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today, cloud computing technology is becoming popular because it provides on-demand services for distributed resources like databases, servers, software, infrastructure, etc. Web traffic and service provisioning is increasing day by day. Load balancing is the biggest challenge in cloud computing, which distribute the workload dynamically across the different nodes to make sure that no node is overwhelmed or underutilized. That can be considered as an optimization problem. A good load balancing must adopt its strategy to the changing environment and the types of tasks. This paper proposes a new load balancing strategy which is based on genetic algorithm. The algorithm thrives to balancing the load of the cloud infrastructure while trying minimizing the make span of a given tasks set. The proposed load balancing policy is simulated using Cloud Analyst. The results of the simulation for sample application show that the proposed algorithm surpassed the existing algorithm like Round Robin, First Come First Serve, and Stochastic Hill Climbing.

Cloud Computing, Cloud Analyst, Load balancing, Genetic algorithm

[1] R. Buyya, J. Broberg, and A. M. Goscinski, Cloud Computing: Principles and Paradigms. John Wiley & Sons, 2010.
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[3] R. Mishra, “Ant colony Optimization: A Solution of Load balancing in Cloud,” IJWesT, vol. 3, Issue. 2, pp. 33–50, 2012.
[4] B. Mondal, K. Dasgupta, and P. Dutta, “Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach,” Procedia Technology, vol. 4, pp. 783–789, 2012.
[5] B. Jana, M. Chakraborty, and T. Mandal, “A Task Scheduling Technique Based on Particle Swarm Optimization Algorithm in Cloud Environment,” in Soft Computing: Theories and Applications, pp. 525–536, 2019.
[6] F. Saeed, “Load Balancing on Cloud Analyst Using First Come First Serve Scheduling Algorithm,” in Advances in Intelligent Networking and Collaborative Systems, pp. 463–472, 2019.
[7] G. Liu and X. Wang, “A Modified Round-Robin Load Balancing Algorithm Based on Content of Request,” in 2018 5th International Conference on Information Science and Control Engineering (ICISCE), pp. 66–72, 2018.
[8] K. Dasgupta, B. Mandal, P. Dutta, J. K. Mandal, and S. Dam, “A Genetic Algorithm (GA) based Load Balancing Strategy for Cloud Computing,” Procedia Technology, vol. 10, pp. 340–347, 2013.
[9] P. Devarasetty and S. Reddy, “Genetic algorithm for quality of service based resource allocation in cloud computing,” Evol. Intel., 2019.
[10] B. Wickremasinghe, R. N. Calheiros, and R. Buyya, “CloudAnalyst: A CloudSim-Based Visual Modeller for Analysing Cloud Computing Environments and Applications,” in 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 446–452, 2010.
[11] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, “CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms,” Software: Practice and Experience, vol. 41, issue. 1, pp. 23–50, 2011.
[12] A.A. Ekre, N.M. Nimbarte, S.V. Balamwar, "An Empirical Proposition to Load Balancing Effectuate on AWS Hybrid Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.9-17, 2018
[13] Deepti Sharma, Vijay B. Aggarwal, "Dynamic Load Balancing Algorithms for Heterogeneous Web Server Clusters", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.4, pp.56-59, 2017

Tulsidas Nakrani, Dilendra Hiran, Chetankumar Sindhi, "Load Balancing Strategy based on Genetic Algorithm for Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1106-1111, 2019.

Cloud computing is a universal innovation which is spreading its root in every area of modern computing. The benefits of these services are excellent, but the data centres that run these services consume lots of energy and create a serious problem in the environment due to the increased carbon footprint. This creates the need to move to green cloud computing, that is the very important area today for researchers. Green computing provides techniques for energy management, efficient cooling, recycling, server virtualization and load balancing. We have explored potential domains to handle the issues that the development of cloud computing brings along, including underutilized resources such as conventional database management servers, processors, other servers, and cooling infrastructure. Along with the advantages, we also mentioned some of the disadvantages of the techniques. However, these impediments are not a noteworthy worry for the huge scale execution of these strategies. Once implemented, that is expected to mitigate the vitality issue and the growing carbon footprint of cloud data centres. We also discussed about a few parameters that can be utilized to calculate the energy utilization of data server and furthermore to evaluate the green energy coefficient of cloud server.

