronoh Perak, Malaysia, 2014.
[15] Ayush Anand, Divya Shakti, “Prediction of Diabetes Based on Personal Lifestyle Indicators”, IEEE, International Conference on Next Generation Computing Technologies, pp. 673-676, 2015.
[16] Divya Chitkara, Dr. R.K. Sharma, “Voice based Detection of type 2 Diabetes Mellitus”, IEEE, International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics, 2016.
[17] Namrata Ghuse, Pranali Pawar, Amol Potgantwar, “An Improved Approch For Fraud Detection In Health Insurance Using Data Mining Techniques”, Int. J. Sc. Res. in Network Security and Communication, Vol. 5, Iss. 5, pp. 27-32, June 2017.

Monika, Pooja Sharma, "Survey on Prediction and Analysis of Diabetic Data using Machine Learning Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1032-1038, 2018.

Data mining is a set of techniques to analyze available data to find some hidden truths and unknown facts. The purpose of these techniques is to determine the information from the data. Such information might be in the form of explaining past or predicting future. In recent years, prediction algorithms are used for various applications. From prediction of student performance to product selling, from prediction of diseases to stock market, we try to find out what will happen in future using prediction algorithms. One of the most widely used future predictions is classification. This paper discusses how classification helps in prediction of life threaten diseases like cervical cancer.

Data Mining, Prediction, Classification, Decision Tree, Naïve Bayes, Cancer, Cervical Cancer

[1] Han, Jiawei, Jian Pei, and Micheline Kamber. Data mining: concepts and techniques. Elsevier, 2011.
[2] Aggarwal, Charu C. Data mining: the textbook. Springer, 2016.
[3] Jiawei Han, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, 2001
[4] Kaur H, Wasan SK. Empirical Study on Applications of Data Mining Techniques in Healthcare. J Comput Sci. 2006;2(2):194-200. doi:10.3844/jcssp.2006.194.200.
[5] Venkatadri.M and Lokanatha C. Reddy ,“A comparative study on decision tree classification algorithm in data mining” , International Journal Of Computer Applications In Engineering ,Technology And Sciences (IJCAETS), Vol.- 2 ,no.- 2 , pp. 24- 29 , Sept 2010.
[6] Safavian, S. Rasoul, and David Landgrebe. "A survey of decision tree classifier methodology." IEEE transactions on systems, man, and cybernetics21.3 (1991): 660-674.

Dipti N. Punjani, Kishor Atkotiya, "A Comprehensive Study of Various Classification Techniques in Medical Application using Data Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1039-1042, 2018.

Electronic commerce is an emerging area and one of the technological innovations of business management and information technology. A fair online electronic cash system with double spending tracing based on elliptic curve cryptography is presented in this paper. The security of the protocols is based on the computational hard elliptic curve discrete logarithm problem. In the proposed scheme, the anonymity of the user is maintained and is revocable by an on-line trusted third party under certain conditions. Dual spending of the same coin by the user is traced out by the bank. The proposed scheme is illustrated using Matlab 7.1

Cryptography, electronic cash system, elliptic curve, anonymity, unforgeability, double spending tracing

[1] Brands S, “Untraceable off-line cash in wallets with observers”, Advances in Cryptology: Proceedings of Crypto ’93, Lecture Notes in Computer Science, Springer-Verlag, (1994) pp. 302-318.
[2] Chaum D, “ Blind Signature for untraceable Payments”, Advances in Cryptology-Crypto’ 82, (1982) 199-203.
[3] Chaum D, Fiat A and Naor M, “Untraceable Electronic Cash” Advances in Cryptology CRYPTO `88, LNCS 403, Springer Verlag,(1988) 319-327.
[4] Chaum D , “Online Cash Checks” Advances in Cryptology-EUROCRYPT`89, LNCS 434, Springer-Verlag, (1989) 288-293.
[5] Hou X, Tan C H , “A New Electronic Cash Model”,Proceedings of the International Conference on Information Technology, Coding and Computing (ITCC’05).
[6] Hou X, Tan C H, “On Fair Traceable Electronic Cash”, Proceedings of the 3rd Annual Communication Networks and Services Research Conference (CNSR’05).
[7] Hankerson D, Menezes A and Vanstone S, “Guide to Elliptic Curve Cryptography”, Springer Verlag New York, Inc,2004.
[8] Hans Delfs and Helmut Knebl, “Introduction To Cryptography, Principles and Applications”,Springer ,(1998),91-109.
[9] Khalid O Elaalim and Shoubao Yang, “Secure Electronic Cash Using Elliptic Curve Cryptography Based On Zero Knowledge Proof”, International Journal Of Cryptology Research ,3(1) : (2011) 15-26.
[10] Khalid O Elaalim and Shoubao Yang, “Electronic Cash System With Double Spending Tracing Based On Elliptic Curve Cryptography”, Journal Of Computational Information Systems 6:9(2010), 2949-2957.
[11] Popescu C, “A Fair Off-line Electronic Cash System Based On Elliptic Curve Discrete Logarithm Problem”, Studies in Informatics and Control, Vol. 14, No. 4,(2005) 291-298.
[12] Popescu C, “ A Secure E-cash Transfer System based on the Elliptic Curve Discrete Logarithm Problem”, Informatica, Vol.22, No. 3,(2011) 395-409.
[13] Ziba Eslami and Mehdi Talebi, “A New Fair Untraceable Off-Line Electronic Commerce Research and Applications , 10(2011) 59-66.

