Cloud Usage Optimization through Data Analysis and Visualization: A Case Study for Company X in 2016

Abstract

Cloud computing has revolutionized the IT landscape, offering businesses scalability and cost-efficiency. To enhance cloud usage efficiency and optimize costs, companies need to analyze their cloud usage data thoroughly. This research paper presents a comprehensive analysis of a company’s cloud usage data for the year 2016, focusing on CPU core, CPU capacity, Memory usage, Disk read, Disk Write, and transmitted throughput. The study utilizes a dataset containing 186 days of cloud usage, encompassing essential metrics and date-wise details. The primary objective of this research is to provide insights into cloud resource utilization patterns, identify high-usage instances, and detect instances with zero memory usage and disk throughput. The analysis leverages Google Looker Studio to create interactive charts and heat maps, facilitating a deeper understanding of the data and enabling data-driven decision-making. The paper discusses the data collection process, data cleaning, and the extraction of relevant features. The results showcase date-wise trends of memory usage and processor utilization, arranged in ascending order to highlight the most and least utilized instances. Moreover, the study demonstrates instances with zero memory usage and disk throughput, shedding light on opportunities for service optimization and resource cost reduction. The findings demonstrate the effectiveness of data analysis and visualization in optimizing cloud services for Company X. By identifying heavily utilized instances and instances with negligible usage, the company can make ijdccinformed decisions to optimize resource allocation and enhance overall system efficiency.

Keywords: Cloud computing, Google studio, Resource allocation.

View PDF