ANALISA KINERJA INFRASTRUCTURE AS A SERVICE PADA CLOUD COMPUTING DENGAN PENDEKATAN REINFORCEMENT LEARNING

AZZAHRA, MUTIA YASMIN and Heryanto, Ahmad and Hermansyah, Adi (2024) ANALISA KINERJA INFRASTRUCTURE AS A SERVICE PADA CLOUD COMPUTING DENGAN PENDEKATAN REINFORCEMENT LEARNING. Undergraduate thesis, Sriwijaya University.

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Abstract

Cloud Computing, particularly Infrastructure as a Service (IaaS), offers high flexibility, cost efficiency, and scalability in managing computing resources. However, fluctuating demands and dynamic workloads require adaptive management strategies to maintain service quality. This study explores the use of Reinforcement Learning (RL) algorithms, specifically Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), to optimize IaaS performance. Using simulations based on datasets from IEEE DataPort, RL agents were designed to dynamically learn resource usage patterns. The results show that DQN achieved 89% accuracy in predicting system status, while PPO achieved 82%. Results indicate DQN performs slightly better, with a batch size of 32 compared to PPO's 64. Both models utilize identical network architectures [64, 64] and similar learning rates (DQN: 0.00025, PPO: 0.0003). Additionally, both algorithms reduced resource wastage by improving the efficiency of CPU, memory, bandwidth, and response time usage. However, challenges remain in addressing imbalances in negative class detection. This research contributes to the optimization of IaaS management using RL, with potential for further development through the integration of other algorithms and application to more complex cloud computing scenarios.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Cloud Computing, Infrastructure as a Service (IaaS), Reinforcement Learning, Deep Q-Network (DQN), Proximal Policy Optimization (PPO)
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Mutia Yasmin
Date Deposited:	09 Jan 2025 02:52
Last Modified:	09 Jan 2025 03:21
URI:	http://repository.unsri.ac.id/id/eprint/163153

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