Cloud Computing, Green Computing, Load balancing, Energy Efficient

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[3] V. Vasudevan, D. Andersen, M. Kaminsky, L. Tan, J. Franklin, and I. Moraru, “Energy-efficient Cluster Computing with FAWN: Workloads and Implications,” in Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking, New York, NY, USA, pp. 195–204, 2010
[4] D. Bouley, “Estimating a Data Center’s Electrical Carbon Footprint,” p. 13.
[5] A. Jain, M. Mishra, S. K. Peddoju, and N. Jain, “Energy efficient computing- Green cloud computing,” in 2013 International Conference on Energy Efficient Technologies for Sustainability, 2, pp. 978–982, 2013
[6] T. Kaur and I. Chana, “Energy Efficiency Techniques in Cloud Computing: A Survey and Taxonomy,” ACM Comput. Surv., vol. 48, Issue. 2, pp. 22:1–22:46, 2015.
[7] J.-T. Tsai, J.-C. Fang, and J.-H. Chou, “Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm,” Computers & Operations Research, vol. 40, Issue. 12, pp. 3045–3055, 2013.
[8] Y. Zhao et al., “An Experimental Evaluation of Datacenter Workloads on Low-power Embedded Micro Servers,” Proc. VLDB Endow., vol. 9, Issue. 9, pp. 696–707, 2016.
[9] N. J. Kansal and I. Chana, “Cloud Load Balancing Techniques : A Step Towards Green Computing,” vol. 9, Issue. 1, p. 9, 2012.
[10] J. Baliga, R. W. A. Ayre, K. Hinton, and R. S. Tucker, “Green Cloud Computing: Balancing Energy in Processing, Storage, and Transport,” Proceedings of the IEEE, vol. 99, Issue. 1, pp. 149–167, 2011.
[11] “Energy‐Efficient Distributed Computing Systems - Energy‐ Efficient Distributed Computing Systems - Wiley Online Library.”
[12] M. S. Obaidat, A. Anpalagan, and I. Woungang, Handbook of green information and communication systems. Academic press, 2012.
[13] A. Ezendu, Green Technology Applications for Enterprise and Academic Innovation. IGI Global, 2014.
[14] L. Chiaraviglio, F. D’Andreagiovanni, R. Lancellotti, M. Shojafar, N. Blefari-Melazzi, and C. Canali, “An Approach to Balance Maintenance Costs and Electricity Consumption in Cloud Data Centers,” IEEE Transactions on Sustainable Computing, vol. 3, Issue. 4, pp. 274–288, 2018.
[15] S. Kaushal, D. Gogia, and B. Kumar, “Recent Trends in Green Cloud Computing,” in Proceedings of 2nd International Conference on Communication, Computing and Networking, pp. 947–956, 2019.
[16] L. Lakhani, "Green Computing - A New Trend in It", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.11-13, 2016
[17] Chingrace Guite, Kamaljeet Kaur Mangat, "A Study on Energy Efficient VM Allocation in Green Cloud Computing", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.4, pp.37-40, 2018

Tulsidas Nakrani, Dilendra Hiran, Chetankumar Sindhi, "Energy Efficient Cloud System: Steps towards Green Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1112-1116, 2019.

Viola Jones introduced an efficient method to detect the face rapidly within an image. There are different calculations and strategies utilized for face location; here Viola Jones algorithm is utilized to for face recognition in a picture. This algorithm is accustomed to recognizing and finding the human face independent of its size, circumstance and environment. The face discovery is a strategy that distinguishes the human face and disregarding whatever else, similar to trees, bodies and structures. This algorithm is utilized to discover a programmed human face structure dataset. In this paper we implement Viola Jones algorithm to detect the face, nose, mouth and eye. This paper for the most part tends to the structure of facial acknowledgment programming which falls into a huge gathering of advancements known as biometrics. It has been a standout amongst the most fascinating and significant research fields.

