i-manager Publications

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

Current Issue Vol. 10 Issue 2

Most Cited

Volume 6 Issue 1 January - June 2019

Article

A Taxonomy of Issues, Challenges and Applications in Internet of Multimedia Things (IoMMT)

G. Rama Subba Reddy* , M. Ajay kumar, N. V. Kishore kumar*

* Department of Computer Science & Engineering, Mother Theresa Institute of Engineering & Technology, Palamaner, Andhra Pradesh, India.

Department of Electronics and Communication Engineering, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India.

* Department of Electrical and Electronics Engineering, Mother Theresa Institute of Engineering & Technology, Palamaner, Andhra Pradesh, India.

Reddy, G. R. S., Kumar, M. A., Kumar, N. V. K.(2019).A Taxonomy of Issues, Challenges and Applications in Internet of Multimedia Things (IoMMT), i-manager's Journal on Cloud Computing, 6(1), 1-8. https://doi.org/10.26634/jcc.6.1.16178

Abstract

In IoT technology, Multimedia big data which is said to be the large amount of data from multimedia devices will be generated with the fast growth of the multimedia gadgets. The IoT systems are failed in realizing the multimedia devices connectivity unless they are able in processing multimedia gadgets at a time. In contrast, earlier activities concerning research and development concentrate on the scaling strategies to sensor information gathered from many IoT gadgets. Nonetheless the present activities of development and the research do not made mandatory about the features of connectivity between the objects of multimedia. In this paper, we mainly concentrated on the above issue by considering the IoT concept and the advantages are taken to sight towards the IoMMT’s (Internet of Multimedia Things) vision. This paper describes the classification of computing the multimedia big data and also the challenges in models of multimedia computing along with the applications of IoT are discussed. . In addition to this we presented the taxonomy of Multimedia Things (MMT) along with the current research challenges like heterogeneity, reliability, scalability, and accessibility and Quality of Service requirements.

References

[1]. Aazam, M., & Huh, E. N. (2016). Fog computing: The cloud-iot\/ioe middleware paradigm. IEEE Potentials, 35(3), 40-44.

[2]. Alvi, S. A., Afzal, B., Shah, G. A., Atzori, L., & Mahmood, W. (2015). Internet of multimedia things: Vision and challenges. Ad Hoc Networks, 33, 87-111.

[3]. Aslanpour, M. S., Ghobaei-Arani, M., & Toosi, A. N. (2017). Auto-scaling web applications in clouds: A costaware approach. Journal of Network and Computer Applications, 95, 26-41.

[4]. Bui, D. M., Yoon, Y., Huh, E. N., Jun, S., & Lee, S. (2017). Energy efficiency for cloud computing system based on predictive optimization. Journal of Parallel and Distributed Computing, 102, 103-114.

[5]. Caron, E., Desprez, F., & Muresan, A. (2010, November). Forecasting for grid and cloud computing on-demand resources based on pattern matching. In 2010 IEEE Second International Conference on Cloud Computing Technology and Science (pp. 456-463). IEEE.

[6]. Ghomi, E. J., Rahmani, A. M., & Qader, N. N. (2017). Load-balancing algorithms in cloud computing: A survey. Journal of Network and Computer Applications, 88, 50- 71.

[7]. Hameed, A., Khoshkbarforoushha, A., Ranjan, R., Jayaraman, P. P., Kolodziej, J., Balaji, P., ... & Khan, S. U. (2016). A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems. Computing, 98(7), 751-774.

[8]. Hourdakis, J., Morris, T., Michalopoulos, P., & Wood, K. (2005). Advanced portable wireless measurement and observation station. Final Report, University of Minneseta.

[9]. Kawamura, A., Yoshimitsu, Y., Kajitani, K., Naito, T., Fujimura, K., & Kamijo, S. (2011, October). Smart camera network system for use in railway stations. In 2011 IEEE International Conference on Systems, Man, and Cybernetics (pp. 85-90). IEEE.

[10]. Kumari, A., Tanwar, S., Tyagi, S., Kumar, N., Maasberg, M., & Choo, K. K. R. (2018). Multimedia big data computing and Internet of Things applications: A taxonomy and process model. Journal of Network and Computer Applications, 124, 169-195.

