i-manager Publications

i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

An OptiAssign-PSO Based Optimisation for Multi-Objective Multi-Level Multi-Task Scheduling in Cloud Computing Environment
Advanced Analytics for Disease Tracking & Remote Intravenous Injection Monitoring
DevKraft - Fueling Collaboration in Coding Challenges
Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions
Blockchain Healthcare Management using Patients

Most Cited

Volume 7 Issue 1 January - June 2020

Research Paper

Cloud Computing Technology for Digital Economy

Bommala Harikrishna * , S. Kiran**

* Department of Computer Science and Engineering, KG Reddy College of Engineering & Technology, Moinabad, Telangana, India.

** Department of Computer Science and Engineering, YSR Engineering College, Proddatur, Andhra Pradesh, India.

Harikrishna, B., and Kiran, S. (2020). Cloud Computing Technology for Digital Economy. i-manager's Journal on Cloud Computing, 7(1), 1-11. https://doi.org/10.26634/jcc.7.1.17307

Abstract

The society is getting more and more electronic every day, as the usage of the hard currency from their pockets is now reduced to the e-cards or online. In India, the flow of money from a person pocket has changed in the recent decades. This paper’s main aim is using cloud computing technology for the cashless economy in India by strong bond and relationship with the cloud computing technology and cashless economy. The scope of advanced research technology by using cloud computing technology in the current financial system, block chain technology should be implemented, as it has leverage over crypto currency that can help counteract money laundering and terrorist funding in the country and around the world to accomplish the cashless economy in India. The statistical report relayed that the present online banking transaction system is highly secure enforcing about 15% of people's transaction in India.

References

[1]. Abiodun, O. O., Sunday, A. I., Chukwudi, O. A., Phalecs, J., Blessing, O., & Omozuanvbo, A. C. (2013). Achieving cashless economy in Nigeria: The cloud computing approach and imperative of Nigeria's CBN for effective coordination. International Journal of Multidisciplinary Sciences and Engineering, 4(6), 20-26.

[2] Bellamy, E. (1894). Looking Backward. London: William Reeves.

[3]. Civilsdaily (2015). Cashless Society – Digital Payments, Demonetization, etc. https://www.civilsdaily.com/story/ cashless-society/

[4]. Durden, T. (2015). What a Cashless Society Would Look Like. https://www.zerohedge.com/news/2015-05-19/ cashless-society

[5]. Krishna, B. H., Kiran, S., Murali, G., & Reddy, R. P. K. (2016). Security issues in service model of cloud computing environment. Procedia Computer Science, 87, 246-251.

[6]. Peck, M. (2017). Reinforcing the links of the Blockchain. IEEE Future Directions Blockchain Initiative White Paper. https://blockchain.ieee.org/images/files/pdf/ieee-futuredirections- blockchain-white-paper. pdf

[7]. Rahman, A. (2012). Adding cybercrime pandemic in the cashless society using HMM. International Journal of Computer Applications (IJCA), 60(7), 35-43.

[8]. RBI. (2016). Payment and Settlement Systems in India. Reserve Bank of India., New Delhi, India. https://rbidocs. rbi.org.in/rdocs/PublicationReport/Pdfs/VISION20181A8972 F5582F4B2B8B46C5B669CE396A.PDF

[9]. The Telegraph Online (2019). Stage Set for Experiment in Digital Rupee. https://www.telegraphindia.com/india/ stage-set-for-experiment-in-digital-rupee/cid/ 1695047

Full Article (HTML)

Pdf

Research Paper

Trusted Hybrid Multifactor Authentication for Cloud Users

K. Devi Priya* , L. Sumalatha **

*,** Department of Computer Science and Engineering, JNTUK University, Kakinada, Andhra Pradesh, India.