C. Porkodi, K. Sangavai, "An Online Electronic Cash System based on Elliptic Curve Cryptography," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1043-1047, 2018.

This paper illustrates how BlueOcean embraces the Jenkins CI/CD pipelines for providing enhanced and better user experience by enabling teams to easily adopt to continuous delivery. Blue Ocean plugin brings out an enhanced user interface to Jenkins automation tool based on modern and personalized design that allows users to graphically generate, diagnose and visualize continuous delivery pipelines. With the introduction of visual pipeline editor not just the developer but any member of the DevOps team can easily understand the process flow, enabling them to quickly identify and explore the issue that’s causing hindrance in execution of the flow.

Continuous Integration, Continuous Delivery, Jenkins, Pipeline, BlueOcean, Jenkins File

[1] Valentina Armenise, “Continuous Delivery with Jenkins: Jenkins Solutions to Implement Continuous Delivery”, Release Engineering (RELENG), 2015 IEEE/ACM 3rd International Workshop, 19 May 2015, Date Added to IEEE Xplore: 30 July 2015.
[2] M. Shahin, M. Ali Babar, and L. Zhu, ―Continuous Integration, Delivery and Deployment: A Systematic Review on Approaches, Tools, Challenges and Practices”, IEEE Access, 2017.
[3] P. M. Duvali, S. Matyas and A. Glover, “Continuous Integration Improving Software Quality and Reducing Risk”, Addison Wesley Signature Series publisher, UK, pp. 12-20, 2007
[4] J. McAllister, “Mastering Jenkins”, Packt publication, Sweden, pp. 58-70, 2015.
[5] Mathias Meyer, “Continuous Integration and Its Tools”, IEEE Software, 2014.
[6] M. Soni, “Jenkins Essentials”, Packt publication, India, pp. 30-45, 2015.
[7] Manish Virmani, “Understanding DevOps & Bridging The Gap From Continuous Integration To Continuous Delivery,” 2015.
[8] K. Sree Poornalinga, P. Rajkumar, “Survey on Continuous Integration, Deployment and Delivery in Agile and DevOps Practices” International Journal of Computer Sciences and Engineering, Volume: 4, Issue: 4 PP(213-216) April 2016, E-ISSN: 2347-2693

S.F. Rayanagoudar, P. S. Hampannavar, J.D. Pujari, V. K. Parvati, "Enhancement of CI/CD Pipelines with Jenkins BlueOcean," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1048-1053, 2018.

This paper diligently presents a comprehensive review of applications of web semantic technologies. Web semantics provides the backbone for information exchange among numerous diverse applications which are quite complex in themselves. The objective is to provide a controlled and scalable channel of knowledge transfer over the web .In this review paper we have limited our presentation to only three popular applications of web technology namely in digital libraries, legal domain, telecommunications. Our paper provides insights into few research projects currently taken up as well as challenges faced by researchers working in this field.

