Conference paper Open Access
Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicing
Farhad Rezazadeh;
Hatim Chergui;
Loizos Christofi;
Christos Verikoukis
To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO) system. This paper proposes a novel knowledge plane (KP)-based MANO framework that accommodates and exploits recent NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic innovation and artificial intelligence (AI) in KB5G. We invoke a continuous model-free deep reinforcement learning (DRL) method to minimize energy consumption and virtual network function (VNF) instantiation cost. We present a novel Actor-Critic-based NS approach to stabilize learning called, twin-delayed double-Q soft Actor-Critic (TDSAC) method. The TDSAC enables central unit (CU) to learn continuously to accumulate the knowledge learned in the past to minimize future NS costs. Finally, we present numerical results to showcase the gain of the adopted approach and verify the performance in terms of energy consumption, CPU utilization, and time efficiency.
| Name | Size | |
|---|---|---|
|
Rezazadeh2.pdf
md5:a9897bfea5db5c8964c22308312a4d3a |
1.1 MB | Download |
| All versions | This version | |
|---|---|---|
| Views | 22 | 22 |
| Downloads | 22 | 22 |
| Data volume | 24.2 MB | 24.2 MB |
| Unique views | 22 | 22 |
| Unique downloads | 22 | 22 |
- Publication date:
- June 23, 2021
- DOI:
-
- Grants:
-
European Commission:
-
MonB5G - Distributed management of Network Slices in beyond 5G (871780)
- 5G-SOLUTIONS - 5G Solutions for European Citizens (856691)
-
MonB5G - Distributed management of Network Slices in beyond 5G (871780)
- Meeting:
- IEEE International Conference on Communications (ICC2021), Canada, 14-23 June 2021
- License (for files):
- Creative Commons Attribution 4.0 International