Priya, K. D., and Sumalatha, L. (2020). Trusted Hybrid Multifactor Authentication for Cloud Users. i-manager's Journal on Cloud Computing, 7(1), 12-20. https://doi.org/10.26634/jcc.7.1.16670

Abstract

Cloud Computing is an advance technology that provides different services to the users on the rental basis. The main issue associated with the cloud is cyber attacks that are performed on the cloud services due to the improper security mechanisms especially authentication. In this paper, a multi level security using hybrid multifactor authentication methodologies based on the security questions, mobile OTP, and biometric authentication in addition to password based authentication to protect the data/services from the cyber attackers is proposed. The hybrid methodologies provides strong and secure authentication due to the two layer security. The biometric authentication includes face image as an authentication factor and the extraction of the face features are performed using VGG face model with the concept of transfer learning. The proposed work is implemented in the Google Colab cloud with python API and evaluated results show the proposed scheme providing privacy, confidentiality, integrity, and resistivity against the collision resistance attack, replay attack, tampering of the data, etc.

References

[1]. Abu-Libdeh, H., Princehouse, L., & Weatherspoon, H. (2010, June). RACS: A case for cloud storage diversity. In Proceedings of the 1^st ACM symposium on Cloud computing (pp. 229-240). https://doi.org/10.1145/1807 128.1807165

[2]. Appel, A. W. (2015). Verification of a cryptographic primitive: SHA-256. ACM Transactions on Programming Languages and Systems (TOPLAS), 37(2), 1-31.

[3]. ARN. (2020). Top 10 Most Notorious Cyber Attacks in History. https://www.arnnet.com.au/ slideshow/341113/top- 10-most-notorious-cyber-attacks-history/

[4]. Banyal, R. K., Jain, P., & Jain, V. K. (2013, September). Multi-factor authentication framework for cloud computing. In 2013, Fifth International Conference on Computational Intelligence, Modelling and Simulation (pp. 105-110). IEEE. https://doi.org/10.1109/CIMSim.2013.25

[5]. Bisong, E. (2019). Building Machine Learning and Deep Learning Models on Google Cloud Platform (pp. 59-64). Berkeley, CA: Apress

[6]. Bowers, K. D., Juels, A., & Oprea, A. (2009, November). HAIL: A high-availability and integrity layer for cloud storage. In Proceedings of the 16^th ACM Conference on Computer and Communications Security (pp. 187-198). ACM.

[7]. Brownlee, J. (2019). How to Perform Face Recognition With VGGFace2 in Keras. Machine Learning Mastery. https://machinelearningmastery.com/how-to-performface- recognition-with-vggface2-convolutional-neuralnetwork- in-keras/

[8]. Devkota, D., Ghimire, P., Burris, J., & Alkadi, I. (2015, March). Comparison of security algorithms in cloud computing. In 2015, IEEE Aerospace Conference (pp. 1-7). IEEE.

[9]. Dooley, R., Edmonds, A., Hancock, D. Y., Lowe, J. M., Skidmore, E., Adams, ... Knepper, R. (2018). Security Best Practices for Academic Cloud Service Providers. Bloomington, Indiana: Indiana University Scholar Works.

[10]. Feng, J., Chen, Y., Summerville, D., Ku, W. S., & Su, Z. (2011, January). Enhancing cloud storage security against roll-back attacks with a new fair multi-party non-repudiation protocol. In 2011, IEEE Consumer Communications and Networking Conference (CCNC) (pp. 521-522). IEEE.

[11]. Kim, Y., Kim, I., & Park, N. (2014). Analysis of cyber attacks and security intelligence. In Mobile, Ubiquitous, and Intelligent Computing (pp. 489-494). Heidelberg, Berlin: Springer. https://doi.org/10.1007/978-3-642-40675- 1_73

[12]. Lee, S., Ong, I., Lim, H. T., & Lee, H. J. (2010). Two factor authentication for cloud computing. Journal of Information and Communication Convergence Engineering, 8(4), 427- 432.

[13]. Mashhadi, N. (2015). Authentication in mobile cloud computing by combining the tow factor authentication and one time password token. Ciência e Natura, 37, 220-229.

[14]. Padma, P., & Srinivasan, S. (2016, August). A survey on biometric based authentication in cloud computing. In 2016, International Conference on Inventive Computation Technologies (ICICT) (Vol. 1, pp. 1-5). IEEE. https://doi.org /10.1109/INVENTIVE.2016.7823273

[15]. Pawle, A. A., & Pawar, V. P. (2013). Face recognition system (FRS) on cloud computing for user authentication. International Journal of Soft Computing and Engineering (IJSCE), 3(4), 189-192.

