The primary aim of the study was to develop a regression model for forecasting monthly cloud storage consumption. Second, to ascertain if the month is a reliable predictor of cloud storage capacity consumed. The model was developed using Minitab18 statistical software. The dependent variable was cloud storage capacity consumed, while the independent variable was the month of cloud storage consumption. The model was validated by checking the assumptions of regression to establish its suitability in making future predictions. Twelve-month data sets was analyzed to make future prediction for each passing month. The model made predictions with near accuracy from the actual cloud storage data consumed in each month. The model determines the intervals of monthly storage consumption. The study concluded that the month is a globally significant linear predictor of cloud storage capacity consumed over a period.
Table of Contents
1.0. Introduction
1.1. Background of the study
1.2. Problem statement
1.3. Objectives of the study:
1.4. Research hypothesis:
2.0. Related works/ Review of related literature
3.0. Methods
3.1. Method and source of data collection
3.2. Sample size
3.3. Method of data analysis, procedure and instrument used for analysis
3.4. The regression model
3.5. Dependent and Independent Variable
3.6. Validation of model
4.0. Analysis
4.1. Regression Equation
4.2. Discussion
4.3. Key findings
5.0. Conclusion
6.0. References
Research Objective and Core Themes
The primary objective of this study is to develop a regression-based forecasting model to predict monthly cloud storage consumption, thereby assisting IT managers in effective budgeting and resource planning. The research investigates whether the month of service consumption serves as a reliable linear predictor for storage capacity requirements.
- Development of a linear regression model for cloud storage forecasting.
- Statistical validation of regression assumptions (linearity, normality, independence, equal variance).
- Analysis of historical cloud consumption data over a 12-month period.
- Evaluation of the month as a significant predictor for storage usage.
- Guidance for IT infrastructure management and decision-making in cloud environments.
Excerpt from the Book
Problem statement
Earlier (Kondo, 2009) asserted that the cost-benefits of cloud computing compared to traditional IT infrastructure and what constitutes the cost of cloud computing ranging from computational size, time, and storage is not perfectly clear to some organization and their IT managers. (Linthicum, 2014), affirmed that dynamic workloads and changing prices of cloud computing, most enterprises seem to be getting worse at understanding their storage actual storage cost.
(McKendrick, 2016), stated that in the coming years, one of the major forces driving cloud storage services in the organizations will be Internet of Things (IoT) and associated big data. The report says the ugly implications of this evolution is that organizations do not have enough storage capacity to handle the terabytes of data that will be generated. Therefore, nearly all workload will be cloud-borne and organization will contract storage services. Recently (RightScale, 2017), reaffirmed this problem through its conducted annual state of the cloud survey, which shows that understanding cloud storage service consumption and managing cloud costs has become a top challenge to companies. Consequently, the researchers developed a regression-forecasting model, for prediction of the future cloud storage consumption based on historical data. Without this forecasting model, organizations will find it difficult to determine the ranges of their cloud storage consumption and budget accordingly.
Chapter Summary
1.0. Introduction: This chapter introduces the growing reliance on cloud computing and the critical need for accurate storage consumption forecasting for effective IT budgeting and planning.
2.0. Related works/ Review of related literature: An overview of existing predictive models in cloud computing is provided, highlighting the limitations of current approaches in determining specific intervals for monthly cloud storage consumption.
3.0. Methods: This section details the quantitative approach used to develop the regression model, including data collection from a private organization and the validation procedures for the statistical assumptions.
4.0. Analysis: This chapter presents the regression equation and statistical results, validating that the model performs reliably and provides significant insights into storage trends.
5.0. Conclusion: The study summarizes that the month of consumption is a globally significant linear predictor of storage capacity, confirming the model's utility for future forecasting.
Keywords
Cloud computing, regression model, forecasting, cloud storage, storage capacity, IT budgeting, data center, linear predictor, resource planning, Internet of Things, statistical modeling, information system auditing, performance analysis.
Frequently Asked Questions
What is the primary focus of this research?
The research focuses on creating a regression-based model to forecast monthly cloud storage consumption based on historical data to help organizations manage storage costs.
What are the central thematic areas?
The core themes include cloud storage consumption, statistical forecasting techniques, IT infrastructure management, and big data implications for storage needs.
What is the primary research goal?
The main goal is to develop an accurate forecasting model and determine if the month of usage is a reliable predictor of cloud storage capacity.
Which scientific method is applied?
The study uses a quantitative approach utilizing linear regression analysis via Minitab18 statistical software.
What is covered in the main section of the paper?
The main body covers the theoretical background, the development of the regression equation, and the validation of the model against four key statistical assumptions.
Which keywords define this study?
The work is characterized by terms such as cloud computing, regression model, forecasting, storage capacity, and IT budgeting.
Why is this model necessary for modern organizations?
As organizations face increasing data generation due to the Internet of Things, they struggle with budgeting for cloud costs; this model provides a way to estimate storage intervals.
Did the study confirm the researchers' hypothesis?
Yes, the results showed a p-value of 0.000, allowing the researchers to reject the null hypothesis and confirm the significance of the month as a predictor.
Can this model justify causal relationships?
The authors clarify that while the model shows a strong linear relationship, it does not provide sufficient evidence to claim that the passage of time strictly causes increased storage usage.
- Quote paper
- lecturer Abdallah Ziraba (Author), Mbata David (Author), 2017, Forecasting Cloud Storage Consumption Using Regression Model, Munich, GRIN Verlag, https://www.grin.com/document/413003
