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 5 Issue 2 July - December 2018

Research Paper

Development of Blowfish Encryption Scheme for Secure Data Storage in Public and Commercial Cloud Computing Environment

Shafi’i Muhammad Abdulhamid* , Nafisat Abubakar Sadiq, Abdullahi Mohammed *, Nadim Rana**, Haruna Chiroma*, Emmanuel Gbenga Dada ****

* Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

Graduate, Department of Computer Science, Federal University of Technology (FUT), Minna, Nigeria.

* Lecturer, Department of Computer Science, Ahmadu Bello University Zaria-Nigeria.

** Senior Lecturer, College of Computer Science and Information Systems, Jazan University, Jazan, Kingdom of Saudi Arabia.

* Senior Lecturer, Department of Computer Science, Federal College of Education (Technical), Gombe, Nigeria.

**** Faculty, Department of Computer Engineering, University of Maiduguri, Maiduguri, Nigeria.

Abdulhamid, S. M., Sadiq, N. A., Abdullahi, M., Rana, N., Chiroma, H., Dada, E.G (2018). Development of Blowfish Encryption Scheme for Secure Data Storage in Public and Commercial Cloud Computing Environment, i-manager's Journal on Cloud Computing 5(2), 1-10. https://doi.org/10.26634/jcc.5.2.15690

Abstract

Cloud computing is defined as the delivery of on-demand computing resources ranging from infrastructure application to datacenter over the internet on a pay-per-use basis. Most cloud computing applications does not guarantee high level security, such as privacy, confidentiality, and integrity of data because of third-party transition. This brings the development of Blowfish cloud encryption system that enables them to encrypt their data before storage in the cloud. Blowfish encryption scheme is a symmetric block cipher used to encrypt and decrypt data. Microsoft Azure cloud server was used to test the proposed encryption system. Users are able to encrypt their data and obtain a unique identification to help them retrieve encrypted data from the cloud storage facility as and when needed.

References

[1]. Abdullahi, M., & Ngadi, M. A. (2016). Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640-650.

[2]. Cao, N., Wang, C., Li, M., Ren, K., & Lou, W. (2014). Privacy-preserving multi-keyword ranked search over encrypted cloud data. IEEE Transactions on Parallel and Distributed Systems, 25(1), 222-233.

[3]. Chaplot, V. (2015). Databases Management Systems (DBMS). International Education and Research Journal (IERJ), 1(2), 24-25.

[4]. Curino, C., Jones, E. P., Popa, R. A., Malviya, N., Wu, E., Madden, S., & Zeldovich, N. (2011). Relational cloud: A database-as-a-service for the cloud. Information Applications, 1(1), 235-240.

[5]. Gahi, Y., Guennoun, M.., & Khatib, K. E (2000). A secure database system using Homomorphic Encryption Schemes. DBKDA 2011: The Third International Conference on Advances in Databases, Knowledge, and Data Applications (pp. 54-58).

[6]. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Abdullah, G., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues. Information Systems, 47(7), 98-115. https://doi.org/ 10.1016/j.is.2014.07.006

[7]. Hoang, V. T., Katz, J., & Malozemof, A. J. (2015). Automated analysis and synthesis of authenticated encryption schemes. IET Technology Security, 3(1), 1-31.

[8]. Itani, W., Kayssi, A., & Chehab, A. (2009). Privacy as a Service: Privacy-Aware Data Storage and Processing in Cloud Computing Architectures. IEEE International Conference on Dependable, Autonomic and Secure Computing, (Vol. 3, No. 5, pp. 711-716) https://doi.org/ 10.1109/DASC.2009.139

[9]. Kaur, M., & Singh, R. (2013). Implementing encryption algorithms to enhance data security of cloud in cloud computing. International Journal of Computer Applications, 70(18), 16-21.

[10]. Krishnamurthy, G. N., Ramaswamy, V., Leela. G. H., & Ashalatha, M. E (2008). Blow-CAST-Fish: A new 64-bit block cipher. International Journal of Computer Science and Network Security (IJCSNS), 8(4), 282-290.

[11]. Latiff, M. S. A., Abdul-Salaam, G., & Madni, S. H. H. (2016). Secure scientific applications scheduling technique for cloud computing environment using global league championship algorithm. PloS One, 11(7), 158102.

