An Analytical Model of IaaS Architecture for Determining Resource Utilization

Area of Science:

• Computer Science
• Cloud Computing
• IT Infrastructure

Background:

• Cloud computing, particularly Infrastructure as a Service (IaaS), is crucial for modern IT.
• Optimizing resource utilization (processor, RAM, disk, bandwidth) is a key challenge in IaaS design.
• Virtual Machines (VMs) run on physical machines (PMs), abstracting hardware needs for users.

Purpose of the Study:

• To develop an analytical model for determining average resource utilization in IaaS cloud systems.
• To validate the model's effectiveness in predicting resource usage.
• To assess the model's applicability in designing efficient cloud infrastructures.

Main Methods:

• Development of an analytical model, termed the ARU method.
• Evaluation through comparison with digital simulations of IaaS cloud system operations.
• Testing the model's performance across various system configurations and request patterns.

Main Results:

• The ARU method accurately determines average resource utilization levels.
• Model effectiveness is consistent regardless of request structure, resource variability, or the number of physical machines.
• The analytical model provides reliable predictions for cloud system resource management.

Conclusions:

• The ARU method is a validated tool for assessing resource utilization in IaaS.
• The model's proven effectiveness supports its application in the design of cloud systems.
• This contributes to more efficient and optimized cloud infrastructure development.