[11]. Leader, S. (2004). Telecommunications handbook for transportation professionals: The basics of telecommunications (No. FHWA-HOP-04-034). United States. Federal Highway Administration.

[12]. Li, N., Yan, B., Chen, G., Govindaswamy, P., & Wang, J. (2010). Design and implementation of a sensor-based wireless camera system for continuous monitoring in assistive environments. Personal and Ubiquitous Computing, 14(6), 499-510.

[13]. Luo, N. (2011). A wireless traffic surveillance system using video analytics (Doctoral dissertation, University of North Texas).

[14]. Manvi, S. S., & Shyam, G. K. (2014). Resource management for Infrastructure as a Service (IaaS) in cloud computing: A survey. Journal of Network and Computer Applications, 41, 424-440.

[15]. Sahni, Y., Cao, J., Zhang, S., & Yang, L. (2017). Edge Mesh: A new paradigm to enable distributed intelligence in Internet of Things. IEEE Access, 5, 16441-16458.

[16]. Sharkh, M. A., & Shami, A. (2017). An evergreen cloud: Optimizing energy efficiency in heterogeneous cloud computing architectures. Vehicular Communications, 9, 199-210.

[17]. Sun, L. N. (2014). Building intelligent parking lot based on RFID and cloud computing technology. In Advanced Materials Research (Vol. 846, pp. 1550-1553). Trans Tech Publications.

[18]. Vázquez-Poletti, J. L., Moreno-Vozmediano, R., Han, R., Wang, W., & Llorente, I. M. (2017). SaaS enabled admission control for MCMC simulation in cloud computing infrastructures. Computer Physics Communications, 211, 88-97.

[19]. Wang, W., De, S., Toenjes, R., Reetz, E., & Moessner, K. (2012, June). A comprehensive ontology for knowledge representation in the internet of things. In 2012 IEEE 11^th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 1793- 1798). IEEE.

[20]. Zhu, W., Zhuang, Y., & Zhang, L. (2017). A three-dimensional virtual resource scheduling method for energy saving in cloud computing. Future Generation Computer Systems, 69, 66-74.

Full Article (HTML)

Pdf

Research Paper

A Dynamic Replication Mechanism in Data Grid Based on a Weighted Priority-based Scheme

Mohammad Samadi Gharajeh*

Young Researchers and Elite Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Gharajeh, M. S.(2019). A Dynamic Replication Mechanism in Data Grid Based on a Weighted Priority-based Scheme, i-manager's Journal on Cloud Computing, 6(1), 9-18. https://doi.org/10.26634/jcc.6.1.15897

Abstract

Replication is one of the popular tools to determine the availability degree of resources (e.g., data files) in data grids. Since data grids involve limited file storages and high computing costs, replication process is very essential in these networks. This paper proposes a dynamic replication mechanism in a data grid that uses a weighted priority-based replication scheme, called WPRS. It specifies a value for existing each in a local storage based on three parameters including price, number of access time, and present time. When a resource is not available for a desired job, it is hired from other sites in the network. The proposed mechanism removes the file having the least value to increase the free space of data storage. Simulation results show that the proposed replication mechanism surpasses some of the existing replication methods in terms of the number of successful jobs, number of non successful jobs, and buy price.

References

[1]. Al Mistarihi, H. H. E., & Yong, C. H. (2008). Replica management in data grid. International Journal of Computer Science and Network Security, 8(6), 22-32.

[2]. Alkhanak, E. N., Lee, S. P., Rezaei, R., & Parizi, R. M. (2016). Cost optimization approaches for scientific workflow scheduling in cloud and grid computing: A review, classifications, and open issues. Journal of Systems and Software, 113, 1-26.

[3]. Chamola, V., Sikdar, B., & Krishnamachari, B. (2017). Delay aware resource management for grid energy savings in green cellular base stations with hybrid power supplies. IEEE Transactions on Communications, 65(3), 1092-1104.

[4]. Chang, R. S., Chang, H. P., & Wang, Y. T. (2008, March). A dynamic weighted data replication strategy in data grids. In 2008 IEEE/ACS International Conference on Computer Systems and Applications (pp. 414-421). IEEE.

[5]. Gharajeh, M. S. (2015). The significant concepts of cloud computing : Technology, architecture, applications, and security. CreateSpace Independent Publishing Platform.

