i-manager Publications

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

Current Issue Vol. 10 Issue 2

Most Cited

Volume 3 Issue 4 August - October 2016

Research Paper

Cloud-Based ERP for Arab Manufacturing Firms

Subramaniam Arunachalam* , Mohammad Alsadi, Aloysius A. Edohis*

*-*** School of Architecture, Computing and Engineering, University of East London, UK.

Arunachalam. S., Alsadi. M., and Edoh. A. (2016). Cloud-Based ERP for Arab Manufacturing Firms. i-manager’s Journal on Cloud Computing, 3(4), 1-6. https://doi.org/10.26634/jcc.3.4.13592

Abstract

Enterprise Resource Planning (ERP) is a successful technique of integrating business processes to one computer system so that the information is easily available and accessible to everybody in the system. Even though the technique is successful and helps in reducing the lead times, inventory costs, labour costs, etc., the adoption rate is very low in Arab. This study focuses on the problem areas that Arab companies face while implementing ERP in their business processes. The study explores how ERP can be implemented successfully in Arab medium sized manufacturing firms and to understand why only some firms are able to implement ERP successfully, while others experience ERP failure. The study involves an empirical analysis to identify the issues and barriers in adopting ERP and outlines a research methodology to establish a novel framework to implement cloud-based ERP suitable for Arab manufacturing firms.

References

[1]. Buonanno, G., Faverio, P., Pigni, F., Ravarini, A., Sciuto, D., & Tagliavini, M. (2005). Factors affecting ERP system adoption: A comparative analysis between SMEs and large companies. Journal of Enterprise Information Management, 18(4), 384-426.

[2]. Cartman, C., & Salazar, A. (2011). The influence of organisational size, internal IT capabilities, and competitive and vendor pressures on ERP adoption in SMEs. International Journal of Enterprise Information Systems (IJEIS), 7(3), 68-92.

[3]. Cragg, P., Mills, A., & Suraweera, T. (2010). Understanding IT management in SMEs. Electronic Journal Information Systems Evaluation, 13(1), 27-34.

[4]. Laukkanen, S., Sarpola, S., & Hallikainen, P. (2007). Enterprise size matters: Objectives and constraints of ERP adoption.Journal of Enterprise Information Management, 20(3), 319-334.

[5]. Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing—The business perspective. Decision Support Systems, 51(1), 176-189.

[6]. Mohlameane, M. J. (2012). A framework for cloud computing adoption: A case study for SME's in South Africa.

[7]. Peng, G. C. A., & Gala, C. (2014). Cloud ERP: A new dilemma to modern organisations? Journal of Computer Information Systems, 54(4), 22-30.

[8]. Scavo, F., Newton, B., & Longwell, M. (2012). Choosing between cloud and hosted ERP, and why it matters. Computer Economics Report, 34(8).

[9]. Seethamraju, R., & Seethamraju, J. (2008). Adoption of ERPs in a medium-sized enterprise-A case study. ACIS 2008 Proceedings, 104.

[10]. Sharma, D., Garg, S. K., & Sharma, C. (2013). A cloud computing - based frame work for internationalisation of SMEs. International Journal of Cloud Computing, 2(4), 364-377.

[11]. Sheldon, D. H. (2005). Class A ERP Implementation: Integrating Lean and Six Sigma. J. Ross Publishing.

[12]. Shtub, A., & Karni, R. (2010). ERP: The Dynamics of Supply Chain and Process Management. Springer Science & Business Media.

[13]. Sledgianowski, D., Tafti, M. H., & Kierstead, J. (2008). SME ERP system sourcing strategies: A case study. Industrial Management & Data Systems, 108(4), 421-436.

[14]. Vimalkumar, P., Rajamani, A., & Jayasubramanian, P. (2012). Implementation of ERP in Business Process Based On Cloud Computing. OSR Journal of Business and Management (IOSRJBM), 45-50.

[15]. Weng, F., & Hung, M. C. (2014). Competition and challenge on adopting cloud ERP. International Journal of Innovation, Management and Technology, 5(4), 309.

[16]. Xu, D. (2010, June). Cloud computing: An emerging technology. In Computer Design and Applications (ICCDA), 2010 International Conference on (Vol. 1, pp. V1-100). IEEE.

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Research Paper

Machine Learning methods for Cloud Computing

P. Kiran Rao* , R. Sandeep Kumar**

*-** Assistant Professor, Department of Computer Science and Engineering, G. Pullaiah College of Engineering & Technology, Kurnool, AP, India.