Viola-Jones, face detection, Haar feature, Adaboost, Integral Image

[1]. Muller, N., Magaia, L. and Herbst B.M. Singular value decomposition, eigenfaces, and 3D reconstructions. SIAM Review, Vol. 46 Issue 3, pp. 518–545. Dec. 2004.
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[3]. Viola, Jones: Robust Real-time Object Detection, IJCV 2001 See pages 1, 3.
[4]. "Viola-Jones` face detection claims 180k features" Retrieved 2017-06-27.
[5]. R.Szeliski, Computer Vision, algorithms and applications, Springer
[6]. Viola, Jones: Robust Real-time Object Detection, IJCV 2001 See page 11.
[7]. Face Detection and Tracking using the KLT algorithm
[8]. Tej Pal Singh, “Face Recognition by using Feed Forward Back Propagation Neural Network”, International Journal of Innovative Research in Technology & Science, vol.1, no.1,
[9]. N.Revathy, T.Guhan, “Face recognition system using back propagation artificial neural networks”, International Journal of Advanced Engineering Technology, vol.3, no. 1, 2012.
[10]. H.Schneiderman and T. Kanade,“A statistical method for 3D object detection applied to faces and cars,” IEEE conference on Computer vision and pattern recognition, 13-15 June 2000, Hilton Head Island, pp. 746–751.
[11]. Ling-Zhi Liao, Si-Wei Luo, and Mei Tian “Whitened faces Recognition With PCA and ICA” IEEE Signal Processing Letters, vol. 14, no. 12, pp1008-1011, Dec. 2007.
[12]. Paul Viola and Michael J. Jones. Rapid Object Detection using a Boosted Cascade of Simple Features. IEEE CVPR, 2001.
[13]. Kumaran U, Automated Real-Time Face Recognition and Tagging, IJSRCSEIT Volume 4 Issue 6 ISSN : 2456-3307,2018
[14]. Numitha M N1, Taha Noorain1, Face Recognition and IoT Based Smart Lock Access System, IJSRCSEIT | Volume 4 | Issue 6 | ISSN : 2456-3307,2018

Vivek Kumar Mishra, Ravindra Gupta, Ashvini Chaturvedi, "Face Profiler for Face Detection and Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1117-1120, 2019.

The cloud is a forefront arrange that gives dynamic resource pools, virtualization, and high openness. Today, it can utilize versatile, passed on handling conditions inside the limits of the Web, a preparation known as circulated registering. Circulated processing is the thought realized to interpret the step by step enrolling issues, inclinations of hardware programming and resource availability unhurried by PC customers. The circulated figuring gives an undemanding and non-unable response for step by step enlisting. Winning cloud systems in a general sense revolve around finding an amazing response for the advantage the executives. In disseminated processing, the examination of money related parts of the cloud is in a general sense basic. The enhancement of advantage is done in this. For enhancing the advantage initially ought to appreciate the cost and pay. Advantage intensification must consider the customer satisfaction moreover the cost of the cloud fuses the renting cost and power use cost. For enlarging, must reduce the cost. For this it will plan the server perfectly. For structuring the server, figure the normal holding up time and organization charge is resolved. Using the propelling methodology, will streamline the speed and the size so get most noteworthy advantage.