[16]. Pawle, A. A., & Pawar, V. P. (2017). A Study of Different Biometric Authentication Techniques in Cloud Computing. International Journal of Engineering Research & Technology (IJERT), 6(05), 612-619.

[17]. Rimal, B. P., Choi, E., & Lumb, I. (2009, August). A taxonomy and survey of cloud computing systems. In 2009, Fifth International Joint Conference on INC, IMS and IDC (pp. 44-51). IEEE.

[18]. Serengil, S. I. (2018). Deep Face Recognition with Keras. https://sefiks.com/2018/08/06/deep-face-recognition-withkeras/

[19]. Singh, A., & Malhotra, M. (2015). Security concerns at various levels of cloud computing paradigm: A review. International Journal of Computer Networks and Applications, 2(2), 41-45.

[20]. Soni, S. K., Likhare, D., & Choudhary, A. (2016). Review of authentication mechanisms in cloud computing. International Journal of Modern Engineering & Management Research, 4(2), 38-46.

[21]. Zeng, W., Zhao, Y., Ou, K., & Song, W. (2009, November). Research on cloud storage architecture and nd key technologies. In Proceedings of the 2 International Conference on Interaction Sciences: Information Technology, Culture and Human (pp. 1044-1048). ACM.

[22]. Zhang, K., Zhang, Z., Li, Z., & Qiao, Y. (2016). Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Processing Letters, 23(10), 1499-1503.

[23]. Zhou, X., & Tang, X. (2011, August). Research and implementation of RSA algorithm for encryption and decryption. In Proceedings of 2011 6^th International Forum on Strategic Technology (Vol. 2, pp. 1118-1121). IEEE.

Full Article (HTML)

Pdf

Review Paper

Green Computing Model on Clouds

Daisy Sharmah*

Department of Computer Science & Electronics, University of Science & Technology, Meghalaya, India.

Sharmah, D. (2020). Green Computing Model on Clouds. i-manager's Journal on Cloud Computing, 7(1), 21-25. https://doi.org/10.26634/jcc.7.1.16956

Abstract

The world today is almost working on machines which are releasing hazardous gases and cloud is mainly working on the machine environment. The users of Cloud computing are gradually increasing due to its key technology in the computing environment. The Cloud Computing Technology deploys enormous elegant and sophisticated computers which extricates poisonous particles into the environment. For this reason, a design is needed to focus on the features on making cloud energy-efficient concerning the power efficiency. In this paper, some of the algorithms are proposed to overcome the problem.

References

[1]. Agarwal, S. (2014). Impact of Green Computing in IT industry to make eco friendly environment. Journal of Global Research in Computer Science, 5(4), 05-10.

[2]. Agarwal, S., Basu, K., & Nath, A. (2013). Green Computing and Green Technology based teaching learning and administration in Higher Education Institutions. International Journal of Advanced Computer Research, 2(3), 295-303.

[3]. Ahmed, A. E. I. (2015). Integrated Framework For Green ICT: Energy Efficiency by Using Effective Metric and Efficient Techniques For Green Data Centres. https:// repository.sustech.edu/handle/123456789/ 11405

[4]. Atrey, A., Jain, N., & Iyengar, N. (2013). A study on green cloud computing. International Journal of Grid and Distributed Computing, 6(6), 93-102. https://doi.org/10.14 257/ijgdc.2013.6.6.08

[5]. Cloud Computing. (2017). Wikipedia, The Free Encyclopedia. https://simple.wikipedia.org/w/index.php? title=Cloud_computing&oldid=7112121

[6]. Diouani, S., & Medromi, H. (2018). Green cloud computing: Efficient energy-aware and dynamic resources management in data centers. International Journal of Advanced Computer Science and Applications, 9(7), 124-127.

[7]. Garg, S. K., & Buyya, R. (2012). Green cloud computing and environmental sustainability. In Harnessing Green IT: Principles and Practices, (pp. 315-340), UK: John Wiley & Sons.

