Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning

• URL: http://arxiv.org/abs/2306.09290v1
• Date: Thu, 15 Jun 2023 17:16:34 GMT
• Title: Generalizable Resource Scaling of 5G Slices using Constrained Reinforcement Learning
• Authors: Muhammad Sulaiman, Mahdieh Ahmadi, Mohammad A. Salahuddin, Raouf Boutaba, Aladdin Saleh
• Abstract summary: Network slicing is a key enabler for 5G to support various applications. It is imperative that the 5G infrastructure provider (InP) allocates the right amount of resources depending on the slice's traffic.
• Score: 2.0024258465343268
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Network slicing is a key enabler for 5G to support various applications. Slices requested by service providers (SPs) have heterogeneous quality of service (QoS) requirements, such as latency, throughput, and jitter. It is imperative that the 5G infrastructure provider (InP) allocates the right amount of resources depending on the slice's traffic, such that the specified QoS levels are maintained during the slice's lifetime while maximizing resource efficiency. However, there is a non-trivial relationship between the QoS and resource allocation. In this paper, this relationship is learned using a regression-based model. We also leverage a risk-constrained reinforcement learning agent that is trained offline using this model and domain randomization for dynamically scaling slice resources while maintaining the desired QoS level. Our novel approach reduces the effects of network modeling errors since it is model-free and does not require QoS metrics to be mathematically formulated in terms of traffic. In addition, it provides robustness against uncertain network conditions, generalizes to different real-world traffic patterns, and caters to various QoS metrics. The results show that the state-of-the-art approaches can lead to QoS degradation as high as 44.5% when tested on previously unseen traffic. On the other hand, our approach maintains the QoS degradation below a preset 10% threshold on such traffic, while minimizing the allocated resources. Additionally, we demonstrate that the proposed approach is robust against varying network conditions and inaccurate traffic predictions.