Rao. P. K., and Kumar. R. S. (2016). Machine Learning methods for Cloud Computing. i-manager’s Journal on Cloud Computing, 3(4), 7-11. https://doi.org/10.26634/jcc.3.4.13593

Abstract

Due to the huge use of data made available in cloud computing, cloud computing challenged with issues of security on demand application resource management and self-monitoring without latency. This paper explores supervised and unsupervised learning methods on resource provisioning, monitoring, and management topics in cloud computing and examines a number of methods which propose to make use of machine learning to either allow for more self-monitored on demand application resource management or used to know the work level of resources in infinity cloud computing. The authors have also compared regular techniques in resource management in cloud computing like FIFO, VMs cluster management, etc., with machine learning methods.

References

[1]. Aggarwal, P., & Sharma, S. K. (2015, February). An empirical comparison of classifiers to analyze intrusion detection. In Advanced Computing & Communication Technologies (ACCT), 2015 Fifth International Conference on (pp. 446-450). IEEE.

[2]. Bala, K., & Vashist, S. (2014). Machine Learning based decision making by brokers in cloud computing. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3(7), 269-273.

[3]. Dinda, P. A., & O'hallaron, D. R. (2000). Host load prediction using linear models. Cluster Computing, 3(4), 265-280.

[4]. Hu, R., Jiang, J., Liu, G., & Wang, L. (2014). Efficient resources provisioning based on load forecasting in cloud. The Scientific World Journal, 2014.

[5]. Li, K., Gibson, C., Ho, D., Zhou, Q., Kim, J., Buhisi, O., ... & Gerber, M. (2013, April). Assessment of machine learning algorithms in cloud computing frameworks. In Systems and Information Engineering Design Symposium (SIEDS), 2013 IEEE (pp. 98-103). IEEE.

[6]. Matsunaga, A., & Fortes, J. A. (2010, May). On the use of machine learning to predict the time and resources consumed by applications. In Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (pp. 495-504). IEEE Computer Society.

[7]. Rodriguez, M. A., & Buyya, R. (2015, September). A responsive knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds. In Parallel Processing (ICPP), 2015 44^th International Conference on (pp. 839-848). IEEE.

[8]. Rodriguez, M. A., & Buyya, R. (2017). A taxonomy and survey on scheduling algorithms for scientific workflows in IaaS cloud computing environments. Concurrency and Computation: Practice and Experience, 29(8).

[9]. Rumelhart, D. E., McClelland, J. L., & PDP Research Group. (1986). Parallel Distributed Processing: Explorations in the Microstructures of Cognition. Volume 1: Foundations.

[10]. Stamatakis, A. (2006). RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics, 22(21), 2688- 2690.

[11]. Vázquez, C., Huedo, E., Montero, R. S., & Llorente, I. M. (2011). On the use of clouds for grid resource provisioning. Future Generation Computer Systems, 27(5), 600-605.

[12]. Vecchiola, C., Calheiros, R. N., Karunamoorthy, D., & Buyya, R. (2012). Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka. Future Generation Computer Systems, 28(1), 58- 65.

[13]. Wang, S., & Summers, R. M. (2012). Machine learning and radiology. Medical Image Analysis, 16(5), 933-951.

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Research Paper

An IoT Based Motion Detection using Raspberry PI

Yerrajennugari Raj Kumar* , Mutala Kishore Reddy, Dr. D. Rajaveerappa*

- PG Scholar, Department of Electronics and Communication Engineering, MITS College, Madanapalle, India.

Professor, Department of Electronics and Communication Engineering, MITS College, Madanapalle, India.

Kumar. Y. R., Reddy. M. K., and Rajaveerappa. D. (2016). An IoT Based Motion Detection using Raspberry PI. i-manager’s Journal on Cloud Computing, 3(4), 12-17. https://doi.org/10.26634/jcc.3.4.13594

Abstract

This paper intends to depict a security caution framework having low handling power chips utilizing Internet of things, which screens and gets alert when a movement is distinguished and sends photographs and recordings to a cloud server. Web of things is the correspondence of anything with any other things, the correspondence basically refers to exchanging of usable information, for instance a sensor in a cloud to screen and control the temperature. It is evaluated that by 2020, there will be around 50 billion web empowered gadgets. It helps in observing and controlling of home appliances from outside and within industry for checking the hardwares and so on. In present days, cost is an essential element. So, this work will be valuable to principally diminish the cost for observing the movements. So, to actualize this technique, the authors have utilized Raspberry pi, to act as a server inside the home to capture as photos. Later, these photos are changed into video, which will be sent to a Web Page already developed or sent to a URL, or an IP address. With this goal, we can perceive what is occurring at our home or office by simply entering the URL or IP address.