[8]. Lakshmi, S. V. S. S., Sarwani, I. S. L., & Tuveera, M. N. (2012). A study on green computing: the future computing and eco-friendly technology. International Journal of Engineering Research and Applications (IJERA), 2(4), 1282- 1285.

[9]. Pandya, S. S. (2014). Green cloud computing. International Journal of Information and Computation Technology, 4(4), 431-436.

[10]. Radu, L. D. (2017). Green cloud computing: A literature survey. Symmetry, 9(12), 1-20. https://doi.org/10. 3390/sym9120295

[11]. Sahoo, J., Mohapatra, S., & Lath, R. (2010, April). Virtualization: A survey on concepts, taxonomy and associated security issues. In 2010, Second International Conference on Computer and Network Technology (pp. 222-226). IEEE.

[12]. Saxena, S., & Saxena, D. (2016). Green cloud computing architecture with efficient resource allocation system. International Journal of Trends in Research and Development, 3(6), 248-251.

[13]. Usvub, K., Farooqi, A. M., & Alam, M. A. (2017). Edge up green computing in cloud data centers. International Journal of Advanced Research in Computer Science, 8(2), 61-67.

Full Article (HTML)

Pdf

Survey Paper

Survey on Consensus Algorithms in Blockchain for Secure Data Sharing

Thirumalaimuthu T. Ramanathan*

Information Science and Technology, Multimedia University, Melaka, Malaysia.

Ramanathan, T. T. (2020). Survey on Consensus Algorithms in Blockchain for Secure Data Sharing. i-manager's Journal on Cloud Computing, 7(1), 26-31. https://doi.org/10.26634/jcc.7.1.17114

Abstract

Blockchain technology is one of the latest innovations that provide a secured, decentralized, distributed database of records of all transaction and digital events. In recent years, blockchain technology is being applied in many powerful applications such as financial services, smart contracts, healthcare, supply chain management, e-voting etc. As blockchain ensures high security, it is being trusted and accepted by many business organizations. Consensus algorithms are the primary component of blockchain technology that maintains integrity and security in blockchain. This paper reviews various blockchain based data sharing architectures and their consensus algorithms that were proposed in the recent years for data sharing security. The importance of consensus algorithms in blockchain, and their advantages and disadvantages are discussed in this paper.

References

[1]. Androulaki, E., Barger, A., Bortnikov, V., Cachin, C., Christidis, K., De Caro, A., ... & Muralidharan, S. (2018, April). Hyperledger fabric: A distributed operating system for permissioned blockchains. In Proceedings of the Thirteenth Eurosys Conference (pp. 1-15). https://doi.org/ 10.1145/3190508.3190538

[2]. Calvaresi, D., Mattioli, V., Dubovitskaya, A., Dragoni, A. F., & Schumacher, M. (2018, December). Reputation management in multi-agent systems using permissioned blockchain technology. In 2018, IEEE/WIC/ACM International Conference on Web Intelligence (WI) (pp. 719-725). IEEE.

[3]. Chen, Y., Ding, S., Xu, Z., Zheng, H., & Yang, S. (2019). Block chain-based medical records secure storage and medical service framework. Journal of Medical Systems, 43(1), 1-9. https://doi.org/10.1007/s10916-018-1121-4

[4]. Fan, K., Wang, S., Ren, Y., Li, H., & Yang, Y. (2018). Medblock: Efficient and secure medical data sharing via blockchain. Journal of Medical Systems, 42(8), 1-11. https://doi.org/10.1007/s10916-018-0993-7

[5]. Kang, J., Yu, R., Huang, X., Wu, M., Maharjan, S., Xie, S., & Zhang, Y. (2018). Blockchain for secure and efficient data sharing in vehicular edge computing and networks. IEEE Internet of Things Journal, 6(3), 4660-4670.

