Actor-Critic Optimization Based Adaptive Task Scheduling Using Deep Double Dueling Q Network for Heterogeneous Cloud Environments

In the rapidly evolving domain of cloud computing, the efficient scheduling of dependent tasks is critical for optimizing resource utilization and achieving key objectives such as minimizing latency and maximizing throughput. This paper presents the Actor-Critic Optimization based Adaptive Task Scheduling using Deep Double Dueling Q Network (ACTS-D3QN) in heterogeneous cloud environments enhances cloud task scheduling by incorporating advanced machine learning and optimization techniques. The D3QN framework is structured as an actor-critic model, where the actor component handles task scheduling and resource allocation, and the critic component refines these schedules. The actor component of D3QN integrates a Proportional Integral Derivative (PID) Controller for adaptive scheduling, ensuring real-time optimization of resource allocation while adhering to strict deadlines and dynamically managing workloads. Additionally, the system introduces a Dynamic Data Placement Algorithm with Predictive Caching (DDPPC), aimed at improving data locality and minimizing data transfer times. To balance operational costs with performance, a Modified NSGA-III algorithm is employed in the critic component of D3QN for multi-objective optimization. Furthermore, constraint programming is leveraged for efficient task-to-resource matching. Experimental results demonstrate that the ACTS-D3QN method achieves significant improvements, including a 22.14% reduction in makespan and a 20.0% increase in throughput, thereby validating its effectiveness in dynamic cloud environments.