References

[1]. Ansari, A. N., Patil, S., Navada, A., Peshave, A., & Borole, V. (2011, July). Online C/C++ compiler using cloud computing. In Multimedia Technology (ICMT), 2011 International Conference on (pp. 3591-3594). IEEE.

[2]. Ansari, A. N., Sedky, M., Sharma, N., & Tyagi, A. (2015, January). An Internet of things approach for motion detection using Raspberry Pi. In Intelligent Computing and Internet of Things (ICIT), 2014 International Conference on (pp. 131-134). IEEE.

[3]. Babu, M. G., Venugopal, M., & Kiran, M. P. (2013). Driver Development for CMOS Camera on ARM11 using Embedded Linux. International Journal of Computer Science and Information Technologies, 4(5), 664-667.

[4]. Ning, H., & Liu, H. (2012). Cyber-physical-social based security architecture for future internet of things. Advances in Internet of Things, 2(1), 1.

[5]. Patel, M. M., Jajal, M. A., & Dixita, B. V. (2015). Home automation using Raspberry Pi. International Journal of Innovative and Emerging Research in Engineering, 2(3).

[6]. Patel, T. K., Wadekar, U., Wabale, A., & Datkhore, S. S. (2015). Appliances Control Using Ethernet and Raspberry PI. International Journal of Advanced Research in Computer Science and Software Engineering, 5(3), 494- 497.

[7]. Pfister, C. (2011). Getting Started with the Internet of Things: Connecting Sensors and Microcontrollers to the Cloud. O'Reilly Media, Inc.

[8]. Raspberrypi.org. Forum [Online]. Retrieved from http://www.raspberrypi.org/phpBB3/

[9]. Roelands, M., Claeys, L., Godon, M., Geerts, M., Feki, M. A., & Trappeniers, L. (2011). Enabling the masses to become creative in smart spaces. In Architecting the Internet of Things (pp. 37-64). Springer Berlin Heidelberg.

[10]. Srinivas, V., & Bh, V. P. (2015). Smart Monitoring and Controlling of a System Using ARM11 SOC. IJSEAT, 3(6), 208-211.

[11]. Wu, Z. (2010). Initial Study on IoT Security Architecture. Journal on Strategy and Decision-Making Research.

[12]. Yang, X., Li, F. J., Mu, X. Y., & Yu, T. W. (2010). Design of security and defense system for home based on Internet of things. Journal of Computer Applications, 30.

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Research Paper

Effective Key Management (EKM) for Class Based Cloud Data

Dileep Kumar Reddy P* , R. Praveen Sam**

* Lecturer, Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, India.

** Professor, Department of Computer Science and Engineering, GPREC, Kurnool, India.

Reddy. P. D. K., and Sam. R. P. (2016). Effective Key Management (EKM) for Class Based Cloud Data. i-manager’s Journal on Cloud Computing, 3(4), 18-25. https://doi.org/10.26634/jcc.3.4.13595

Abstract

Security in Class Based Cloud Data is undoubtedly one of the challenging tasks in Cloud Computing. Class based Cloud structure is totally a new concept where the cloud can be divided into a number of classes. Through this, a protected cloud could be implemented in hybrid cloud which helps to reduce the time and cost of searching and maintenance. Normally, the classes can be divided based on the usage of the cloud and for each and every class, there is a key for data security. This paper explains about the key management to different classes and allows the authenticated users to use them.

References

[1]. Chu, C. K., Chow, S. S., Tzeng, W. G., Zhou, J., & Deng, R. H. (2014). Key-aggregate cryptosystem for scalable data sharing in cloud storage. IEEE Transactions on Parallel and Distributed Systems, 25(2), 468-477.

[2]. Kandukuri, B. R., & Rakshit, A. (2009, September). Cloud security issues. In Services Computing, 2009. SCC'09. IEEE International Conference on (pp. 517-520). IEEE.

[3]. Kaufman, L. M. (2009). Data security in the world of cloud computing. IEEE Security & Privacy, 7(4), 62-64.

[4]. Khedkar, S. V., & Gawande, A. D. (2014). Data partitioning technique to improve cloud data storage security. International Journal of Computer Science and Information Technologies, 5(3), 3347-50.