[6]. Li, H., Zhu, L., Shen, M., Gao, F., Tao, X., & Liu, S. (2018). Blockchain-based data preservation system for medical data. Journal of Medical Systems, 42(8), 1-13. https://doi. org/10.1007/s10916-018-0997-3

[7]. Liang, X., Zhao, J., Shetty, S., Liu, J., & Li, D. (2017, October). Integrating blockchain for data sharing and collaboration in mobile healthcare applications. In 2017, IEEE 28^th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC) (pp. 1-5). IEEE.

[8]. Nakamoto, S. (2019). Bitcoin: A peer-to-peer electronic cash system. Manubot. https://bitcoin.org/bitcoin.pdf.

[9]. Noh, S. W., Park, Y., Sur, C., Shin, S. U., & Rhee, K. H. (2017). Blockchain-based user-centric records management system. International Journal of Control Automation, 10(11), 133-144.

[10]. Patel, V. (2019). A framework for secure and decentralized sharing of medical imaging data via block chain consensus. Health Informatics Journal, 25(4), 1398- 1411.

[11]. Penard, W., & van Werkhoven, T. (2008). On the secure hash algorithm family. In Gerald Tel (Ed) Cryptography in Context, (pp. 1-18). https://webspace. science.uu.nl/~tel00101/liter/Books/CrypCont.pdf

[12]. Shafagh, H., Burkhalter, L., Hithnawi, A., & Duquennoy, S. (2017, November). Towards blockchainbased auditable storage and sharing of IoT data. In Proceedings of the 2017 on Cloud Computing Security Workshop (pp. 45-50).

[13]. Shrestha, A. K., & Vassileva, J. (2018, June). Blockchain-based research data sharing framework for incentivizing the data owners. In International Conference on Blockchain (pp. 259-266). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-94478-4_19

[14]. The Economist (2015). Blockchains: The great chain of being sure about things. https://www.economist.com/ briefing/2015/10/31/the-great-chain-of-being-sure-aboutthings

[15]. Theodouli, A., Arakliotis, S., Moschou, K., Votis, K., & Tzovaras, D. (2018, August). On the design of a Blockchainbased system to facilitate healthcare data sharing. In 2018, 17^th IEEE International Conference on Trust, Security th and Privacy in Computing and Communications/12 IEEE International Conference on Big Data Science and Engineering (TrustCom/BigDataSE) (pp. 1374-1379). IEEE.

[16]. Wood, G. (2014). Ethereum: A secure decentralised generalised transaction ledger. Ethereum Project Yellow Paper, 151, 1-32.

[17]. Yang, H., & Yang, B. (2017). A blockchain-based approach to the secure sharing of healthcare data. Nisk Journal, 100-111.

[18]. Zhang, A., & Lin, X. (2018). Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain. Journal of Medical Systems, 42(8), 1-18. https://doi.org/10.1007/s10916-018-0995-5

[19]. Zheng, X., Mukkamala, R. R., Vatrapu, R., & Ordieres- Mere, J. (2018, September). Blockchain-based personal health data sharing system using cloud storage. In 2018, IEEE 20^th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-6). IEEE.

[20]. Zhu, L., Wu, Y., Gai, K., & Choo, K. K. R. (2019). Controllable and trustworthy blockchain-based cloud data management. Future Generation Computer Systems, 91, 527-535. https://doi.org/10.1016/j.future.2018.09.019

Full Article (HTML)

Pdf

Survey Paper

Survey on Cloud Computing Load Balancing

Chandu Vaidya * , Kalpana S. Bhure**

*-** Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering & Research, Nagpur, Maharashtra, India.

Vaidya, C., and Bhure, K. S. (2020). Survey on Cloud Computing Load Balancing. i-manager's Journal on Cloud Computing, 7(1), 32-46. https://doi.org/10.26634/jcc.7.1.17156