[6]. Gharajeh, M. S. (2017). Applications of virtualization technology in grid systems and cloud servers. In P. K. Das and G. C. Deka (Eds.), Design and Use of Virtualization Technology in Cloud Computing (pp. 1-28). Hershey, PA: IGI Global

[7]. Gill, S. S., Chana, I., Singh, M., & Buyya, R. (2018). CHOPPER: An intelligent QoS-aware autonomic resource management approach for cloud computing. Cluster Computing, 21(2), 1203-1241.

[8]. Granville, A., & Rudnick, Z. (Eds.). (2007). Equidistribution in number theory, an introduction (Vol. 237). Springer Science & Business Media.

[9]. Hossain, E., Han, Z., & Poor, H. V. (Eds.). (2012). Smart Grid Communications and Networking. Cambridge University Press.

[10]. Jinno, M., Kozicki, B., Takara, H., Watanabe, A., Sone, Y., Tanaka, T., & Hirano, A. (2010). Distance-adaptive spectrum resource allocation in spectrum- sliced elastic optical path network [topics in optical communications]. IEEE Communications Magazine, 48(8), 138-145.

[11]. Krašovec, B., & Filipčič, A. (2019). Enhancing the Grid with Cloud Computing. Journal of Grid Computing, 17(1), 119-135.

[12]. Kuipers, L., & Niederreiter, H. (2012). Uniform Distribution of Sequences. Courier Corporation.

[13]. Mahmood, A., Ullah, M. N., Razzaq, S., Basit, A., Mustafa, U., Naeem, M., & Javaid, N. (2014). A new scheme for demand side management in future smart grid networks. Procedia Computer Science, 32, 477-484.

[14]. Mansouri, N., & Dastghaibyfard, G. H. (2013). Enhanced dynamic hierarchical replication and weighted scheduling strategy in data grid. Journal of Parallel and Distributed Computing, 73(4), 534-543.

[15]. Mariotti, M., Gervasi, O., Vella, F., Cuzzocrea, A., & Costantini, A. (2018). Strategies and systems towards grids and clouds integration: A DBMS-based solution. Future Generation Computer Systems, 88, 718-729.

[16]. Mishra, M. K., Patel, Y. S., Ghosh, M., & Mund, G. B. (2017). A review and classification of grid computing systems. International Journal of Computational Intelligence Research, 13(3), 369-402.

[17]. Nazir, B., Ishaq, F., Shamshirband, S., & Chronopoulos, A. (2018). The impact of the implementation cost of replication in data grid job scheduling. Mathematical and Computational Applications, 23(2), 1-19.

[18]. Nicholson, C., Cameron, D. G., Doyle, A. T., Millar, A. P., & Stockinger, K. (2008). Dynamic data replication in lcg 2008. Concurrency and Computation: Practice and Experience, 20(11), 1259-1271.

[19]. O'neil, E. J., O'neil, P. E., & Weikum, G. (1993). The LRU-K page replacement algorithm for database disk buffering. ACM Sigmod Record, 22(2), 297-306.

[20]. Prischepa, V. (2004). An efficient web caching algorithm based on LFU-K replacement policy. In Proceedings of the Spring Young Researcher's Colloquium on Database and Information Systems (pp. 1- 5). IEEE.

[21]. Rahman, R. M., Barker, K., & Alhajj, R. (2009). Performance evaluation of different replica placement algorithms. International Journal of Grid and Utility Computing, 1(2), 121-133.

[22]. Ranganathan, K., Iamnitchi, A., & Foster, I. (2002, May). Improving data availability through dynamic model-driven replication in large peer-to-peer communities. In 2^ndIEEE/ACM International Symposium on Cluster Computing and the Grid (CCGRID'02) (pp. 376-376). IEEE.

[23]. Sashi, K., & Thanamani, A. S. (2011). Dynamic replication in a data grid using a modified BHR region based algorithm. Future Generation Computer Systems, 27(2), 202-210.

[24]. Tang, M., Lee, B. S., Tang, X., & Yeo, C. K. (2006). The impact of data replication on job scheduling performance in the Data Grid. Future Generation Computer Systems, 22(3), 254-268.