[12]. Latiff, M. S. A., Madni, S. H. H., & Abdullahi, M. (2018). Fault tolerance aware scheduling technique for cloud computing environment using dynamic clustering algorithm. Neural Computing and Applications, 29(1), 279-293.

[13]. Lee, C. C., Chung, P. S., & Hwang, M. S. (2013). A survey on attribute-based encryption schemes of access control in cloud environments. IJ Network Security, 15(4), 231-240.

[14]. Li, M., Yu, S., Cao, N., & Lou, W. (2011, June). Authorized private keyword search over encrypted data in cloud computing. In Distributed Computing Systems (ICDCS), 2011 31^stInternational Conference on (pp. 383- 392). IEEE.

[15]. Madni, S. H. H., Latiff, M. S. A., Abdullahi, M., & Usman, M. J. (2017). Performance comparison of heuristic algorithms for task scheduling in IaaS cloud computing environment. PloS One, 12(5), e0176321.

[16]. Madni, S. H. H., Latiff, M. S. A., & Coulibaly, Y. (2017). Recent advancements in resource allocation techniques for cloud computing environment: A systematic review. Cluster Computing, 20(3), 2489-2533.

[17]. Rittinghouse, J. W., & Ransome, J. F. (2016). Cloud Computing: Implementation, Management, and Security. CRC Press.

[18]. Said, A. (2005, September). Measuring the strength of partial encryption schemes. In Image Processing, 2005. ICIP 2005. IEEE International Conference on (Vol. 2, pp. 1126-1129). IEEE.

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[20]. Singh, P., & Singh, K. (2013). Image encryption and decr yption using blowfish algorithm in Matlab. International Journal of Scientific & Engineering Research, 4(7), 150-154.

[21]. Thakur, J., & Kumar, N. (2011). DES, AES and Blowfish: Symmetric key cryptography algorithms simulation based performance analysis. International Journal of Emerging Technology and Advanced Engineering, 1(2), 6-12.

[22]. Xia, Z., Wang, X., Sun,X., & Wang, Q (2016) A secure and dynamic multi-keyword ranked search scheme over encrypted cloud data. In IEEE Transactions on Parallel and Distributed Systems, 27(2), 340-352. doi: 10.1109/TPDS.2015.2401003

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Research Paper

Inter-Agent Coordinated Security Model for Cloud Based Virtual Machines

E. C. Onuoha* , O. P. Akomolafe **

* Lecturer, Federal Polytechnic, Bida, Nigeria.

** Lecturer, Department of Computer Science, University of Ibadan, Ibadan, Nigeria.

Onuoha,E.C., Akomolafe,O.P.(2018).Inter-Agent Coordinated Security Model for Cloud Based Virtual Machines, i-manager's Journal on Cloud Computing 5(2), 11-19. https://doi.org/10.26634/jcc.5.2.15688

Abstract

For a user who desires to utilize the services of the cloud, security is not negotiable. Cloud Service Providers (CSPs) have security features that help protect user's data and information. These features are however not comprehensive. The Service Level Agreement (SLA) of most CSPs have certain exclusions that warrant users to undertake some measures of security upon themselves, especially for tenants having Virtual Machines (VM) in a multi-tenant architecture. This means that users who are ignorant of the security implications might be exposed to great risks. This paper presents a security model that used the OPNET (Optimized Network Engineering Tools) modeler, based on distributed agents, to prevent attacks from rogue virtual machine and enhance security of VM-to-VM communication. A set of mobile devices were given varying levels of access and pitched against some servers. Observing the packet network delays, phase response time for security apps and the coordination between these mobile devices and the installed agents on the servers showed that data belonging to tenants are safer and attacks from virtual machines are almost negligible.

References

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[3]. Che, J., Duan, Y., Zhang, T., & Fan, J. (2011). Study on the security models and strategies of cloud computing. Procedia Engineering, 23, 586-593.

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[9]. Kumar, V. K. A., Kumar, R., N., Kumar, K. K., & Kumar, S. N. K. (2014). Survey on security threats in cloud computing. International Journal of Applied Engineering Research (IJAER), 9(21), 10495-10500.