[5]. López-Alt, A., Tromer, E., & Vaikuntanathan, V. (2012, May). On-the-fly multiparty computation on the cloud via multikey fully homomorphic encryption. In Proceedings of the 44^th Annual ACM Symposium on Theory of Computing (pp. 1219-1234). ACM.

[6]. Mohamed, E. M., Abdelkader, H. S., & El-Etriby, S. (2012, May). Enhanced data security model for cloud computing. In Informatics and Systems (INFOS), 2012 8^th International Conference on (pp. CC-12). IEEE.

[7]. Ren, K., Wang, C., & Wang, Q. (2012). Security challenges for the public cloud. IEEE Internet Computing, 16(1), 69-73.

[8]. Sood, S. K. (2012). A combined approach to ensure data security in cloud computing. Journal of Network and Computer Applications, 35(6), 1831-1838.

[9]. Wang, B., Chow, S. S., Li, M., & Li, H. (2013, July). Storing shared data on the cloud via security-mediator. In Distributed Computing Systems (ICDCS), 2013 IEEE 33^rd International Conference on (pp. 124-133). IEEE.

[10]. Wang, Q., Wang, C., Li, J., Ren, K., & Lou, W. (2009). Enabling public verifiability and data dynamics for storage security in cloud computing. Computer Security–ESORICS 2009, 355-370.

[11]. Ye, Y., Xiao, L., Yen, I. L., & Bastani, F. (2010, December). Cloud storage design based on hybrid of replication and data partitioning. In Parallel and Distributed Systems (ICPADS), 2010 IEEE 16^th International Conference on (pp. 415-422). IEEE.

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Research Paper

Information Replication in Cloud Computing Datacenters Using Most Efficient Overall Performance and Safety

M. Saraswathi Mani* , V. Ramya**

* Research Scholar, Department of Computer Science, Government College (Autonomous), Nandanam, Chennai, India.

** Assistant Professor, Department of Computer Science, Government College (Autonomous), Nandanam, Chennai, India.

Mani. S. M., and Ramya. V. (2016). Information Replication In Cloud Computing Datacenters Using Most Efficient Overall Performance And Safety. i-manager’s Journal on Cloud Computing, 3(4), 26-33. https://doi.org/10.26634/jcc.3.4.13596

Abstract

The online records storage, a cloud utility wishes clients to move the records in cloud's virtualized and shared environment to be able to end in numerous safety issues. Pooling, a property of a cloud, permits the resources (assets) to be shared among numerous clients. Moreover, the shared assets are additionally reassigned to various users at a few occurrence of them, which can gradually end in an information compromise through data recovery methodologies. Multi-tenant virtualized surroundings could likely result in a VM to urge away the bounds of virtual tool screen (VM). The open VM can intervene with entirely different VM to get entry to unauthorized data. In addition, multi-tenant virtualized network gets right to entry for flexible data privacy and integrity. Additionally, to improve the retrieval time; the information is fragmented over the nodes. The results of the simulations explain protection and performance have resulted in increased security level of information.

References

[1]. Ali, M., Bilal, K., Khan, S., Veeravalli, B., Li, K., & Zomaya, A. (2015). DROPS: Division and Replication of Data in the Cloud for Optimal Performance and Security. IEEE Transactions on Cloud Computing.

[2]. Juels, A., & Oprea, A. (2013). New approaches to security and availability for cloud data. Communications of the ACM, 56(2), 64-73.

[3]. Kappes, G., Hatzieleftheriou, A., & Anastasiadis, S. V. (2013). Dike: Virtualization-aware Access Control for Multitenant Filesystems. Technical Report No. DCS2013- 1, University of Ioannina, Greece.

[4]. Khan, A. N., Kiah, M. M., Khan, S. U., & Madani, S. A. (2013). Towards secure mobile cloud computing: A survey. Future Generation Computer Systems, 29(5), 1278-1299.

[5]. Khan, A. N., Kiah, M. M., Madani, S. A., & Ali, M. (2013). Enhanced dynamic credential generation scheme for protection of user identity in mobile-cloud computing. The Journal of Supercomputing, 66(3), 1687- 1706.

[6]. Khan, S.U., & Ahmad, I. (2008). Comparison and analysis of ten static heuristics-based Internet data replication techniques. Journal of Parallel and Distributed Computing, 68(2), 113-136.