[25]. Wang, L., Jie, W., & Chen, J. (2018). Grid Computing: Infrastructure, Service, and Applications. CRC Press.

[26]. Weng, Y., Negi, R., Faloutsos, C., & Ilić, M. D. (2017). Robust data-driven state estimation for smart grid. IEEE Transactions on Smart Grid, 8(4), 1956-1967.

[27]. Wilkinson, B. (2009). Grid Computing: Techniques and Applications. CRC Press.

Full Article (HTML)

Pdf

Research Paper

Adoption of Cloud Computing in Palestinian Ministry of Telecommunication and Information Technology: A Framework Development

Ramiz Assaf*

Department of Industrial Engineering, University of Business and Technology, Jeddah, Saudi Arabia.

Assaf, R.(2019). Adoption of Cloud Computing in Palestinian Ministry of Telecommunication and Information Technology: A Framework Development, i-manager's Journal on Cloud Computing, 6(1), 19-26. https://doi.org/10.26634/jcc.6.1.15491

Abstract

In the information technology sector cloud computing has clearly become very strong driving force by taking over 90% of global enterprises using cloud as major part of their business. This study aims to introduce a comprehensive framework for the adoption of Cloud Computing and to determine the influencing factors in Palestinian Ministry of Telecommunication and Information Technology from point of view of employees from the ministry. Depending on two prominent frameworks: Technology-Organization-Environment framework and Diffusion of innovation framework. The researcher used the quantitative methodology to answer the research question and test the hypotheses. The questionnaire was delivered to the employees and received from 69 respondents from 75 disrupted survey. In order to evaluate the internal, convergent and discriminant validity of the instrument, validity and reliability tests of panel data were performed. The linear regression analysis was deployed to test the research hypotheses and come out with findings, recommendations and future to consider it when developing a framework to adopt Statistical analysis shows that there are some factors were rejected and other were accepted. The supported factors were: Competitive Pressure, External Support, Security, Top Management Support and Organization Readiness and the others are not significant.

References

[1]. Craig, R., Frazier, J., Jacknis, N., Murphy, S., Purcell, C., Spencer, P., & Stanley, J. (2009). Cloud Computing in the Public Sector: Public Manager's Guide to Evaluating and Adopting Cloud Computing [White Paper]. Cisco Internet Business Solutions Group.

[2]. Depietro, R., Wiarda, E., & Fleischer, M. (1990). The context for change: Organization, technology and environment. The Processes of Technological Innovation, 199(0), 151-175.

[3]. Eurofin Hospitality Report. (2013). Diagnostic Study- Palestine: Export Readiness & Potential of Services Sectors. PALTRADE, Ramallah, Palestine. Retrieved from https://www.paltrade.org/upload/multimedia/admin/201 4/08/53f19b2f06485.pdf

[4]. Grover, V. (1993). An empirically derived model for the adoption of customer based interorganizational systems. Decision Sciences, 24(3), 603-640.

[5]. Hayes, B. (2008). Cloud computing. Communications of the ACM, 51(7), 9-11.

[6]. Ifinedo, P. (2011). An empirical analysis of factors influencing Internet/e-business technologies adoption by SMEs in Canada. International Journal of Information Technology & Decision Making, 10(4), 731-766.

[7]. Jansen, W., & Grance, T. (2011). Guidelines on Security and Privacy in Public Cloud Computing (No. Special Publication (NIST SP)-800-144).

[8]. Mishra, A. N., Konana P., & Barua A. (2005). Antecedents and consequences of internet use in procurement: An empirical investigation of us manufacturing firms. Information Systems Research, 18(1), 103-120.

[9]. Nanos, I., Manthou, V., & Androutsou, E. (2019). Cloud computing adoption decision in E-government. In Operational Research in the Digital Era–ICT Challenges (pp. 125-145). Springer, Cham.

[10]. Rogers, E. M. (1995). Diffusion of Innovations (4^th Ed.). New York: The Free Press. Scientific Research an Academic Publisher.

[11]. Rogers, E. M. (2010). Diffusion of Innovations. Germany: Simon and Schuster.

[12]. Sean, C., & Kevin, C. (2012). Cloud Computing Technologies. International Journal of Cloud Computing and Services Science (IJ-CLOSER) 1(2), 59-65.