Digital Libraries, Web Semantics, Ontology, eTOM, SID

[1]K. Aberer, P. Cudre-Mauroux, and M. Hauswirth. The Chatty Web: Emergent Semantics Through Gossiping. In Proceedings of the 12th International World Wide Web Conference (WWW’03), Budapest, Hungary, 20-24 May 2003. ACM.
[2] S. Alexaki, V. Christophides, G. Karvounarakis, D. Plexousakis, K. Tolle, B. Amann, I. Fundulaki, M. Scholl, and A.-M. Vercoustre. Managing RDF Metadata for Community Webs. In Proceedings of the ER’00 2nd International Workshop on the World Wide Web and Conceptual Modeling (WCM’00), pages 140–151, Salt Lake City, Utah, 9-12 October 2000.
[3] Yigal Arens, Chun-Nan Hsu, and Craig A. Knoblock. Query Processing in the SIMS Information Mediator. In Advanced Planning Technology, California, USA, 1996. AAAI Press.
[4] Ricardo Baeza-Yates and Berthier Ribeiro-Neto. Modern Information Retrieval. Addison Wesley Longman, 1999.
[5] R. Beckwith and G. A. Miller. Implementing a Lexical Network. Technical Report 43, Princeton University, 1992.

Manas Kumar Yogi, G.Sai Sri Kavya, "A Comprehensive Review Of Web Semantic Technologies In Current World," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1054-1059, 2018.

Wireless Sensor Network (WSN) is an interesting research area in Computer Science. A WSN comprises of a set of sensor nodes which are responsible for gathering and transferring of information from source to destination. It provides the ability of sensing and processing information. This network also consists of different types of protocols with self organizing capabilities which are responsible for overall communication in the network. Routing protocols are responsible for finding the routes and to make sure reliable communication in the network. This paper is based on a survey of Location based routing protocols in which the information about the location of the node is used for communication.

Wireless Sensor Network, Routing Protocols, Energy Efficient Routing, Location Based Routing Protocols

[1] D. Prasad, V. Padmavati, “An Approaching of Energy Management Routing Protocols in Wireless Sensor Network”, International Research Journal of Engineering and Technology (IRJET), Volume: 04 Issue: 05 | ISSN: 2395 -0056, May -2017.
[2] A. Alim, M. Abdul, Y. C. Wu & W. Wang "A Fuzzy based clustering protocol for energy efficient Wireless sensor network". Advanced Materials Research vol-760, pp.685-690, 2013.
[3] G. Prajapati, P. Paramar, “A Survey On Routing Protocols Of Location Aware And Data Centric Routing Protocols in Wireless Sensor Network”, International Journal of Science And Research (IJSR), India, Vol. 2, No 1, pp. 128-133, January 2013.
[4] A. Kumar, H. Y. Shwe, K. J. Wong, P. H. J. Chong, “Location-Based Routing Protocols for Wireless Sensor Networks: A Survey”, Scientific Research Journal, Wireless Sensor Network, 2017, 9, 25-72, ISSN Online: 1945- 3086 ISSN Print: 1945-3078.

[5] F. Akyildiz, W. Su, Y. S. Subramanian, and E. Cayirci, “Wireless sensor networks: a survey”, Computer Networks (Elsevier) Journal, Vol. 38, no. 4, Mar. 2002, pp. 393-422.
[6] C. Intanagonwiwat, R. Govindan, and D. Estrin, "Directed diffusion: A scalable and robust communication paradigm for sensor networks", Proceedings ACM MobiCom`00, Boston, MA, Aug. 2000, pp. 56-67.
[7] H. Cheng, G. Yang, S. Hu, “NHRPA: A Novel Hierarchical Routing Protocol Algorithm for Wireless Sensor Networks,” China Universities of Posts and Telecommunications, 2008, Vol. 15, Issue 3, pp. 75-81.
[8] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient Communication Protocol for Wireless Sensor Networks”, in IEEE Computer Society Proceedings of the Thirty Third Hawaii International Conference on System Sciences (HICSS `00), Washington, DC, USA, , vol. 8, pp. 8020. Jan. 2000.
[9] R. Guleria and A. K. Jain, “Geographic Load Balanced Routing in Wireless Sensor Networks”, I. J. Computer Network and Information Security, Vol. 5, No. 8, pp. 62-77, June 2013.
[10] R. S. Battula and O. S. Khanna, “Geographic Routing Protocols for Wireless Sensor Networks: A Review”, International Journal of Engineering and Innovative Technology (IJEIT), Vol. 2, Issue 12, pp. 39-42, June 2013.
[11] A. M. Propescu, I. G. Tudorache, B. Peng and A. H. Kemp, “Surveying Position Based Routing Protocol for Wireless Sensor Network and Ad-hoc Networks”, International Journal of Communication Networks and Information Society, Vol. 4, No. 1, pp. 41, April 2012.
[12] R.V. Biradar, V. C. Patil, Dr. S. R. Sawan and Dr. R. R. Mudholkar, “Classification and Comparison of Routing Protocols in Wireless Sensor Networks”, UbiCCJournal, Vol. 4.
[13] J. Heidemann, and D. Estrin, "Geography-informed energy conservation for ad-hoc routing", Proceedings ACM/IEEE MobiCom`01, Rome, Italy, pp. 70-84, July 2001.
[14] G. V. Rama Lakshmi and V. Srikanth, “Location Based Routing Protocol in Wireless Sensor Network- A Survey”, International Journal of Advance Research in Computer Science and Software Engineering (IJARCSSE), Vol. 5, Issue 4, pp. 663-667, April 2015.
[15] Y. Yu, R. Govindan, and D. Estrin, "Geographical and energy aware routing: A recursive data dissemination protocol for wireless sensor networks", Technical Report UCLA/CSD-TR-01-0023, UCLA Computer Science Department, May 2001.
[16] V. Rodoplu, T.H. Ming, “Minimum Energy Mobile Wireless Networks”, IEEE Journal of Selected Areas in Communications 17 (8), 1333–1344, 1999.

