Optimization of cloud computing resource allocation using reinforcement learning
Table Of Contents
- 1.Introduction - Background and significance of cloud computing resource allocation - Objectives and scope of the project
- 2.Literature Review - Overview of cloud resource allocation methods - Review of reinforcement learning in resource management
- 3.Reinforcement Learning for Resource Allocation - Formulation of the resource allocation problem as a reinforcement learning task - Selection of state, action, and reward functions
- 4.Algorithm Design and Implementation - Development of a reinforcement learning-based resource allocation algorithm - Integration with cloud management systems
- 5.Performance Evaluation - Metrics for assessing resource utilization and cost efficiency - Experimental setup and workload scenarios
Thesis Abstract
This project aims to optimize cloud computing resource allocation using reinforcement learning techniques. The project will focus on developing a resource allocation algorithm that can adaptively allocate computing resources to different tasks based on dynamic workload patterns and performance requirements. The use of reinforcement learning will enable the algorithm to learn and optimize resource allocation policies over time, leading to improved resource utilization and reduced operational costs in cloud environments.
Thesis Overview
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