Abstract

A wise person is the basis of counseling just as cloud computing is a computer particle. Cloud computing is an unmistakable, limitless, unchanged, remarkable, upright, pervasive technology of the present period. This also pursues enormous capability to support all end users with fancy facilities. Cloud computing has multi-tenancy features, virtualization, load balancing technology, security, scheduling and much more. Cloud computing Virtualization (helping design of the cloud) enables users to access several virtual machines or their features, delivering pay-per-use services to those. Load Balancing is one of the big issues of cloud computing. Load balancing is the strategy used to divide the load between various computing elements that are available and to equalize load with virtual machines to achieve efficiency and improve the throughput. Cloud computing is a massive network and behaves heterogeneously. But it could be an incentive for inappropriate allocation of loads and unequal use of resources. A survey on cloud computing models, load balancing & scheduling process is carried out in this paper. By considering the different parameters of the resources, many authors have proposed the solution on load balancing problem. Here, we suggest a new load balancing method (Decider, Threshold based), taking into account various parameters such as RAM, CPU consumption, network traffic statistics. We also concentrate on scheduling at the local and global level for optimal use of computing elements and thus reduce the task's overall execution time. Based on comparative analysis we will assess the efficiency of algorithms / servers.

References

[1]. Baby, A. (2015). Improved honey bee inspired load balancing of tasks with position updation. International Journal of Research in Applied Science & Engineering Technology (IJRASET), 3(IV), 1157-1163.

[2]. Bhure, K., Titarmare, N. (2020). A study on:- cloud computing, virtual private cloud, load balancing. International Journal of Engineering and Creative Science, 3(2), 47-52.

[3]. Buyya, R., Yeo, C. S., Venugopal, S., Broberg, J., & Brandic, I. (2009). Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5^th utility. Future Generation Computer Systems, 25(6), 599-616. https://doi.org/10.1016/j.future.2008.12. 001

[4]. Chakraborty, S., & Khan, A. K. (2013). A Study of Load Distribution Algorithms In Distributed Scheduling. International Journal of Research in Engineering and Technology, 2(2), 37-40. https://doi.org/10.15623/ijret. 2013.0214007

[5]. Choudhary, A., & Rathi, R. (2015). An approach on dynamic semi-distributed load balancing algorithm for cloud computing system. International Journal of Scientific & Engineering Research, 6(6), 406-411.

[6]. Dasgupta, K., Mandal, B., Dutta, P., Mandal, J. K., & Dam, S. (2013). A genetic algorithm (GA) based load balancing strategy for cloud computing. Procedia Technology, 10, 340-347.

[7]. Deepa, T., & Cheelu, D. (2018). A Comparative study of static and dynamic load balancing algorithms in cloud computing. International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS-2017) (pp. 3375-3378).

[8]. Dhakal, S., Hayat, M. M., Pezoa, J. E., Yang, C., & Bader, D. A. (2007). Dynamic load balancing in distributed systems in the presence of delays: A regeneration-theory approach. IEEE transactions on parallel and distributed systems, 18(4), 485-497. https://doi.org/10.1109/TPDS. 2007.1009

[9]. Domanal, S.G., & Reddy, G.R. (2013). Load balancing in cloud computingusing modified throttled algorithm. 2013 IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 1-5.

[10]. Fang, Y., Wang, F., & Ge, J. (2010, October). A task scheduling algorithm based on load balancing in cloud computing. In International Conference on Web Information Systems and Mining (pp. 271-277). Springer, Berlin, Heidelberg.

[11]. Feng, J., Liu, Z., Wu, C., & Ji, Y. (2018). Mobile edge computing for the Internet of vehicles: Offloading framework and job scheduling. IEEE Vehicular Technology Magazine, 14(1), 28-36.

[12]. Fiandrino, C., Allio, N., Kliazovich, D., Giaccone, P., & Bouvry, P. (2019). Profiling performance of application partitioning for wearable devices in mobile cloud and fog computing. IEEE Access, 7, 12156-12166.

[13]. Gawali, M. B., & Shinde, S. K. (2018). Task scheduling and resource allocation in cloud computing using a heuristic approach. Journal of Cloud Computing, 7(1), 1- 16.

[14]. Hawilo, H., Jammal, M., & Shami, A. (2019). Network function virtualization-aware orchestrator for service function chaining placement in the cloud. IEEE Journal on Selected Areas in Communications, 37(3), 643-655.

[15]. Kapoor, S., & Dabas, C. (2015, August). Cluster based load balancing in cloud computing. In 2015, Eighth International Conference on Contemporary Computing (IC3) (pp. 76-81). IEEE.