[17] L. Li, J. Y Halpern, “Minimum energy mobile wireless networks revisited”, Proceedings of IEEE International Conference on Communications (ICC_01), Helsinki, Finland, June 2001.
[18] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, "Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks", Proceedings ACM MobiCom`01, Rome, Italy, pp. 85-96, July 2001.
[19] B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris, "Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks", Wireless Networks, vol. 8, no.5, pp. 481-494, Sept. 2002.
[20] Shubhi and Akhilesh Kumar Singh, “Wireless Sensor Network: A Survey”, International Journal of Engineering and Innovative Technology (IJEIT) Volume 4, Issue 11, pp. 280-286, May 2015.
[21] R.V. Biradar, V. C. Patil, Dr. S. R. Sawan and Dr. R. R. Mudholkar, “Classification and Comparison of Routing Protocols in Wireless Sensor Networks”, UbiCCJournal, Vol. 4.
[22] B. Gupta and S. Rana, “Mutual Exclusive Sleep Awake Distributive Clustering (MESADC): Energy Efficient Protocol for Prolonging Lifetime of Wireless Sensor Network”, International Journal of Computer Sciences and Engineering, Vol. 6, Issue 4, pp. 1-7, April 2018.

P. P. Bairagi, L. P. Saikia, "A Comparative Study on Location Based Routing Protocols in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1060-1064, 2018.

It is possible to identify an individual based on “who they are rather than what they possess or what they remember" by using biometric features like Thumb print, face, chin etc because to remember password or to take care of cards are difficult task. From all Biometrics security organs Iris is one of the most carefully protected organs in one’s body. It is not affected by aging; the feature of the iris remains fixed and stable from one year of age until death. Biometric-based solutions are able to provide for confidential financial transactions and personal data privacy there has been a lot of work carried out on face recognition using the PCA. Here I have tried to use the same concepts in detecting Irises. Here to implement iris recognition but here I have implemented iris detection from eye and recognition using PCA algorithm.

Co-variance matrix, Eigenvectors, Iris Recognition, Mean image, correlation coefficients