[13]. Seyal, A., Awais, M. M., Shamai, S., & Abbas, A. (2004). Determinants of electronic commerce in Pakistan: Preliminary evidence from small and medium enterprises. Electronic Markets, 14(4), 372-387.

[14]. Takabi, H., Joshi, J. B., & Ahn, G. J. (2010). Security and privacy challenges in cloud computing environments. IEEE Security & Privacy, 8(6), 24-31.

[15]. Voorsluys, W., Broberg, J., & Buyya, R. (2011). Introduction to cloud computing. In Cloud Computing: Principles and Paradigms (pp. 1-41).

[16]. Yan, X., & Su, X. (2009). Linear regression analysis: Theory and computing. World Scientific. Retrieved from http://www.manalhelal.com/Books/geo/LinearRegression AnalysisTheoryandComputing.pdf

Full Article (HTML)

Pdf

Research Paper

Privacy Preservation Guidelines Based Substance Distribution in Public Clouds

S. Muthuraj Kumar *

Department of Computer Technology, MIT Campus, Anna University, Chennai, Tamil Nadu, India.

Kumar, S. M.(2019). Privacy Preservation Guidelines Based Substance Distribution in Public Clouds, i-manager's Journal on Cloud Computing, 6(1), 27-35. https://doi.org/10.26634/jcc.6.1.16160

Abstract

Cloud data storage has the biggest challenge on the maintenance of data integrity at untrusted servers. Cloud computing delivers cloud services such as databases, networking, etc. The service charge for cloud services is based on the usage of the services. Cloud databases provide facilities for large scale data storage and retrieval of distributed data. In order to provide effective data integrity and security, it is necessary to propose new storage and retrieval algorithms for cloud databases. The cloud storage provides a secure way to remotely store the important information. In Cloud data is stored on remote servers and retrieved from the any internet or cloud. Cloud Security consists of set of policies to protect data, applications and infrastructure of cloud computing. In proposed system, the secure cloud data distribution in clouds provides data confidentiality, truthfulness and data access control. The secure cloud data sharing method encrypts a data file with anonly encryption key. Two different key segments for everyone of the users are engendered, with the user only receiving one segment. Thereby, security level is increased in the cloud databases by using the privilege based hierarchy values.

References

[1]. Alharbi, K. N., Lin, X., & Shao, J. (2016). A privacy-preserving data-sharing framework for smart grid. IEEE Internet of Things Journal, 4(2), 555-562.

[2]. Anglano, C., Gaeta, R., & Grangetto, M. (2016). Securing coding-based cloud storage against pollution attacks. IEEE Transactions on Parallel and Distributed Systems, 28(5), 1457-1469.

[3]. Arora, A., Khanna, A., Rastogi, A., & Agarwal, A. (2017, January). Cloud security ecosystem for data security and privacy. In 2017 7^thInternational Conference on Cloud Computing, Data Science & Engineering- Confluence (pp. 288-292). IEEE.

[4]. Cordeiro, F. R., & da Silva-Filho, A. G. (2016). Multi-objective optimization applied to unified second level cache memory hierarchy tuning aiming at energy and performance optimization. Applied Soft Computing, 49, 603-610.

[5]. Cui, B., Liu, Z., & Wang, L. (2015). Key-aggregate searchable encryption (KASE) for group data sharing via cloud storage. IEEE Transactions on Computers, 65(8), 2374-2385.

[6]. Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., & Wernsing, J. (2017). Manual for using Homomorphic encryption for bioinformatics. Proceedings of the IEEE, 105(3), 552-567.

[7]. Ghorpade, N., Vijaykarthik, P., Dhananjaya, V., & Balasubramani, R. (2017, January). Towards Achieving Efficient and Secure Way to Share the Data. In 2017 IEEE 7^thInternational Advance Computing Conference (IACC) (pp. 11-14). IEEE.

[8]. Haider, A., Hassan, A., Shahzadi, K., & Fatima, M. (2018). A Comprehensive Review of Security Issues in Cloud Computing. i-manager's Journal on Cloud Computing, 5(1), 12-19. https://doi.org/10.26634/jcc. 5.1.15448.

