POMDP-Active Inference Model for Hybrid Critical Multi-Core Heterogeneous Scheduling

In heterogeneous multicore systems, efficiently allocating tasks to cores is a key challenge due to the complexity of hardware architectures and the dynamic nature of workloads. System states and task behaviors are inherently partially observable due to limited accessible information about interactions among tasks and shared resources, which complicates the prediction and management of execution times and resource contention. Moreover, uncertainties, such as unpredictable execution times and variable workloads, combined with the challenge of prioritizing high-criticality tasks while maintaining overall system performance and ensuring fairness in the execution of low-criticality tasks, necessitate the use of sophisticated allocation strategies. We propose a novel approach which embraces the uncertainty of modern systems. partially observable Markov decision processes (POMDP) and Active Inference are used to minimize uncertainty and optimize allocation decisions. By representing uncertainties within the POMDP framework, our method enables probabilistic predictions of task behaviors and system states. Active Inference refines these predictions and adapts decisions dynamically, handling workload variability and system changes, such as adding new tasks. Our methodology emphasizes application-layer scheduling as opposed to kernel-level modifications, thereby giving our approach both platform awareness and deployment flexibility. Moreover, the capacity for offline training mitigates the adverse effects of online updates on the accuracy of target platform comprehension. Experimental results demonstrate superior performance over baseline approaches, enhancing system efficiency and resource utilization by incorporating task criticality awareness, even in the presence of uncertainties and partial observability.