[1] Li. Ma, T.Tan,Y.Young, D. Zhang “ Personal Identification based on texture analysis”, IEEE transactions on Pattern Analysis and Machine intelligence,, Vol. 25, No. 12, 1519-1533, Dec.2003.
[2] B.Ganeshan, Dhananjay Theckedath, Rupert Young,Chris Chatwin, “ Biometric iris recognition using a fast and robust iris localization and alignment procedure”, Optics and Lasers in Engineering, Elsevier,vol.44 (2006) 1-24.
[3] Dhananjay Theckedath, Madhavi Joshi, Vivek Deodeshmukh, “Identification based on iris detection” NCBME 2006,pp 162-166 March2006.
[4] Li Ma, Yunhong Wang, Tieniu Tan “Iris Recognition Based on Multichannel Gabor Filtering” National Laboratory of Pattern Recognition, Institute of Automation,Chinese Academy of Sciences, P.O. Box 2728, Beijing, 100080, P.R. China.
[5] Dhananjay Theckedath ”Iris Detection Based On Principal Component Analysis-Eigen Irises” Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India.
[6] D. Zhang, “Biometrics Technologies and Applications”, Proc. of International Conference on Image and Graphics, pp.42-49, Tianjing, China, August 2000.
[7] R.P. Wildes, “Iris Recognition: An Emerging Biometric Technology”, Proceedings of the IEEE, vol.85, pp.1348-1363, Sept. 1997.
[8] W.W. Boles, and B. Boashah, “A Human Identification Technique Using Images of the Iris and Wavelet Transform”, IEEE Trans. on Signal Processing, vol.46, pp.1185-1188, April 1998.
[9] T.S. Lee, “Image Representation Using 2D Gabor Wavelets”, IEEE Trans. Pattern Analysis and Machine Intelligence, vol.18, pp.959-971, Oct. 1996.
[10] Anil K. Jain, Salil Prabhakar, Lin Hong, and Sharath Pankanti, “Filterbank-Based Fingerprint Matching”, IEEE Trans. Image Processing, vol. 9, pp.846-859, May 2000.
[11] Mary Reni B. Assistant Professor, Faculty of Computing Sathyabama University, Chennai. “Iris Recognition based Age Estimation in Security Systems using Canny Edge Detection” Research Journal of Pharmaceutical, Biological and Chemical Sciences September - October 2015 RJPBCS 6(5) Page No. 349.
[13] Gatheejathul Kubra.J,Rajesh.P “Iris Recognition and its Protection Overtone using Cryptographic Hash Function” SSRG International Journal of Computer Science and Engineering (SSRG-IJCSE) – volume 3 Issue 5–May 2016.

Sejal Thakkar, "Performance analysis of on Iris Detection Recognition and its Applications”," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1065-1068, 2018.

In these days the most important emerging technologies to enter IT mainstream are big data and cloud computing. These two technologies are coming together to provide powerful results and making intelligent decisions in businesses. Cloud computing is a blessing for an organisation that wants to update technology under limited budget. It provides reliable, scalable and fault-tolerant environment to big data distributed management systems. Big data has ability to store and process different types of data at a high speed which is stored at different locations. In this paper the benefits and challenges involved in deploying big data through cloud computing has been discussed. Although there are couple of challenges and risk that come cross the path of integration between cloud computing and big data but with the invasion of new tools and technologies we are able to cope up with these problems. This paper presents an overview of both the technologies big data and cloud computing and also introduces the characteristics, benefits and challenges of big data in cloud computing.

Big data, Cloud Computing, types of data, services of cloud, benefits, challenges

[1] Rajsingh, E.B., Veerasamy, J., Alavi, A.H., Peter, J.D. “Advances in Big Data and Cloud Computing”
[2] N.Turner, “Cloud Computing: A Brief Summary”, Lucid Communications limited, 2009.
[3] Dillon,T., Wu,C., Chang, E.: “Cloud Computing: Issues and Challenges”, 24th IEEE international Conference on advance Information Networking and Applications
[4] Marcello Trovati, Richard Hill, Ashiq Anjum, Shao Ying Zhu Lu Liu Editors, “Big Data Analytics and Cloud Computing- Theory, Algorithms and Applications”
[5] Arun K.Somani, Ganesh Chandra Deka, “Big Data Analytics- Tools and Technology for effective Planning”
[6] Chaowei Yang, Qunying Huang, Zhenlong Li, Kai Liu & Fei Hu, “Big Data and Cloud Computing: innovation opportunities and challenges”.
[7]J.Preethi,,N.Aswathy,"A Survey on Cloud Applications", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 2, pp.10-17
[8] Xiaofang Li, Yanbin Zhuang & Simon X. Yang, “Cloud Computing for Big Data Processing” .
[9] K. Phaneendra, Dr. M. Babu Reddy, "Prototype Survey of Different Resource Provisioning Procedures in Cloud Computing", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 1, Issue 1, pp.108-115.

Harmanpreet Kaur, "Big Data in Cloud Computing: Benefits and Challenges," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1069-1071, 2018.

Many important real world problems are computationally “hard” and one of those is cloud scheduling. There are various approaches to cloud scheduling, but in recent time scheduling strategies based on heuristics and metaheuristics are gaining popularity because of their performance. Especially important metaheuristics are nature inspired metaheuristics which have been proved to be very efficient in solving hard problems. These metaheuristics are inspired by natural phenomenon and simulate them in an algorithmic manner. In this paper, we describe those methods and present their successful applications in cloud scheduling problem. We will also describe the formal statement of the problem so that a reader can directly correlate the algorithms with applications below.

