Multi-Agent Context-Aware Dynamic-Scheduling for Large-scale Processing Networks

Optimally scheduling jobs in processing networks to meet multiple objectives for economic considerations and operational efficiencies has been a hot topic. However, most data-driven methods are rarely applied. One of the critical reasons is that the scheduling policy generated by these methods tends to bias toward the specific environment. In order to better deal with the discrepancy, this work in progress paper presents a context-aware dynamic scheduling method (CADS) that can adaptively select a specific policy based on the on-demand context. The CADS has two components: 1) The evaluation module that evaluates the performances of the policies that are learned from each operational context; 2) The decision-making module that maintains the knowledge of each policy's performance under each context and constructs the weighted best-fit policy based on the identified context. The promising preliminary result using numerical simulation that demonstrates the effectiveness of CADS is presented. CADS outperforms traditional scheduling methods in various kinds of processing network environments.