Cloud computing, Pricing model, load balancing

[1] Gemma Reig, Javier Alonso, and Jordi Guitart “Prediction Of Job Resource Requirements For Deadline Schedulers to Manage High-Level SLAs On The Cloud”, 2010 NinthIEEE International Symposium on Network Computing andApplications.
[2] Mayank Mishra, Anwesha Das, Purushottam Kulkarni, and AnirudhaSahoo, IIT Bombay”Dynamic ResourceManagement Using Virtual Machine Migrations,” International journal in cloud computing.
[3] M. Armbrustet al., “A View of Cloud Computing,” Common. ACM, vol. 53, no. 4, 2010, pp. 50–58.
[4] JunweiCao, Kai Hwang, Keqin Lin, Albert Y.Zamaya ”Optimal Multiserver Configuration for profitMaximization in cloud computing ,” ieee transactions on parallel and distributed systems, vol. 24, no. 6, June 2013
[5] Pankesh Patel, AjithRanabahuAmitSheth, “ServiceLevel Agreement in Cloud Computing,” Knoesis Center, Wright State University, USA.
[6] Saurabh Kumar Garg, Rajkumar Buyya1 and H. J. Siegel” Scheduling Parallel Application OnUtility Grids: Time And Cost Trade-off management”Management” The University of MelbouneVictoria 3010, Australia.
[7] Qian Zhu, Student Member, IEEE, and GaganAgrawal, Senior Member, IEEE, “Resource Provisioning with BudgetConstraints for Adaptive Applications in CloudEnvironments,” ieee transactions on services computing, vol. 5, no. 4, october-december 2012.

Borra Sushma Rekha, K. Mohan Krishna, "Profit Maximization on the Premise of saving Costs for Users in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1121-1126, 2019.

Vehicular Ad-hoc Networks (VANETs) are special type of networks which are having wireless mobile vehicle nodes that are establish temporarily network connectivity. These vehicle nodes perform routing functions under the self-organization. Delay Tolerant Network (DTN) follows the approach to store and forward the message. DTN are the networks do not require the immediate data delivery and these type of networks can wait for a specific time period before transferring of data. Different kind of routing protocols have been designed and presented by the researchers after considering the major challenges that are involved in DTN enabled VANETs. In this paper an algorithm is proposed called Multi Category ERDV (MC ERDV) Algorithm. This proposed algorithm gives the low delay and network area is enhanced.