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Research Paper

An Extended Min-Min Scheduling Algorithm in Cloud Computing

J. Y. Maipan-uku* , A. Mishra, A. Abdulganiyu*, A. Abdulkadir****

, Lecturer, Department of Computer Science, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai (IBBUL), Niger State, Nigeria.

Senior Lecturer, Department of Computer Science, Baze University, Abuja, Nigeria.

**** Lecturer, Department of Mathematics, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai (IBBUL), Niger State, Nigeria.

Maipan-uku,J.Y., Mishra.A.,Abdulganiyu.A.,Abdulkadir.A.(2018).An Extended Min-Min Scheduling Algorithm in Cloud Computing, i-manager's Journal on Cloud Computing 5(2), 20-26. https://doi.org/10.26634/jcc.5.2.15693

Abstract

Cloudlet scheduling seems to be the most fundamental problem of cloud computing as per Infrastructure as a Service (IaaS). Proper scheduling in cloud lead to load balancing, minimization of makespan, and adequate resources utilization. To meet consumers' expectations, the execution of cloudlet simultaneously is required. Many algorithms have been implemented to solve the cloud scheduling problem. This include Min-Min which gave priority to cloudlet with minimum completion time. Min-Min scheduling algorithm has two clear weaknesses; a high value of makespan being generated and low resource utilization. To address these problems, this research proposes an Extended Min-Min Algorithm which assigns cloudlet base on the differences between maximum and minimum execution time of cloudlets. CloudSim was used to implement and compare the performances of the proposed algorithm with the benchmarks. The results of the extensive experiments show that the proposed algorithm is able to perform better in terms of makespan minimization compared to the existing heuristics.

References

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[2]. Abdullahi, M., & Ngadi, M. A. (2016). Symbiotic Organism Search optimization based task scheduling in cloud computing environment. Future Generation Computer Systems, 56, 640-650.

[3]. Agarwal, D., & Jain, S. (2014). Efficient optimal algorithm of task scheduling in cloud computing environment. arXiv preprint arXiv:1404.2076.

[4]. Choudhary, M., & Peddoju, S. K. (2012). A dynamic optimization algorithm for task scheduling in cloud environment. International Journal of Engineering Research and Applications (IJERA), 2(3), 2564-2568.

[5]. Duan, R., Prodan, R., & Li, X. (2014). Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds. IEEE Transactions on Cloud Computing, 2(1), 29-42.

[6]. Elzeki, O. M., Rashad, M. Z., & Elsoud, M. A. (2012). Overview of scheduling tasks in distributed computing systems. International Journal of Soft Computing and Engineering, 2(3), 470-475.

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[9]. Kaur, G., & Sharma, E. S. (2014). Optimized utilization of resources using improved particle swarm optimization based task scheduling algorithms in cloud computing. International Journal of Emerging Technology and Advanced Engineering, 4(6), 110-115.

[10]. Kaur, N., & Kaur, K. (2015). Improved max-min scheduling algorithm. IOSR Journal of Computer Engineering (IOSR-JCE), 17(3), 42-49.

[11]. Keshk, A. E., El-Sisi, A. B., & Tawfeek, M. A. (2014). Cloud task scheduling for load balancing based on intelligent strategy. International Journal of Intelligent Systems and Applications, 6(5), 25-36.

[12]. Konjaang, J., Ayob, F. H., & Muhammed, A. (2017). An optimized Max-Min scheduling algorithm in cloud computing. Journal of Theoretical & Applied Information Technology, 95(9), 1916-1926.

[13]. Konjaang, J. K., Maipan-uku, J. Y., & Kubuga, K. K. (2016). An efficient Max-Min resource allocator and task scheduling algorithm in cloud computing environment. arXiv preprint arXiv:1611.08864.

[14]. Lin, Y. D., Thai, M. T., Wang, C. C., & Lai, Y. C. (2015). Two-tier project and job scheduling for SaaS cloud service providers. Journal of Network and Computer Applications, 52, 26-36.

[15]. Liu, G., Li, J., & Xu, J. (2013). An improved min-min algorithm in cloud computing. In Proceedings of the 2012 International Conference of Modern Computer Science and Applications (pp. 47-52). Springer, Berlin, Heidelberg.