[9]. Henry, S., & Ali, M. L. (2017). Cloud Computing Security Threats and Solutions. i-manager's Journal on Cloud Computing, 4(2), 1-8. https://doi.org/10. 26634/jcc.4.2. 14249.

[10]. Hur, J., Koo, D., Shin, Y., & Kang, K. (2016). Secure Data deduplication with dynamic ownership management in cloud storage. IEEE Transactions on Knowledge and Data Engineering, 28(11), 3113-3125.

[11]. Li, J., Ma, R., & Guan, H. (2015). Tees: An efficient search scheme over encrypted data on mobile cloud. IEEE Transactions on Cloud Computing, 5(1), 126-139.

[12]. Li, K., Zhang, W., Yang, C., & Yu, N. (2015). Security analysis on one-to-many order preserving encryptionbased cloud data search. IEEE Transactions on Information Forensics and Security, 10(9), 1918-1926.

[13]. Liang, K., & Susilo, W. (2015). Searchable attributebased mechanism with efficient data sharing for secure cloud storage. IEEE Transactions on Information Forensics and Security, 10(9), 1981-1992.

[14]. Lin, G., Hong, H., & Sun, Z. (2017). A collaborative key management protocol in ciphertext policy attributebased encryption for cloud data sharing. IEEE Access, 5, 9464-9475.

[15]. Muthurajkumar, S., Ganapathy, S., Vijayalakshmi, M., & Kannan, A. (2017). An intelligent secured and energy efficient routing algorithm for MANETs. Wireless Personal Communications, 96(2), 1753-1769. https://doi.org/ 10.1007/s11277-017-4266-4

[16]. Muthurajkumar, S., Vijayalakshmi, M., & Kannan, A. (2017). Secured data storage and retrieval algorithm using map reduce techniques and chaining encryption in cloud databases. Wireless Personal Communications, 96(4), 5621-5633. https://doi.org/10.1007/s11277-017- 4437-3

[17]. Naseera, S., Gopichand, G., & Ande, B. P. (2018). On security of information access for multi expert cloud frameworks. i-manager's Journal on Cloud Computing, 5(1), 7. https://doi.org/10.26634/jcc.5.1.15367.

[18]. Onuoha, E. C., & Akomolafe, O. P. (2018). Interagent coordinated security model for cloud based virtual machines. i-manager's Journal on Cloud Computing, 5(2), 11. https://doi.org/10.26634/jcc.5.2.15688

[19]. Parsamehr, R., & Nezhad, S. F. H. (2016, December). Mutual authentication protocol to share files in cloud storage. In 2016 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) (pp. 153-158). IEEE.

[20]. Prasad, K. L., & Anusha, P. (2017). Implementing preserved access of cloud networking. i-manager's Journal on Cloud Computing, 4(1), 8-14. https://doi.org/ 10.26634/jcc.4.1.13753.

[21]. Saroj, S. K., Chauhan, S. K., Sharma, A. K., & Vats, S. (2015, February). Threshold cryptography based data security in cloud computing. In 2015 IEEE International Conference on Computational Intelligence & Communication Technology (pp. 202-207). IEEE.

[22]. Suryawanshi, R., & Shelke, S. (2016, August). Improving data storage security in cloud environment using public auditing and threshold cryptography scheme. In 2016 International Conference on Computing Communication Control and automation (ICCUBEA) (pp. 1-6). IEEE.

[23]. Tamilarasi, K., & Ramya, V. (2017). HABCO: A hybrid algorithm to improve load balancing in cloud computing. i-manager's Journal on Cloud Computing, 4(1), 15-22. https://doi.org/10.26634/jcc.4.1.13754.

[24]. Xue, K., Xue, Y., Hong, J., Li, W., Yue, H., Wei, D. S., & Hong, P. (2017). RAAC: Robust and auditable access control with multiple attribute authorities for public cloud storage. IEEE Transactions on Information Forensics and Security, 12(4), 953-967.

[25]. Zhu, Z., & Jiang, R. (2015). A secure anti-collusion data sharing scheme for dynamic groups in the cloud. IEEE Transactions on Parallel and Distributed Systems, 27(1), 40-50.

