A QoS-aware task scheduling strategy for heterogeneous cloud environments via hybrid parallel PSO and deep reinforcement learning

Key Points

• The central aim is to develop a quality of service-aware task scheduling strategy for cloud environments.
• Utilized a hybrid approach combining parallel particle swarm optimization (PSO) and deep reinforcement learning.
• Focused on task scheduling in heterogeneous cloud computing environments.
• Analyzed performance metrics to evaluate scheduling effectiveness.
• The proposed strategy improved task completion times in diverse cloud environments.
• Achieved higher quality of service compared to existing scheduling methods.