[16]. Marphatia, A., Muhnot, A., Sachdeva, T., & Shukla, E. (2013). Optimization of FCFS based Resource Provisioning Algorithm for cloud computing. IOSR Journal of Computer Engineering (IOSR-JCE), 10(5), 1-5.

[17]. Masdari, M., ValiKardan, S., Shahi, Z., & Azar, S. I. (2016). Towards workflow scheduling in cloud computing: A comprehensive analysis. Journal of Network and Computer Applications, 66, 64-82.

[18]. Mohammadi, F., Jamali, S., & Bekravi, M. (2014). Survey on job scheduling algorithms in cloud computing. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(2), 151-154.

[19]. Pasupuleti, S. K., Ramalingam, S., & Buyya, R. (2016). An efficient and secure privacy-preserving approach for outsourced data of resource constrained mobile devices in cloud computing. Journal of Network and Computer Applications, 64, 12-22.

[20]. Rana, M., Bilgaiyan, S., & Kar, U. (2014, July). A study on load balancing in cloud computing environment using evolutionary and swarm based algorithms. In Control, Instrumentation, Communication and Computational Technologies (ICCICCT), 2014 International Conference on (pp. 245-250). IEEE.

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[23]. Wei, G., Vasilakos, A. V., Zheng, Y., & Xiong, N. (2010). A game-theoretic method of fair resource allocation for cloud computing services. The Journal of Supercomputing, 54(2), 252-269.

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[25]. Zhou, Z., & Zhigang, H. (2014). Task scheduling algorithm based on greedy strategy in cloud computing. The Open Cybernetics & Systemics Journal, 8(1), 111-114.

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Review Paper

Data Quality Evaluation Framework for Big Data

Grace Amina Onyeabor * , Azman Ta’a**

*Lecturer Department of Information Science, University of Ibadan, Nigeria.

** Senior Lecturer, Department of Information Science, University of Ibadan, Nigeria.

Onyeabor,G.A., Ta’a,A.(2018). Data Quality Evaluation Framework for Big Data, i-manager's Journal on Cloud Computing 5(2), 27-35. https://doi.org/10.26634/jcc.5.2.15692

Abstract

Data is an important asset in all business organizations of today. Thus the results of its poor quality can be very grievous leading to erroneous insights. Therefore, Data Quality (DQ) needs to be evaluated before the analysis of any Big Data (BD). The evaluation of DQ in BD is challenging. Given the enormous datasets that are of varied format fashioned at a rapid speed, it is impossible to use the traditional methods of evaluating DQ in BD. Rather, there is a requirement of strategies and devices for the assessment and evaluation of DQ in BD in a rapid and more efficient manner. However, assessing the quality of data on the whole BD can be very expensive. In addition, there is also a need for improvement in data transformation activities of BD. This paper proposes a framework for DQ evaluation with the application of data sampling technique on BD sets from different data sources reducing the size of the data to samples representing the population of the BD sets. The Bag of Little Bootstrap (BLB) sampling technique will be used. The target Data Quality Dimensions (DQDs) to be used in this paper are completeness, consistency, and accuracy. In addition, the DQDs will be measured using different metric functions relevant to the DQDs. This will be done before and after an improved data transformation techniques to check the improvement of DQ in BD.

References

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Survey Paper

An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier

Haruna Atabo Christopher* , Jimoh Yakubu, Shafi’i Muhammad Abdulhamid*, Abdulmalik D. Mohammed****

- PG Scholar, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

**** Research Scholar, University of Manchester, United Kingdom.

Christopher,H.A., Yakubu.J., Abdulhamid,S.M., Mohammed,A.D.(2018). An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier, i-manager's Journal on Cloud Computing 5(2), 36-44. https://doi.org/10.26634/jcc.5.2.15691

Abstract

Cloud computing has become popular due to its numerous advantages, which include high scalability, flexibility, and low operational cost. It is a technology that gives access to shared pool of resources and services on pay per use and at minimum management effort over the internet. Because of its distributed nature, security has become a great concern to both cloud service provider and cloud users. That is why Cloud Intrusion Detection System (CIDS) has been widely used to the cloud computing setting, which detects and in some cases prevents intrusion. In this paper, the authors have proposed a conceptual framework that detects intrusion attacks within the cloud environment using Ant Lion Optimization (ALO) algorithm for feature selection and Bayesian Classifier. This framework is expected to detect cloud intrusion accurately at low computational cost and reduce false alert rate.

