Abstract
Latency is inherent in almost all real-world net-worked applications. In this paper, we propose a distributed resource allocation strategy over multi-agent networks with delayed communications. The state of each agent (or node) represents its share of assigned resources out of a fixed amount (equal to the overall demand). Every node locally updates its state towards optimizing a global allocation cost function via received information of its neighbouring nodes even when the data exchange over the network is heterogeneously delayed at different links. The update is based on the alternating direction method of multipliers (ADMM) formulation subject to both sum-preserving coupling-constraint and local box-constraints. The solution is derivative-free and holds for general (not necessarily differentiable) convex cost models. We use the notion of augmented consensus over undirected networks to model delayed information-exchange for convergence analysis. We simulate our delay-tolerant algorithm for optimal energy reservation-production scheduling.
| Original language | English |
|---|---|
| Title of host publication | 2022 IEEE 61st Conference on Decision and Control (CDC) |
| Publisher | IEEE |
| Pages | 308-315 |
| Number of pages | 8 |
| ISBN (Electronic) | 978-1-6654-6761-2 |
| DOIs | |
| Publication status | Published - 10 Jan 2023 |
| MoE publication type | A4 Article in a conference publication |
| Event | IEEE Conference on Decision and Control - Cancun, Mexico Duration: 6 Dec 2022 → 9 Dec 2022 Conference number: 61 |
Conference
| Conference | IEEE Conference on Decision and Control |
|---|---|
| Abbreviated title | CDC |
| Country/Territory | Mexico |
| City | Cancun |
| Period | 06/12/2022 → 09/12/2022 |
Keywords
- Analytical models
- Costs
- Cost function
- Scheduling
- Convex functions
- Resource management
- Convergence
- Heterogeneous delays
- distributed optimization
- ADMM
- resource allocation