Full Article (HTML)

Pdf

Research Paper

Performance Analysis and Evaluation of Multi-Cloud systems

R. Sugumar* , A. Rajesh**

* Department of Computer Science & Engineering, Sri Chandrashekarendra Saraswathi Viswa Maha Vidyalaya University, Kanchipuram, Tamil Nadu, India.

** C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Vellore District, Tamil Nadu, India.

Sugumar, R., Rajesh, A.(2019). Performance Analysis and Evaluation of Multi-Cloud systems, i-manager's Journal on Cloud Computing, 6(1), 36-42. https://doi.org/10.26634/jcc.6.1.16201

Abstract

Many global enterprises are interested in involving their activities in mature multi-cloud strategies. To avail the resources from multiple public and private clouds concerning their purpose-specific cloud environments. Such a multi-cloud environment allows organizations and enterprises to manage their applications among various remote cloud providers. To proliferate the users/organizations, we present some of the existing multi-cloud environments to their optimization in such application centric. In this paper, we analyzed with CloudChekr– that makes the users analyze cloud resources use dependent on the summary and highlights. Simultaneously, it makes it simple as users monitor and analyze the cloud cost by giving highlights to cost designation, cloud optimization, invoicing and chargeback. The reports created and alert sent by CloudChekr even encourage individual/organizations to scale the cloud framework on time without expanding cloud cost in the Cloudchekr dashboard.Multcloud and Odrive a multicloud environment, a portal with different CSPs and also discussed with various aspects of storage, cost optimization, billing policies and security aspects in Multi-cloud environment. This will impact in increasingly users/organization to adopt multi-cloud strategies.

References

[1]. Akrami, H., Mohammadzadeh, N.,& Vasumathi, D. (2014). Performance analysis of cloud server in multi cloud environment. International Journal of Computer Science and Technology (IJCST), 5(3), 79-82.

[2]. AL-Mukhtar, M. M., & Mardan, A. A. A. (2014). Performance evaluation of private clouds Eucalyptus versus CloudStack. International Journal of Advanced Computer Science and Applications, 5(5), 108-117.

[3]. Bobák, M., Hluchý, L., & Tran, V. (2017). Application performance optimization in multicloud environment. Computing and Informatics, 35(6), 1359-1385.

[4]. Duan, Q. (2017). Cloud service performance evaluation: Status, challenges, and opportunities–a survey from the system modeling perspective. Digital Communications and Networks, 3(2), 101-111.

[5]. Franceschelli, D., Ardagna, D., Ciavotta, M., & Di Nitto, E. (2013, April). Space4cloud: A tool for system performance and costevaluation of cloud systems. In Proceedings of the 2013 International Workshop on Multi- Cloud Applications and Federated Clouds (pp. 27-34). ACM.

[6]. Guerriero, M., Ciavotta, M., Gibilisco, G. P., & Ardagna, D. (2015, September). SPACE4Cloud: A DevOps environment for multi-cloud applications. In Proceedings of the 1^st International Workshop on Quality- Aware DevOps (pp. 29-30). ACM.

[7]. Gupta, L., Jain, R., Samaka, M., Erbad, A., & Bhamare, D. (2016). Performance evaluation of multicloud management and control systems. Recent Advances in Communications and Networking Technology (Formerly Recent Patentson Telecommunication), 5(1), 9-18.

[8]. He, C., & Jain, R. (2011). Performance analysis based on two leading cloud computing platforms google app engine and amazon web service. In Jain, R, (Ed.) The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling.

[9]. Indira, K., & Devi, M. K. (2014, December). Effective integrated parallel distributed processing approach in optimized multi-cloud computing environment. In 2014 Sixth International Conference on Advanced Computing (ICoAC) (pp. 19-22). IEEE.

[10]. Jain, S., Kumar, R., & Anamika, S. K. J. (2014). A comparative study for cloud computing platform on open source software. ABHIYANTRIKI: An International Journal of Engineering & Technology (AIJET), 1(2), 28-35.

[11]. Odrive. (n.d). Is Your Cloud Storage Slow As Shit? Retrieved from https://medium.odrive.com/is-yourcloud- storage-slow-as-shit-f021f5ff5e11

[12]. Qadri, M. N., & Quadri, S. M. K. (2017). A study of mapping educational institute with cloud computing. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2(6), 59-66.

