Improving Quality-of-Service in Cloud/Fog Computing through Efficient Resource Allocation

Area of Science:

• Computer Science
• Cloud Computing
• Resource Management

Background:

• Enterprise applications are increasingly migrating to cloud environments.
• Quality-of-Service (QoS) management is a critical challenge in cloud computing, involving resource allocation.
• Optimizing resource allocation is vital, especially in cloud infrastructures using lightweight devices.

Purpose of the Study:

• To formulate and present task allocation and virtual machine placement problems in a unified cloud/fog computing environment.
• To propose novel algorithmic solutions for these complex resource management challenges.

Main Methods:

• Developed a specific algorithmic solution for task allocation.
• Implemented a Genetic Algorithm Based Virtual Machine Placement (GABP) approach.
• Evaluated solutions within a single cloud/fog computing environment.

Main Results:

• The proposed task allocation and virtual machine placement solutions demonstrated improved Quality-of-Service.
• A significant reduction in allocation cost was observed in the cloud/fog computing environment.
• Experimental results validate the effectiveness of the developed algorithms.

Conclusions:

• The study successfully addressed key challenges in cloud/fog resource management.
• The proposed methods offer a viable approach to enhance QoS and optimize costs.
• This work contributes to efficient resource allocation strategies in distributed computing environments.