VANET, DTN, ERDV-FS, Multi Category ERDV, MCE Algorithm

[1] Manjunath P S, Narayana Reddy, “A Review on Security and Challenges for Vehicular Ad Hoc Networks”, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 5, May 2014.
[2] Arun Kumar, “Enhanced Routing in Delay Tolerant Enabled Vehicular Ad Hoc Networks”, International Journal of Scientific and Research Publications, Volume 2, Issue 9, September 2012.
[3] Anas Abu Taleb, “VANET Routing Protocols and Architectures: An Overview”, Journal of Computer Science, Journal of Computer Science 2018, 14 (3): 423.434, DOI: 10.3844/jcssp.2018.423.434.
[4] Wenshuang Liang, Zhuorong Li, Hongyang Zhang, Shenling Wang, and Rongfang Bie, “Vehicular Ad Hoc Networks: Architectures, Research Issues, Methodologies, Challenges, and Trends”, Hindawi Publishing Corporation, International Journal of Distributed Sensor Networks, Volume 2015.
[5] Gurpreet Singh, Seema, “Malicious Data Detection in VANET”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 1, Issue 7, September 2012
[6] Vijander Singh, “Minimize End-To-End Delay in Delay Tolerant Enabled Vehicular Ad-Hoc Networks Using Ferry Selection Approach”, International Journal for Research in Applied Science & Engineering, Technology (IJRASET), Volume 5, Issue V, May 2017.
[7] Harshad S. Modi, Nikhil Kumar Singh, “Survey of Routing in Delay Tolerant Networks”, International Journal of Computer Applications (0975 – 8887), Volume 158, No 5, January 2017.
[8] J. Burgess, B. Gallagher, D. Jensen, and B. N. Levine, “MaxProp: routing for vehicle-based disruption-tolerant networks”, Proceedings of the 25th IEEE International Conference on Computer Communications (INFOCOM ’06), vol. 6, pp. 1–11, Barcelona, Spain, April 2006.
[9] M. J. Khabbaz, W. F. Fawaz, and C. M. Assi, “Probabilistic bundle relaying schemes in two-hop vehicular delay tolerant networks,” IEEE Communications Letters, vol. 15, no. 3, pp. 281– 283, 2011.
[10] D. Wu, G. Zhu, and D. Zhao, “Adaptive carry-store forward scheme in two-hop vehicular delay tolerant networks,” IEEE Communications Letters, vol. 17, no. 4, pp. 721–24, 2013.
[11] D. Yu and Y.-B. Ko, “FFRDV: fastest-ferry routing in DTN enabled vehicular Ad Hoc networks,” Proceedings of the 11th International Conference on Advanced Communication Technology (ICACT ’09), pp. 1410–1414, February 2009.
[12] W. Z. Lo, J. S. Gao, and S.C. Lo, “Distance-aware routing with copy control in vehicle-based DTNs,” in Proceedings of the IEEE 75th Vehicular Technology Conference (VTC ’12), pp. 1–5, IEEE, June 2012.
[13] Q. Fu, L. Zhang, W. Feng, and Y. Zheng, “DAWN: a density adaptive routing algorithm for vehicular delay tolerant sensor networks,” Proceedings of the 49thAnnualAllerton Conference on Communication, Control, and Computing, pp. 1250–1257, Monticello, Ill, USA, 2011.
[14] I. Leontiadis and C. Mascolo, “GeOpps: geographical opportunistic routing for vehicular networks,” Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM’07), pp. 1–6, June 2007.
[15] F. Warthman, “Delay-Tolerant Networks (DTNs) A Tutorial”, DTN Research Group Internet Draft, 2003.
[16] Marziyeh Barootkar, Akbar Ghaarpour Rahbar, and Masoud Sabaei, “LDAOR Location and Direction Aware Opportunistic Routing in Vehicular Ad hoc Networks”, Journal of Telecommunication and Information Technology, 1, 2016.
[17] Moath Muayad Al-Doori, Francois Siewe, and Ali Hilal Al-Bayatti, “Moath Muayad Al-Doori, Francois Siewe, and Ali Hilal Al-Bayatti”, International Journal of Machine Learning and Computing, Vol. 1, No. 5, December 2017.
[18] Vijander Singh, “Efficient Routing by Minimizing End to End Delay in Delay Tolerant Enabled VANETs”, International Bulletin of Mathematical Research, Volume 2, Issue 1, March 2015.
[19] Xiang Ji, Huiqun Yu , Guisheng Fan, Huaiying Sun, and Liqiong Chen , “Efficient and Reliable Cluster-Based Data Transmission for Vehicular Ad Hoc Networks”, Hindawi Mobile Information Systems, Volume 2018, Article ID 9826782, 15 pages, 2018.
[20] Ji, Xiang, HuiQun Yu, GuiSheng Fan, and WenHao Fu, "SDGR: An SDN-based Geographic Routing Protocol for VANET" In Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), 2016 IEEE International Conference on, pp. 276-281. IEEE, 2016.
[21] Datta, Atreyee, "Modified Ant-AODV-VANET routing protocol for Vehicular Adhoc Network." In Electronics, Materials Engineering and Nano-Technology (IEMENTech), 2017 1st International Conference on, pp. 1-6. IEEE, 2017.
[22] Motlagh, Naser Hossein, Tarik Taleb, and Osama Arouk, "Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives", IEEE Internet of Things Journal 3, no. 6, 899-922.2016.
[23] Abuashour, Ahmad, and Michel Kadoch, "Control Overhead Reduction in Cluster-Based VANET Routing Protocol", In Ad Hoc Networks, pp. 106-115, Springer, Cham, 2018.
[24] Cirne, Pedro, André Zúquete, and Susana Sargento, "TROPHY: Trustworthy VANET routing with group authentication keys", Ad Hoc Networks 71, 45-67, 2018.
[25] Singh, Gagan Deep, Ravi Tomar, Hanumat G. Sastry, and Manish Prateek, "A Review on VANET Routing Protocols and Wireless Standards", In Smart Computing and Informatics, pp. 329-340, Springer, Singapore, 2018.
[26] Vijander Singh, Reena Dadhich, “Routing Optimization by Minimizing One-Way Delay in Delay Tolerant Enabled Vehicular Ad-hoc Networks using Ferry Selection Approach”, International Conference on Infocom Technologies and Unmanned Systems (ICTUS`2017), Dec. 18-20, 2017, ADET, Amity University Dubai, UAE, IEEE , 2017.
[27] Kanwalprit Singh1, Harmanpreet kaur, “Evaluation of proposed technique for detection of Sybil attack in VANET”, International Journal of Scientific Research in Computer Science and Engineering Vol.6, Issue.5, pp.11-15, October 2018.
[28] R. Kumari, P. Nand, “Performance Analysis for MANETs using certain realistic mobility models: NS-2”, International Journal of Scientific Research in. Computer Science and Engineering Vol.6, Issue.1, pp.70-77, February 2018.