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

Current Issue Vol. 11 Issue 1

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Volume 5 Issue 2 July - December 2018

Development of Blowfish Encryption Scheme for Secure Data Storage in Public and Commercial Cloud Computing Environment

Shafi’i Muhammad Abdulhamid* , Nafisat Abubakar Sadiq**, Abdullahi Mohammed ***, Nadim Rana****, Haruna Chiroma*****, Emmanuel Gbenga Dada ******

Abdulhamid, S. M., Sadiq, N. A., Abdullahi, M., Rana, N., Chiroma, H., Dada, E.G (2018). Development of Blowfish Encryption Scheme for Secure Data Storage in Public and Commercial Cloud Computing Environment, i-manager's Journal on Cloud Computing 5(2), 1-10. https://doi.org/10.26634/jcc.5.2.15690

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Inter-Agent Coordinated Security Model for Cloud Based Virtual Machines

E. C. Onuoha* , O. P. Akomolafe **

* Lecturer, Federal Polytechnic, Bida, Nigeria. ** Lecturer, Department of Computer Science, University of Ibadan, Ibadan, Nigeria.

Onuoha,E.C., Akomolafe,O.P.(2018).Inter-Agent Coordinated Security Model for Cloud Based Virtual Machines, i-manager's Journal on Cloud Computing 5(2), 11-19. https://doi.org/10.26634/jcc.5.2.15688

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An Extended Min-Min Scheduling Algorithm in Cloud Computing

J. Y. Maipan-uku* , A. Mishra**, A. Abdulganiyu***, A. Abdulkadir****

Maipan-uku,J.Y., Mishra.A.,Abdulganiyu.A.,Abdulkadir.A.(2018).An Extended Min-Min Scheduling Algorithm in Cloud Computing, i-manager's Journal on Cloud Computing 5(2), 20-26. https://doi.org/10.26634/jcc.5.2.15693

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Data Quality Evaluation Framework for Big Data

Grace Amina Onyeabor * , Azman Ta’a**

*Lecturer Department of Information Science, University of Ibadan, Nigeria. ** Senior Lecturer, Department of Information Science, University of Ibadan, Nigeria.

Onyeabor,G.A., Ta’a,A.(2018). Data Quality Evaluation Framework for Big Data, i-manager's Journal on Cloud Computing 5(2), 27-35. https://doi.org/10.26634/jcc.5.2.15692

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An Architectural Framework for Ant Lion Optimization-based Feature Selection Technique for Cloud Intrusion Detection System using Bayesian Classifier

Haruna Atabo Christopher* , Jimoh Yakubu**, Shafi’i Muhammad Abdulhamid***, Abdulmalik D. Mohammed****

*-** PG Scholar, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria. *** Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria. **** Research Scholar, University of Manchester, United Kingdom.

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Shafi’i Muhammad Abdulhamid* , Nafisat Abubakar Sadiq, Abdullahi Mohammed *, Nadim Rana**, Haruna Chiroma*, Emmanuel Gbenga Dada ****

* Lecturer, Federal Polytechnic, Bida, Nigeria.

** Lecturer, Department of Computer Science, University of Ibadan, Ibadan, Nigeria.

J. Y. Maipan-uku* , A. Mishra, A. Abdulganiyu*, A. Abdulkadir****

*Lecturer Department of Information Science, University of Ibadan, Nigeria.

** Senior Lecturer, Department of Information Science, University of Ibadan, Nigeria.

Haruna Atabo Christopher* , Jimoh Yakubu, Shafi’i Muhammad Abdulhamid*, Abdulmalik D. Mohammed****

- PG Scholar, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

Senior Lecturer and Head, Department of Cyber Security Science, Federal University of Technology Minna, Nigeria.

**** Research Scholar, University of Manchester, United Kingdom.