Related papers

• Graph Reinforcement Learning for QoS-Aware Load Balancing in Open Radio Access Networks [4.411880853217908]
  Next-generation wireless cellular networks are expected to provide unparalleled Quality-of-Service (QoS) for emerging wireless applications. Cell congestion involves balancing the load to ensure sufficient radio resources are available for each cell to serve its designated User Equipments (UEs) A novel-aware Load Balancing (LB) approach is developed to optimize the performance of GBR and Best Effort (BE) traffic in a multi-band Open Radio Access Network (O-RAN) under resource constraints.
  arXiv  Detail & Related papers  (2025-04-28T05:41:31Z)
• Learn to Optimize Resource Allocation under QoS Constraint of AR [6.073675653083644]
  We study the uplink and downlink power allocation for interactive augmented reality (AR) services, where live video is uploaded to the network edge and then the augmented video is downloaded. By modeling the AR transmission process as a tandem queuing system, we derive an upper bound for the probabilistic quality of service (QoS) requirement concerning end-to-end latency and reliability. We propose a deep neural network to learn the power allocation policy, leveraging the structure of optimal power allocation to enhance learning performance.
  arXiv  Detail & Related papers  (2025-01-27T16:42:48Z)
• An on-demand resource allocation algorithm for a quantum network hub and its performance analysis [0.9148747049384086]
  We study traffic models for a type of quantum network hub called an Entanglement Generation Switch (EGS), a device that allocates resources to enable entanglement generation between nodes in response to user-generated demand. We propose an on-demand resource allocation algorithm, where a demand is either blocked if no resources are available or else results in immediate resource allocation. Our work is the first analysis of traffic characteristics at an EGS system and provides a valuable analytic tool for devising performance driven resource allocation algorithms.
  arXiv  Detail & Related papers  (2024-05-28T11:25:32Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• Artificial Intelligence Empowered Multiple Access for Ultra Reliable and Low Latency THz Wireless Networks [76.89730672544216]
  Terahertz (THz) wireless networks are expected to catalyze the beyond fifth generation (B5G) era. To satisfy the ultra-reliability and low-latency demands of several B5G applications, novel mobility management approaches are required. This article presents a holistic MAC layer approach that enables intelligent user association and resource allocation, as well as flexible and adaptive mobility management.
  arXiv  Detail & Related papers  (2022-08-17T03:00:24Z)
• Semantic-aware Modular Capsule Routing for Visual Question Answering [55.03883681191765]
  We propose a Semantic-aware modUlar caPsulE framework, termed as SUPER, to better capture the instance-specific vision-semantic characteristics. We comparatively justify the effectiveness and generalization ability of our proposed SUPER scheme over five benchmark datasets.
  arXiv  Detail & Related papers  (2022-07-21T10:48:37Z)
• CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing [19.990451009223573]
  Network slicing is proposed as a promising solution for resource utilization in 5G and future networks. We formulate the problem as a Constrained Markov Decision Process (CMDP) without knowing models and hidden structures. We propose to solve the problem using CLARA, a Constrained reinforcement LeArning based Resource Allocation algorithm.
  arXiv  Detail & Related papers  (2021-11-16T11:54:09Z)
• An Information Theory-inspired Strategy for Automatic Network Pruning [88.51235160841377]
  Deep convolution neural networks are well known to be compressed on devices with resource constraints. Most existing network pruning methods require laborious human efforts and prohibitive computation resources. We propose an information theory-inspired strategy for automatic model compression.
  arXiv  Detail & Related papers  (2021-08-19T07:03:22Z)
• QoS-Aware Power Minimization of Distributed Many-Core Servers using Transfer Q-Learning [8.123268089072523]
  This paper presents a runtime-aware controller using horizontal scaling (node allocation) and vertical scaling (resource allocation within nodes) A horizontal scaling determines the number of active nodes based on workload demands and the required scalable according to a set of rules. Then, it is coupled with vertical scaling using transfer Q-learning, which tunes power/performance based on workload profile using dynamic voltage/frequency scaling (DVFS) When combined, these methods allow to reduce the exploration time and violations when compared to model-free Q-learning.
  arXiv  Detail & Related papers  (2021-02-02T06:47:58Z)
• Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning [40.5603189901241]
  We investigate a radio access network (RAN) slicing problem for Internet of vehicles (IoV) services with different quality of service (QoS) requirements. A dynamic RAN slicing framework is presented to dynamically allocate radio spectrum and computing resource. We show that the RAWS effectively reduces the system cost while satisfying requirements with a high probability, as compared with benchmarks.
  arXiv  Detail & Related papers  (2020-12-03T15:08:38Z)
• Reinforcement Learning for Dynamic Resource Optimization in 5G Radio Access Network Slicing [3.509171590450989]
  The paper presents a reinforcement learning solution to dynamic resource allocation for 5G radio network slicing. Results show that reinforcement learning provides major improvements in the 5G network utility relative to myopic, random, and first come first served solutions.
  arXiv  Detail & Related papers  (2020-09-14T17:10:17Z)
• Resource Allocation via Graph Neural Networks in Free Space Optical Fronthaul Networks [119.81868223344173]
  This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. We consider the graph neural network (GNN) for the policy parameterization to exploit the FSO network structure. The primal-dual learning algorithm is developed to train the GNN in a model-free manner, where the knowledge of system models is not required.
  arXiv  Detail & Related papers  (2020-06-26T14:20:48Z)
• Deep Learning for Radio Resource Allocation with Diverse Quality-of-Service Requirements in 5G [53.23237216769839]
  We develop a deep learning framework to approximate the optimal resource allocation policy for base stations. We find that a fully-connected neural network (NN) cannot fully guarantee the requirements due to the approximation errors and quantization errors of the numbers of subcarriers. Considering that the distribution of wireless channels and the types of services in the wireless networks are non-stationary, we apply deep transfer learning to update NNs in non-stationary wireless networks.
  arXiv  Detail & Related papers  (2020-03-29T04:48:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.