Shiva Katara, Rakesh Rathi, "Enhancing Performance of Vehicular Adhoc Network by reducing Delay with MC-ERDV Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1127-1130, 2019.

In an everyday life, the limit of information expanded hugely with time. The development of information which will be unmanageable in person to person communication destinations like Facebook, Twitter. In the previous two years the information stream can increment in zettabyte. To deal with huge information there are number of uses has been produced. Nonetheless, investigating huge information is an exceptionally difficult errand today. Enormous Data alludes to activities and advances that include information that is excessively assorted, fast changing or immense for traditional innovations, aptitudes and framework to address productively. The present foundation to deal with enormous information isn`t effective as a result of information limit. The handling of huge information issue can be illuminated by utilizing MapReduce strategy. The effective usage of MapReduce show requires parallel handling and arranged joined capacity. Hadoop and Hadoop Distributed File System (HDFS) by apache are normally utilized for putting away and overseeing huge information. In this exploration work we recommend diverse strategies for taking into account the issues close by through MapReduce.

MapReduce; Big Data; Zettabyte; Hadoop; Hadoop Distributed File System

[1] Jianqing Fan1, Fang Han and Han Liu, Challenges of Big Data analysis, National Science Review Advance Access published February, 2014.
[2] Lee, D., Kim J-S. & Maeng, S. (2013) A Large-scale incremental processing with MapReduce. FutureGeneration Computer System, 36, pp 66-79.
[3] VinayakBorkar, Michael J. Carey, Chen Li, Inside “Big Data Management”: Ogres, Onions, or Parfaits?, EDBT/ICDT 2012 Joint Conference Berlin, Germany,2012 ACM 2012, pp 3-14.
[4] Jiang, D., Tung, A. & Chen, G. (2011) MAP-JOIN-REDUCE: Toward Scalable and Efficient Data Analysis on Large Clusters. IEEE Transactions on Knowledge and Data Engineering, 23(9), 1299-1311.
[5] Gu, R., Yang, X., Yan, J., Sun, Y., Wang, B., Yuan, C. & Huang, Y. (2014) SHadoop: Improving MapReduce Performance by Optimizing Job Execution Mechanism in Hadoop Clusters. Journal of Parallel and Distributed Computing, 74(3), 2166-2179.
[6] Afrati, F.N. & Ullman, J.D. (2011) Optimizing Multiway Joins in a Map-Reduce Environment. IEEE Transactions on Knowledge and Data Engineering, 23(9), 1282-1298.
[7] Y. Yuan, Y. Wu, X. Feng, J. Li, G. Yang, W. Zheng, “VDB-MR: MapReduce-based distributed data integration using virtual database”, Future Generation Computer Systems 26 (2010) 1418–1425.
[8] Hadoop: open source implementation of MapReduce,http://hadoop.apache.org/mapreduce/>.
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[11] Hadoop,“PoweredbyHadoop,”http://wiki.apache.org/hadoop/PoweredBy.
[12] Bakshi, K. (2012) Considerations for Big Data: Architecture and Approach. IEEE AerospaceConference,(pp.1-7).Big Sky,USA.
[13] Hadoop Tutorial, YahooInc., https://developer.yahoo.com/hadoop /tutorial/index.html
[14] Apache: Apache Hadoop, http://hadoop.apache.org.
[15] Hadoop Distributed File System (HDFS),http://hortonworks.com/hadoop/hdfs/.

