Resource Allocation Model for Cloud Compute
This project was conducted in an entertainment company, analyzing vCPU data for the internal AWS private cloud. The paper contains an overview of the company, the department, the student's learning, and the MBA courses that gave her the knowledge to complete the project. The given objectives are defined, and the scope is explained as one region containing 1,031 servers focusing on vCPU data. The raw data is provided and an efficiency model (created in Excel) is given to ensure the cloud runs at 70%, allowing for artificial head space, while limiting the over-allocation of valuable resources. The steps for using the model, the jargon used, and the arbitrary decision rules for shifting resources are explicitly defined. The student recommends the Resource Allocation Model be conducted in three phases, going from manual to fully automated over a year's time.