Nature inspired algorithms; Optimization; Genetic algorithm; Cuckoo search optimization; Particle swarm optimization; Ant colony optimization; Cloud scheduling problem

[1] Hopcroft, J.E., Motwani, R. and Ullman, J.D., 2001. Introduction to automata theory, languages, and computation. ACM SIGACT News, 32(1), pp.60-65.
[2] Benatallah, B. ed., 2011. Cloud computing: methodology, systems, and applications. CRC Press.
[3] Garey, M.R. and Johnson, D.S., 1979. A Guide to the Theory of NP-Completeness. WH Freemann, New York, 70.
[4] Yu, J., Buyya, R. and Ramamohanarao, K., 2008. Workflow scheduling algorithms for grid computing. In Metaheuristics for scheduling in distributed computing environments (pp. 173-214). Springer Berlin Heidelberg.
[5] Yang, X.S., 2014. Nature-inspired optimization algorithms. Elsevier.
[6] Rahman, M., Li, X. and Palit, H., 2011, May. Hybrid heuristic for scheduling data analytics workflow applications in hybrid cloud environment. In Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on (pp. 966-974). IEEE.
[7] Elsayed, K.M. and Khattab, A.K., 2006. Channel-aware earliest deadline due fair scheduling for wireless multimedia networks. Wireless Personal Communications, 38(2), pp.233-252.




[8] Schuetz, H.J. and Kolisch, R., 2012. Approximate dynamic programming for capacity allocation in the service industry. European Journal of Operational Research, 218(1), pp.239-250.
[9] Shi, D. and Chen, T., 2013. Optimal periodic scheduling of sensor networks: A branch and bound approach. Systems & Control Letters, 62(9), pp.732-738.
[10] Gendreau, M. and Potvin, J.Y., 2010. Handbook of metaheuristics (Vol. 2). New York: Springer.
[11] Holland, J.H., 1992. Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.
[12] Kennedy, J. and Eberhart, R.C., 1995. Particle swarm optimization. In: Proceedings of the IEEE international conference on neural networks, Piscataway, NJ, USA; 1995. p. 1942–48.
[13] Dorigo, M., 1992. Optimization, learning and natural algorithms. Ph.
D. Thesis, Politecnico di Milano, Italy.
[14] Dorigo, M. and Maniezzo, V., Colorni (1991) A positive feedback as a search strategy. Technical report 91-016, Politecnico di Milano,
Italy.
[15] Dorigo, M., Maniezzo, V. and Colorni, A., 1996. Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 26(1), pp.29-41.
[16] Gu, J., Hu, J., Zhao, T. and Sun, G., 2012. A new resource scheduling strategy based on genetic algorithm in cloud computing environment. Journal of Computers, 7(1), pp.42-52.
[17] Zhao, C., Zhang, S., Liu, Q., Xie, J. and Hu, J., 2009, September. Independent tasks scheduling based on genetic algorithm in cloud computing. In Wireless Communications, Networking and Mobile Computing, 2009. WiCom`09. 5th International Conference on (pp. 1-4). IEEE.
[18] Zhu, K., Song, H., Liu, L., Gao, J. and Cheng, G., 2011, December. Hybrid genetic algorithm for cloud computing applications. In Services Computing Conference (APSCC), 2011 IEEE Asia-Pacific (pp. 182-187). IEEE.
[19] Zuo, L., Shu, L., Dong, S., Zhu, C. and Hara, T., 2015. A multi-objective optimization scheduling method based on the ant colony algorithm in cloud computing. IEEE Access, 3, pp.2687-2699.
[20] X. Lu and Z. Gu, “A load-adapative cloud resource scheduling model based on ant colony algorithm,” IEEE International Conference on Cloud Computing and Intelligence Systems, pp. 296–300, 2011.
[21] Liu, H., Xu, D. and Miao, H.K., 2011, December. Ant colony optimization based service flow scheduling with various QoS requirements in cloud computing. In Software and Network Engineering (SSNE), 2011 First ACIS International Symposium on (pp. 53-58). IEEE.
[22] Li, K., Xu, G., Zhao, G., Dong, Y. and Wang, D., 2011, August. Cloud task scheduling based on load balancing ant colony optimization. In Chinagrid Conference (ChinaGrid), 2011 Sixth Annual (pp. 3-9). IEEE.
[23] Zuo, X., Zhang, G. and Tan, W., 2014. Self-adaptive learning PSO-based deadline constrained task scheduling for hybrid IaaS cloud. IEEE Transactions on Automation Science and Engineering, 11(2), pp.564-573.
[24] Pandey, S., Wu, L., Guru, S.M. and Buyya, R., 2010, April. A particle swarm optimization-based heuristic for scheduling workflow applications in cloud computing environments. In Advanced information networking and applications (AINA), 2010 24th IEEE international conference on (pp. 400-407). IEEE.
[25] Awad, A.I., El-Hefnawy, N.A. and Abdel_kader, H.M., 2015. Enhanced particle swarm optimization for task scheduling in cloud computing environments. Procedia Computer Science, 65, pp.920-929.
[26] Wang, Z., Shuang, K., Yang, L. and Yang, F., 2012. Energy-aware and revenue-enhancing Combinatorial Scheduling in Virtualized of Cloud Datacenter. Journal of Convergence Information Technology, 7(1), pp.62-70.
[27] Raju, R., Babukarthik, R.G., Chandramohan, D., Dhavachelvan, P. and Vengattaraman, T., 2013, February. Minimizing the makespan using Hybrid algorithm for cloud computing. In Advance Computing Conference (IACC), 2013 IEEE 3rd International (pp. 957-962). IEEE.
[28] Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE transactions on evolutionary computation, 1(1), 67-82.