S. Srilakshmi, CH. Mallikarjuna Rao, "Big Data Performance Evaluation in Hadoop Eco System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1131-1135, 2019.

Fault tolerance mechanism employed in the cloud is state of the art problem to ensure reliability of cloud. The proposed work increases reliability of cloud by using swarm optimization based vm allocation policy. Sole PSO approach also selects optimal virtual machine for load allocation but once virtual machine has been selected than that vm is maintained within vm list at top place. Although vm resources may be less due to allocation hence starvation could be the problem. This problem is rectified using dynamic VM selection policy in which number of vm to be selected is reduced as vm at every phase is changed and hence less migration and downtime is obtained. In addition execution time and energy consumed is also affected by dynamic PSO approach.

Fault Tolerance, PSO, Consolidation, Energy Efficient

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[20]. Zhou A, Wang S, Cheng B, Zheng Z, Yang F, Chang R, Lyu M, Buyya R (2016) Cloud Service Reliability Enhancement via Virtual Machine Placement Optimization. IEEE Trans Serv Comput XX:1–1 . doi: 10.1109/TSC.2016.2519898

Manjot Kaur, Kamaljit Kaur, "Dynamic Swarm Based Virtual Machine Selection for Optimizing Execution Time and Fault Tolerance Rate," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1136-1147, 2019.

Now-a-days data mining is widely used in the medical field for analysis and diagnosis of disease. Various techniques such as clustering, classification, association of data mining are used to disclose unseen patterns from large number of datasets. Data mining techniques are applied on incompetent medical data recorded on daily basis. These techniques help to get useful information for diagnosing the diseases. Generally, numbers of tests are required to know the presence of a disease. In order to reduce these numbers of tests, data mining is utilized. In this paper, benign and malignant type of data for breast cancer disease has been used in which Benign tumour is non-cancerous tumour and malignant is cancerous tumour. In this research, two approaches are implemented in MATLAB for disease prediction. The first approach is based on k-means clustering and SVM algorithm for classification algorithm. In second approach, improved k-means clustering algorithm and SVM algorithm is implemented. The second approach gives better performance in terms of accuracy. Accuracy of classification of dataset depends upon the optimization of clustering and pre-processing of dataset.

Data Mining, K-means, SVM

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[2] Nisha, P.J. Kaur, “A Survey of Clustering Techniques and Algorithms,” In the Proceedings of the 2015 International Conference on Computing for Sustainable Global Development, New Delhi, India, pp.304–307, 2015.
[3] P. Manivannan, P.I. Devi, “Dengue fever prediction using K-means clustering algorithm,” In the Proceedings of the 2017 IEEE International Conference Intelligent Techniques in Control, Optimization and Signal Processing, Srivilliputhur, India, pp.1-5, 2018.
[4] V. Jain, “Outlier Detection Based on Clustering Over Sensed Data Using Hadoop”, International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.2, pp.45-50, 2013.
[5] P.S. Bishnu, V. Bhattacherjee, “A dimension reduction technique for K-Means clustering algorithm,” In the Proceedings of the 2012 1st International Conference on Recent Advances in Information Technology, Dhanbad, India, pp.531–535, 2012.
[6] J. Meena, A. Mandloi, “Classification of Data Mining Techniques for Weather Prediction”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.1, pp.21-24, 2016.
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[8] M.M. Muzakki and F. Nhita, “The spreading prediction of Dengue Hemorrhagic Fever (DHF) in Bandung regency using K-means clustering and support vector machine algorithm,” In the Proceedings of the 2018 6th International Conference on Information and Communication Technology, Bandung, Indonesia, pp.453–458, 2018.
[9] S. Babu et al., “Heart disease diagnosis using data mining technique,” In the Proceedings of the 2017 International Conference of Electronics, Communication and Aerospace Technology, Coimbatore, India, pp.750–753, 2017.
[10] M. Shankar, M. Pahadia, D. Srivastava, T.S. Ashwin, and G.R.M. Reddy, “A Novel Method for Disease Recognition and Cure Time Prediction Based on Symptoms,” In the Proceedings of the 2015 2nd Internationl Conference on Adavnces in Computing and Commuication Engineering, Dehradun, India, pp.679–682, 2015.
[11] N. Chetty, K. S. Vaisla, and N. Patil, “An Improved Method for Disease Prediction Using Fuzzy Approach,” Proc. - 2015 2nd International Conference on Advances in. Computing Communication Engineering, Dehradun, India, pp.568–572, 2015.
[12] T.U. Mane, “Smart heart disease prediction system using Improved K-means and ID3 on big data,” 2017 International Conference on Data Management, Analytics and Innovation, Pune, India, pp.239–245, 2017.

