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.