A. Upadhyay, R. Thakur, A. Thakur , "Modern Approaches To Cloud Scheduling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1072-1079, 2018.

Distributed file system is an essential part of data intensive works as it is used as a primary storage solution. Distributed file system also provides distributed environment of processing for fast and effective processing of large set of data. Over the years, there are a number of DFS has been developed. All the DFS are designed to handle a large set of data efficiently, so that users can access the data quickly regardless of how the data are stored. For a user it becomes hard to choose one against a number of available DFSs. A thorough study about the DFSs will definitely guide users to choose their favorable DFS in their applications. In this paper, we give a brief description about GFS, HDFS and GlusterFS and then compare them on some fundamental issues of DFS such as scalability, transparency and fault tolerant.

DFS, GFS, Hadoop, HDFS, GlusterFS, EHA

[1] Snijders, C., Matzat, U., & Reips, U. D. (2012). " Big Data": big gaps of knowledge in the field of internet science. International Journal of Internet Science, 7(1),1-5
[2] Ghemawat, S., Gobioff, H., & Leung, S. T. (2003). The Google file system (Vol. 37, No. 5, pp. 29-43). ACM
[3] Shvachko, K.; Hairong Kuang ; Radia, S. ; Chansler, R. " The Hadoop Distributed File System". Published in: IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), Incline Village, NV, May 2010. Page(s): 1 - 10. E-ISBN: 978-1-4244-7153-9. Print ISBN: 978-1-4244-7152-2. INSPEC Accession Number: 11536653. D.O.I: 10.1109/MSST.2010.5496972. Link: ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6308241
[4] Borthakur, D. (2008). HDFS architecture guide. Hadoop Apache Project, 53
[5] K. Shvachko, H. Kuang, S. Radia and R.Chansler, “The Hadoop Distributed File System”, IEEE 26th Symposium on Mass Storage Systems and Technologies (MSST), (2010) May 3-7: Incline Village, USA.
[6] Claudia Hauff, “Big Data Processing”, 2013/14 Lecture 5 (Web Information Systems).
[7] An introduction to Red Hat Gluster Storage architecture. (2015). Retrieved April 20, 2018, from www.redhat.com: https://www.redhat.com/en/files/resources/en-rhst-gluster-storage-tech-detail-11395347.pdf
[8] Dean, J., & Ghemawat, S. (2008). MapReduce: simplified data processing on large clusters. Communications of the ACM, 51(1), 107-113.
[9] Shvachko, K., Kuang, H., Radia, S., & Chansler, R. (2010, May). The hadoop distributed file system. In Mass storage systems and technologies (MSST), 2010 IEEE 26th symposium on (pp. 1-10).
[10] E. B. Boyer, M. C. Broomfield, and T. A. Perrotti. GlusterFS One Storage Server to Rule Them All. echnical report, Los Alamos National Laboratory (LANL), 2012.

Anupom Chakrabarty, Chandan Kalita, "Comparative study of GFS, HDFS and GlusterFS," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.1080-1085, 2018.