[16]. Kashyap, D., & Viradiya, J. (2014). A survey of various load balancing algorithms in cloud computing. International Journal of Scientific & Technology Research, 3(11), 115-119.

[17]. Khandve, T., Talekar, M., & Dhiwar, S. (2015). Security and load balancing in cloud computing. International Journal of Advanced Research in Computer Engineering & Technology (IJARCET), 4(10), 3871-3874.

[18]. Lakshmanan, G. T., Rabinovich, Y. G., & Schloss, R. J. (2013). U.S. Patent No. 8,479,216. Washington, DC: U.S Patent and Trademark Office.

[19]. Li, C., Zhou, X., Sun, M., Lu, K., Zhou, J., Zhuang, H., & Dai, D. (2014, December). DLBS: Decentralized load balancing scheme for event-driven cloud frameworks. In th 2014, 20 IEEE International Conference on Parallel and Distributed Systems (ICPADS) (pp. 853-858). IEEE.

[20]. Liu, H., Liu, S., Meng, X., Yang, C., & Zhang, Y. (2010, May). LBVS: A load balancing strategy for virtual storage. In 2010, International Conference on Service Sciences (pp. 257-262). IEEE.

[21]. Lu, Y., Xie, Q., Kliot, G., Geller, A., Larus, J. R., & Greenberg, A. (2011).Join-Idle-Queue: A novel load balancing algorithm for dynamically scalable web services. Performance Evaluation, 68(11), 1056-1071. https://doi.org/10.1016/j.peva.2011.07.015

[22]. Meng, S., Wang, Y., Jiao, L., Miao, Z., & Sun, K. (2018). Hierarchical evolutionary game based dynamic cloudlet selection and bandwidth allocation for mobile cloud computing environment. IET Communications, 13(1), 16-25.

[23]. Mishra, R., & Jaiswal, A. (2012). Ant colony optimization: A solution of load balancing in cloud. International Journal of Web & Semantic Technology, 3(2), 33-50.

[24]. Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials, 20(4), 2923-2960.

[25]. Mohanty, R., Behera, H. S., Patwari, K., Dash, M., & Prasanna, M. L. (2011). Priority Based Dynamic Round Robin (PBDRR) algorithm with intelligent time slice for soft real time systems. International Journal of Advanced Computer Science and Applications (IJACSA), 2(2), 46-50.

[26]. Mondal, B., Dasgupta, K., & Dutta, P. (2012). Load balancing in cloud computing using stochastic hill climbing - a soft computing approach. Procedia Technology, 4, 783-789.

[27]. Ning, Z., Kong, X., Xia, F., Hou, W., & Wang, X. (2018). Green and sustainable cloud of things: Enabling collaborative edge computing. IEEE Communications Magazine, 57(1), 72-78.

[28]. Pasha, N., Agarwal, A., & Rastogi, R. (2014). Round robin approach for VM load balancing algorithm in cloud computing environment. International Journal of Advanced Research in Computer Science and Software Engineering, 4(5), 34-39.

[29]. Paya, A., & Marinescu, D. C. (2015). Energy-aware load balancing and application scaling for the cloud ecosystem. IEEE Transactions on Cloud Computing, 5(1), 15-27.

[30]. Sharma, H., & Sekhon, G. S. (2017). Load balancing in cloud using enhanced genetic algorithm. International Journal of Innovations & Advancement in Computer Science IJIACS, 6(1), 13-19.

[31]. Vaidhya, C. (2016). An approach for processor utilization in master slave environment. In Proceedings of International Conference on Advanced Material Technologies (ICAMT).

[32]. Vaidya, C. D., & Chandak, M. B. (2012, November). Efficient parallel process migration algorithm using statistical approach. In 2012 Fourth International Conference on Computational Intelligence and Communication Networks (pp. 525-529). IEEE.

[33]. Vaidya, C., Khobragade, P., & Golghate, A. (2016). Data Leakage Detection and Security in Cloud Computing. GRD Journals-Global Research Development Journal for Engineering, 1(12), 137-140.