C. Kaur, K. Sharma, A.K. Sohal, "Disease Prediction System using Improved K-means Clustering Algorithm and Machine Learning," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1148-1153, 2019.

A phylogenetic tree is a tree that shows the transformative similarity and dissimilarity among various biological species. The biological species may be human species or bacterial species. The comparative analysis of phylogenetic tree is useful in various areas. In this paper phylogenetic tree is constructed for various bacterial species of Rhizobium by using MEGA7 software. MEGA is molecular evolutionary genetics analysis user friendly software for framing sequence alignments and phylogenetic tree construction. This paper also infer us about the how different algorithms like UPGMA, Neighbour joining are implemented effectively on the bacterial species of Rhizobium.

UPGMA,Neighbour joining,Phylogenetic tree

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A. Sharma, R.S. Thakur, S. Jaloree, "Phylogenetic Tree Construction of Bacterial Species using Clustering Algorithms In MEGA 7," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1154-1157, 2019.

The Internet of Things is a rapidly growing concept in recent years. Safety and security have traditionally been distinct problems in engineering and computer science. With a massive amount of devices connected to the Internet and huge data associated in it, there is major concern of security of services. Many traditional security solutions including existing access control mechanisms may not be directly applicable in the IoT environment. Traditional access control approaches are not suitable to the decentralized and dynamic scenario in IoT as well. Various cryptographic algorithms developed which addresses security in Internet, but their use in IOT is questionable as the hardware devices used deal in IOT is not suitable for computationally expensive encryption algorithms. Particularly, the need arises for a dynamic and fine-grained access control algorithm on constrained resources. The paper summaries access control approaches that try to improve security in devices. Paper not only summarizes access control approaches, but also provides an understanding of the limitations and open issues of the existing work.

Internet of Things (IoT), Access Control, Attribute-Based Access Control (ABAC), Role-Based Access Control (RBAC), Capability-Based Access Control (CapBAC)

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[2] Yunpeng Zhang, Xuqing Wu, “Access Control in Internet of Things: A Survey”, 2017 Asia-Pacific Engineering and Technology Conference (APETC 2017) ISBN: 978-1-60595-443-1
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[6] Guoping Zhang, Jiazheng Tian, “An Extended Role Based Access Control Model for the Internet of Things”, 2010 International Conference on Information, Networking and Automation (ICINA)
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for Perception Layer of Internet of Things”,Appl. Math. Inf. Sci. 8, No. 4, 1-8 (2014)
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in the Internet of Things”, JOURNAL OF SOFTWARE, VOL. 6, NO. 4, APRIL 2011
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[15] Mousa Alramadhan, Kewei Sha, “An Overview of Access Control Mechanisms for Internet of Things”, 978-1-5090-2991-4/17 IEEE 2017.
[16] M. Dahiya, A.Sangwan, and “A Review Paper on Various Attacks on Wireless Sensor Networks” International Journal of Computer Sciences and Engineering Vol. 7(4), Apr 2019, E-ISSN: 2347-2693
[17] K.Amutha, V.Vallinayagi, “E-security Through RFID”, International Journal of Computer Sciences and Engineering Vol. 7(8), Apr 2019, E-ISSN: 2347-2693
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Meghana P.Lokhande, Dipti Durgesh Patil, "Access Control Approaches in Internet of Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1158-1161, 2019.