[8]. Qiu, L., Padmanabhan, V. N., & Voelker, G. M. (2001). On the placement of web server replicas. In INFOCOM 2001. Twentieth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE (Vol. 3, pp. 1587-1596). IEEE.

[9]. Sun, D., Chang, G., Sun, L., & Wang, X. (2011). Surveying and analyzing security, privacy and trust issues in cloud computing environments. Procedia Engineering, 15, 2852-2856.

[10]. Wylie, J.J. (2001). Selecting the right data distribution scheme for a survivable storage system. Technical Report CMU-CS-01-120, Carnegie Mellon University, May 2001.

[11]. Zissis, D., & Lekkas, D. (2012). Addressing cloud computing security issues. Future Generation Computer Systems, 28(3), 583-592.

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

Current Issue Vol. 10 Issue 2

Most Read

Most Cited

Volume 3 Issue 4 August - October 2016

Cloud-Based ERP for Arab Manufacturing Firms

Subramaniam Arunachalam* , Mohammad Alsadi**, Aloysius A. Edohis***

*-*** School of Architecture, Computing and Engineering, University of East London, UK.

Arunachalam. S., Alsadi. M., and Edoh. A. (2016). Cloud-Based ERP for Arab Manufacturing Firms. i-manager’s Journal on Cloud Computing, 3(4), 1-6. https://doi.org/10.26634/jcc.3.4.13592

Abstract

References

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Machine Learning methods for Cloud Computing

P. Kiran Rao* , R. Sandeep Kumar**

*-** Assistant Professor, Department of Computer Science and Engineering, G. Pullaiah College of Engineering & Technology, Kurnool, AP, India.

Rao. P. K., and Kumar. R. S. (2016). Machine Learning methods for Cloud Computing. i-manager’s Journal on Cloud Computing, 3(4), 7-11. https://doi.org/10.26634/jcc.3.4.13593

Abstract

References

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An IoT Based Motion Detection using Raspberry PI

Yerrajennugari Raj Kumar* , Mutala Kishore Reddy**, Dr. D. Rajaveerappa***

*-** PG Scholar, Department of Electronics and Communication Engineering, MITS College, Madanapalle, India. *** Professor, Department of Electronics and Communication Engineering, MITS College, Madanapalle, India.

Kumar. Y. R., Reddy. M. K., and Rajaveerappa. D. (2016). An IoT Based Motion Detection using Raspberry PI. i-manager’s Journal on Cloud Computing, 3(4), 12-17. https://doi.org/10.26634/jcc.3.4.13594

Abstract

References

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Effective Key Management (EKM) for Class Based Cloud Data

Dileep Kumar Reddy P* , R. Praveen Sam**

* Lecturer, Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, India. ** Professor, Department of Computer Science and Engineering, GPREC, Kurnool, India.

Reddy. P. D. K., and Sam. R. P. (2016). Effective Key Management (EKM) for Class Based Cloud Data. i-manager’s Journal on Cloud Computing, 3(4), 18-25. https://doi.org/10.26634/jcc.3.4.13595

Abstract

References

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Information Replication in Cloud Computing Datacenters Using Most Efficient Overall Performance and Safety

M. Saraswathi Mani* , V. Ramya**

* Research Scholar, Department of Computer Science, Government College (Autonomous), Nandanam, Chennai, India. ** Assistant Professor, Department of Computer Science, Government College (Autonomous), Nandanam, Chennai, India.

Mani. S. M., and Ramya. V. (2016). Information Replication In Cloud Computing Datacenters Using Most Efficient Overall Performance And Safety. i-manager’s Journal on Cloud Computing, 3(4), 26-33. https://doi.org/10.26634/jcc.3.4.13596

Abstract

References

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Subramaniam Arunachalam* , Mohammad Alsadi, Aloysius A. Edohis*

Yerrajennugari Raj Kumar* , Mutala Kishore Reddy, Dr. D. Rajaveerappa*

- PG Scholar, Department of Electronics and Communication Engineering, MITS College, Madanapalle, India.

Professor, Department of Electronics and Communication Engineering, MITS College, Madanapalle, India.

* Lecturer, Department of Computer Science and Engineering, JNTUA College of Engineering, Ananthapuramu, India.

** Professor, Department of Computer Science and Engineering, GPREC, Kurnool, India.

* Research Scholar, Department of Computer Science, Government College (Autonomous), Nandanam, Chennai, India.

** Assistant Professor, Department of Computer Science, Government College (Autonomous), Nandanam, Chennai, India.