CLOUD-ENABLED UNIFIED RESOURCE SHARING MODEL FOR ETHIOPIA UNIVERSITY NETWORK (CASE OF CLUSTERED FEDERAL UNIVERSITIES

Abstract

loud computing has emerged as a technology that can realize effective and efficient resource sharing with the promised assurance of high-end computing services. Ethiopian universities are classified into four generations. This university's network has numerous redundant data centers with underutilized resources. This high cost and fast obsoleting nature of computing resources are usually either underutilized or unequally distributed by obsolete techno policies. Ethiopia is a developing country where some of the universities have their own high-end data centers equipped with ultra-modern underutilized computing resources however, the other universities are typically lacking in access to basic computing resources. The typical challenging concerns are lack of collaboration, communication, and poor sharing of resources. The ability to integrate resources across regions and databases helps bring together disparate data sources in a way that improves utilization; cloud nodes may added or eliminated with ease, and their size can be increased to expand resources to address issues. The study's data collection strategy would involve using a Google form to conduct an online survey. Salient modern technology-based computing models designed after the Corona age can ensure the high-end facilitation of data center’s computing resources with affordable cost and high-level availability, reliability, scalability, and assured security. In this study, a unified cloud-based model for a cluster of Ethiopia universities (community cloud) is designed for ensuring the optimum utilization of computing resources amongst Ethiopia universities at affordable cost. This research used the converged version of the design science, exploratory research, and conducted a rigorous context analysis and pre-design assessment using technical observation, survey, and interviews through cloud-based survey tools. Cloud analyst is used for testing load balances, and CloudSim is employed for simulation and experimentation using three different algorithms namely Round Robin, Throttled, and Equally Spread Current Execution Load Balance to improve the load balance amongst salient servers. The contextualized community model is designed over the cloud-based designing tools Draw-oi & Vision-Soft. A model is developed and deployed over G site for functional demonstration. The research contribution is a computing model that can better equip the regional university's network towards judicious and dynamic access to computing resources.