An optimized resource allocation in cloud using prediction enabled reinforcement learning

Due to its many applications, cloud computing has gained popularity in recent years. It is simple and fast to access shared resources at any time from any location. Cloud-based package facilities need adaptive resource allocation (RA) to provide Quality-of-Service (QoS) while lowering resource prices owing to workloads and service demands that change over time. As a result of the constantly shifting system states, resource allocation presents enormous challenges. The old methods often require specialist knowledge, which may result in poor adaptability. Additionally, it aims for environments with set workloads; hence, it cannot be used successfully in real-world contexts with fluctuating workloads. This research therefore proposes a Prediction-enabled feedback system to solve these significant problems with the reinforcement learning-based RA (PCRA) framework. Firstly, this research creates a more accurate Q-value prediction to forecast management value processes at various scheme conditions, using Q-values as the basis. For accurate Q-value prediction, the model makes use of several prediction learners using the Q-learning method. Also, an improved optimization-based algorithm is utilized to discover impartial resource allocations called the Feature Selection Whale Optimization Algorithm (FSWOA). Simulations based on practical scenarios using CloudStack and RUBiS benchmarks demonstrate the effectiveness of PCRA for real-time RA. Simulations demonstrate that the PCRA framework achieves a 94.7% Q-value prediction accuracy and reduces SLA violations and resource cost by 17.4% compared to traditional round-robin scheduling.