Cloud computing is growing rapidly, with its server farms evolving at a remarkable rate. This paper tries to elaborate on a community cloud model that tries to use web application to identify maize disease through image matching. The model tries to match uploaded images with the images stored in the file system and if a match is found, the matching image is displayed together with its associated disease. The images were matched based on time, location and images collected from secondary sources to see if the disease can still be identified. All the images matched were successful and the appropriate diseases associated with the images were identified. The load balancing technology was also incorporated into the model mainly for the accessibility of the web application and to avoid overloading of one server. The image matching results were collected and tabulated as shown in chapter four and the results clearly indicated that the community cloud model really helped users to identify possible disease through the web application hosted on the community cloud model servers.

Cloud Computing, Infrastructure-as-a-Service, Web server, Image Matching, Load Balancing

[1] Alhakami, H., Aldabbas, H., & Alwada, T. (2012). COMPARISON BETWEEN CLOUD AND GRID COMPUTING : REVIEW PAPER, 2(4), 1–21.
[2] Al Nuaimi K, Mohamed N, Al Nuaimi M, Al-Jaroodi J 2012 A survey of load balancing in cloud computing: challenges and algorithms In Proceedings - IEEE 2nd Symposium on Network Cloud Computing and Applications, NCCA 2012 p. 137–42
[3] Ahmed, M., & Hossain, M. A. (2014). CLOUD COMPUTING AND SECURITY ISSUES IN THE Cloud. International Journal of Network Security & Its Applications, 6(1), 25–36. http://doi.org/10.5121/ijnsa.2014.6103
[4] Arokia, R., Rajan, P., & Shanmugapriyaa, S. (2013). Evolution of Cloud Storage as a Cloud Computing Infrastructure Service. IOSR Journal of Computer Engineering (IOSRJCE), 1(1), 38–45. Retrieved from http://arxiv.org/abs/1308.1303
[5] Badidi, E. (2013). A Framework for Software-as-a-Service Selection and Provisioning. International Journal of Computer Networks and Communications (IJCNC), 5(3), 12. http://doi.org/10.5121/ijcnc.2013.5314
[6] Basmadjian, R., Meer, H., Lent, R., & Giuliani, G. (2012). Cloud computing and its interest in saving energy: the use case of a private cloud. Journal of Cloud Computing: Advances, Systems, and Applications, 1(1), 5. http://doi.org/10.1186/2192-113X-1-5
[7] Computing, M., Jaiswal, P. R., & Rohankar, A. W. (2014). Infrastructure as a Service : Security Issues in Cloud Computing, 3(3), 707–711.
[8] Dave S, Maheta P 2014 Utilizing round robin concept for load balancing algorithm at virtual machine level in cloud environment Int J Comput Appl [Internet] 94(4) 23–9
[9] Desai T, Prajapati J 2013 A survey of various load balancing techniques and challenges in cloud computing Int J Sci Technol Res [Internet] 2(11) 158–61
[10] Distributed load balancing algorithms for cloud computing 24th IEEE Int Conf Adv Inf Netw Appl Work WAINA [Internet] 551–6
[11] Gopinath P P G, Vasudevan S K 2015 An In-depth analysis and study of load balancing techniques in the cloud computing environment. Procedia Comput Sci [Internet] Elsevier Masson SAS 50 427–32
[12] Gulati, A., & Chopra, R. K. (2013). Dynamic round robin for load balancing in a cloud computing. IJCSMC, 2(6), 274-278.
[13] Haryani N, Jagli D, Sangita O, Dhanamma J, Jagli1 M D, Solanki R, et al. 2014 Dynamic Method for Load Balancing in Cloud Computing Int Conf Circuits, Syst Commun Inf Technol Appl [Internet] 5(4) 336–40