[13]. Rowan, S. (2017). What is the Future of Multi-Cloud Computing? Retrieved from https://stackify.com/futuremulti- cloud-computing/

[14]. Sefraoui, O., Aissaoui, M., & Eleuldj, M. (2012). OpenStack: Toward an open-source solution for cloud computing. International Journal of Computer Applications, 55(3), 38-42.

[15]. Senthil, K. (2012). Performance analysis of multicloud deployment in many task applications. International Journal of Engineering Research and Technology, 1(5), 1-6.

[16]. TechTarget. (2019). Effective multi-cloud storage strategies and management techniques. Retrieved from https://searchstorage.techtarget.com/tip/8-steps-toeffective- multi-cloud-cost-management

[17]. Yeasmin, M., Akter, N., Kabir, M. H., & Hossain, J. (2018). Per formance evaluation of multi-cloud compared to the single-cloud under varying firewall conditions. Cogent Engineering, 5(1), 1-13.

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

Current Issue Vol. 10 Issue 2

Most Read

Most Cited

Volume 6 Issue 1 January - June 2019

A Taxonomy of Issues, Challenges and Applications in Internet of Multimedia Things (IoMMT)

G. Rama Subba Reddy* , M. Ajay kumar**, N. V. Kishore kumar***

Reddy, G. R. S., Kumar, M. A., Kumar, N. V. K.(2019).A Taxonomy of Issues, Challenges and Applications in Internet of Multimedia Things (IoMMT), i-manager's Journal on Cloud Computing, 6(1), 1-8. https://doi.org/10.26634/jcc.6.1.16178

Abstract

References

Full Article (HTML)

Pdf

A Dynamic Replication Mechanism in Data Grid Based on a Weighted Priority-based Scheme

Mohammad Samadi Gharajeh*

Young Researchers and Elite Club, Tabriz Branch, Islamic Azad University, Tabriz, Iran.

Gharajeh, M. S.(2019). A Dynamic Replication Mechanism in Data Grid Based on a Weighted Priority-based Scheme, i-manager's Journal on Cloud Computing, 6(1), 9-18. https://doi.org/10.26634/jcc.6.1.15897

Abstract

References

Full Article (HTML)

Pdf

Adoption of Cloud Computing in Palestinian Ministry of Telecommunication and Information Technology: A Framework Development

Ramiz Assaf*

Department of Industrial Engineering, University of Business and Technology, Jeddah, Saudi Arabia.

Assaf, R.(2019). Adoption of Cloud Computing in Palestinian Ministry of Telecommunication and Information Technology: A Framework Development, i-manager's Journal on Cloud Computing, 6(1), 19-26. https://doi.org/10.26634/jcc.6.1.15491

Abstract

References

Full Article (HTML)

Pdf

Privacy Preservation Guidelines Based Substance Distribution in Public Clouds

S. Muthuraj Kumar *

Department of Computer Technology, MIT Campus, Anna University, Chennai, Tamil Nadu, India.

Kumar, S. M.(2019). Privacy Preservation Guidelines Based Substance Distribution in Public Clouds, i-manager's Journal on Cloud Computing, 6(1), 27-35. https://doi.org/10.26634/jcc.6.1.16160

Abstract

References

Full Article (HTML)

Pdf

Performance Analysis and Evaluation of Multi-Cloud systems

R. Sugumar* , A. Rajesh**

* Department of Computer Science & Engineering, Sri Chandrashekarendra Saraswathi Viswa Maha Vidyalaya University, Kanchipuram, Tamil Nadu, India. ** C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Vellore District, Tamil Nadu, India.

Sugumar, R., Rajesh, A.(2019). Performance Analysis and Evaluation of Multi-Cloud systems, i-manager's Journal on Cloud Computing, 6(1), 36-42. https://doi.org/10.26634/jcc.6.1.16201

Abstract

References

Full Article (HTML)

Pdf

G. Rama Subba Reddy* , M. Ajay kumar, N. V. Kishore kumar*

* Department of Computer Science & Engineering, Sri Chandrashekarendra Saraswathi Viswa Maha Vidyalaya University, Kanchipuram, Tamil Nadu, India.

** C. Abdul Hakeem College of Engineering and Technology, Melvisharam, Vellore District, Tamil Nadu, India.