New Bi-Level Algorithm Tackles Online Provisioning and Scheduling Challenges

Global: New Bi-Level Algorithm Tackles Online Provisioning and Scheduling Challenges

A novel algorithm has been introduced to simultaneously manage slow-time-scale provisioning decisions and fast-time-scale queue-dependent scheduling in network resource allocation systems. The approach integrates an upper-level online convex optimization (OCO) problem with a lower-level constrained Markov decision process (CMDP), addressing the limitations of traditional OCO and CMDP frameworks.

Problem Context

Conventional OCO models assume stateless decisions, which prevents them from capturing dynamic network behaviors such as queue evolution. Conversely, standard CMDP methods typically operate under a fixed constraint threshold, whereas real‑world provisioning systems require thresholds that adapt to online budget allocations. This mismatch creates a gap in effectively coordinating provisioning and scheduling actions.

Methodological Advances

The proposed bi‑level formulation introduces cross‑level constraints that directly couple budget decisions with scheduling policies, as well as switching costs that reflect the expense of reconfiguring budgets. To solve the resulting learning problem, the researchers designed a dual feedback mechanism that supplies the budget multiplier as sensitivity information for the upper‑level OCO update. On the lower level, they employ an extended occupancy‑measure linear program to enable budget‑adaptive safe exploration within the CMDP.

Theoretical Guarantees

Analysis of the algorithm demonstrates near‑optimal regret performance, indicating that the cumulative loss grows only sublinearly with time. Additionally, the method satisfies the cross‑level constraints with high probability, providing strong assurances that budget and scheduling requirements remain aligned throughout operation.

The findings suggest that the algorithm can improve efficiency and reliability in networks where resource provisioning must react to fluctuating demand while maintaining strict performance guarantees.

Future research may explore extensions to multi‑agent environments, integration with real‑time traffic data, and empirical validation on large‑scale network testbeds.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.