[34]. Vaidya, C., Nampalliwar, A., Nampalliwar, K., Thakkar, R., & Bhagat, S. (2018). Statistical approach for load distribution in decentralized cloud computing, Helix, 8(5), 3884-3887. https://doi.org/10.29042/2018-3884- 3887

[35]. Vaidya, C., Saide, S., & Chadawar, S. (2016). Data leakage detection and dependable storage service. IJSTE International Journal of Science Technology & Engineering, 2 (10), 694-701.

[36]. Wang, C., Feng, C., & Cheng, J. (2017, January). Randomized load balancing with a helper. In 2017 International Conference on Computing, Networking and Communications (ICNC) (pp. 518-524). IEEE.

[37]. Xiao, Z., Song, W., & Chen, Q. (2012). Dynamic resource allocation using virtual machines for cloud computing environment. IEEE Transactions on Parallel and Distributed Systems, 24(6), 1107-1117.

[38]. Zhang, Y., Chang, R., & Townend, P. (2019). Guest editor's introduction: special section on virtualization and services for cloud-based application systems. IEEE Transactions on Services Computing, 12(1), 88-90.

i-manager's Journal on Cloud Computing (JCC)

Current Issue Vol. 11 Issue 1

Most Read

Most Cited

Volume 7 Issue 1 January - June 2020

Cloud Computing Technology for Digital Economy

Bommala Harikrishna * , S. Kiran**

* Department of Computer Science and Engineering, KG Reddy College of Engineering & Technology, Moinabad, Telangana, India. ** Department of Computer Science and Engineering, YSR Engineering College, Proddatur, Andhra Pradesh, India.

Harikrishna, B., and Kiran, S. (2020). Cloud Computing Technology for Digital Economy. i-manager's Journal on Cloud Computing, 7(1), 1-11. https://doi.org/10.26634/jcc.7.1.17307

Abstract

References

Full Article (HTML)

Pdf

Trusted Hybrid Multifactor Authentication for Cloud Users

K. Devi Priya* , L. Sumalatha **

*,** Department of Computer Science and Engineering, JNTUK University, Kakinada, Andhra Pradesh, India.

Priya, K. D., and Sumalatha, L. (2020). Trusted Hybrid Multifactor Authentication for Cloud Users. i-manager's Journal on Cloud Computing, 7(1), 12-20. https://doi.org/10.26634/jcc.7.1.16670

Abstract

References

Full Article (HTML)

Pdf

Green Computing Model on Clouds

Daisy Sharmah*

Department of Computer Science & Electronics, University of Science & Technology, Meghalaya, India.

Sharmah, D. (2020). Green Computing Model on Clouds. i-manager's Journal on Cloud Computing, 7(1), 21-25. https://doi.org/10.26634/jcc.7.1.16956

Abstract

References

Full Article (HTML)

Pdf

Survey on Consensus Algorithms in Blockchain for Secure Data Sharing

Thirumalaimuthu T. Ramanathan*

Information Science and Technology, Multimedia University, Melaka, Malaysia.

Ramanathan, T. T. (2020). Survey on Consensus Algorithms in Blockchain for Secure Data Sharing. i-manager's Journal on Cloud Computing, 7(1), 26-31. https://doi.org/10.26634/jcc.7.1.17114

Abstract

References

Full Article (HTML)

Pdf

Survey on Cloud Computing Load Balancing

Chandu Vaidya * , Kalpana S. Bhure**

*-** Department of Computer Science and Engineering, Rajiv Gandhi College of Engineering & Research, Nagpur, Maharashtra, India.

Vaidya, C., and Bhure, K. S. (2020). Survey on Cloud Computing Load Balancing. i-manager's Journal on Cloud Computing, 7(1), 32-46. https://doi.org/10.26634/jcc.7.1.17156

Abstract

References

Full Article (HTML)

Pdf

* Department of Computer Science and Engineering, KG Reddy College of Engineering & Technology, Moinabad, Telangana, India.

** Department of Computer Science and Engineering, YSR Engineering College, Proddatur, Andhra Pradesh, India.