Energy-Saving Scheduling Strategy for Real-Time Applications on big.LITTLE Architectures

In recent years, with the increasing demand for computing power from various intelligent applications on mobile devices, heterogeneous multi-core architectures have received more attention. The big.LITTLE architecture, as a new type of energy-saving architecture, consists of high-performance cores and low energy consumption cores, which can better meet the real-time task requirements of various mobile devices. One of the challenges faced by these scheduling algorithms is how to fully utilize the advantages of big.LITTLE architectures to achieve a balance between system energy consumption and performance. To address this issue, we designed an initial task allocation strategy based on the characteristics of big.LITTLE architecture, introduced reinforcement learning algorithms for decision-making, and assigned different types of tasks to their appropriate processors. In order to balance task load and reduce total scheduling time, we proposed a dynamic migration algorithm, which dynamically adjusts task load by migrating them in the case of imbalanced utilization. Experimental results demonstrate that the proposed algorithm effectively balances energy consumption and performance, improves resource utilization, and reduces both scheduling time and energy consumption.