Journal article

ADRL: A Hybrid Anomaly-Aware Deep Reinforcement Learning-Based Resource Scaling in Clouds

Sara Kardani-Moghaddam, Rajkumar Buyya, Kotagiri Ramamohanarao

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS | IEEE COMPUTER SOC | Published : 2021

Abstract

The virtualization concept and elasticity feature of cloud computing enable users to request resources on-demand and in the pay-as-you-go model. However, the high flexibility of the model makes the on-time resource scaling problem more complex. A variety of techniques such as threshold-based rules, time series analysis, or control theory are utilized to increase the efficiency of dynamic scaling of resources. However, the inherent dynamicity of cloud-hosted applications requires autonomic and adaptable systems that learn from the environment in real-time. Reinforcement Learning (RL) is a paradigm that requires some agents to monitor the surroundings and regularly perform an action based on t..

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