Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs

• URL: http://arxiv.org/abs/2301.00597v1
• Date: Mon, 2 Jan 2023 11:03:50 GMT
• Title: Fairness Guaranteed and Auction-based x-haul and Cloud Resource Allocation in Multi-tenant O-RANs
• Authors: Sourav Mondal and Marco Ruffini
• Abstract summary: We propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized.
• Score: 3.4519649635864584
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The open-radio access network (O-RAN) embraces cloudification and network function virtualization for base-band function processing by dis-aggregated radio units (RUs), distributed units (DUs), and centralized units (CUs). These enable the cloud-RAN vision in full, where multiple mobile network operators (MNOs) can install their proprietary or open RUs, but lease on-demand computational resources for DU-CU functions from commonly available open-clouds via open x-haul interfaces. In this paper, we propose and compare the performances of min-max fairness and Vickrey-Clarke-Groves (VCG) auction-based x-haul and DU-CU resource allocation mechanisms to create a multi-tenant O-RAN ecosystem that is sustainable for small, medium, and large MNOs. The min-max fair approach minimizes the maximum OPEX of RUs through cost-sharing proportional to their demands, whereas the VCG auction-based approach minimizes the total OPEX for all resources utilized while extracting truthful demands from RUs. We consider time-wavelength division multiplexed (TWDM) passive optical network (PON)-based x-haul interfaces where PON virtualization technique is used to flexibly provide optical connections among RUs and edge-clouds at macro-cell RU locations as well as open-clouds at the central office locations. Moreover, we design efficient heuristics that yield significantly better economic efficiency and network resource utilization than conventional greedy resource allocation algorithms and reinforcement learning-based algorithms.

Related papers

• Scalable spectral representations for network multiagent control [53.631272539560435]
  A popular model for multi-agent control, Network Markov Decision Processes (MDPs) pose a significant challenge to efficient learning. We first derive scalable spectral local representations for network MDPs, which induces a network linear subspace for the local $Q$-function of each agent. We design a scalable algorithmic framework for continuous state-action network MDPs, and provide end-to-end guarantees for the convergence of our algorithm.
  arXiv  Detail & Related papers  (2024-10-22T17:45:45Z)
• AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN [1.906179410714637]
  AdapShare is an ORAN-compatible solution leveraging Reinforcement Learning for intent-based spectrum management. By employing RL agents, AdapShare intelligently learns network demand patterns and uses them to allocate resources. AdapShare outperforms a quasi-static resource allocation scheme based on long-term network demand statistics.
  arXiv  Detail & Related papers  (2024-08-29T18:10:36Z)
• Joint Admission Control and Resource Allocation of Virtual Network Embedding via Hierarchical Deep Reinforcement Learning [69.00997996453842]
  We propose a deep Reinforcement Learning approach to learn a joint Admission Control and Resource Allocation policy for virtual network embedding. We show that HRL-ACRA outperforms state-of-the-art baselines in terms of both the acceptance ratio and long-term average revenue.
  arXiv  Detail & Related papers  (2024-06-25T07:42:30Z)
• A Bayesian Framework of Deep Reinforcement Learning for Joint O-RAN/MEC Orchestration [12.914011030970814]
  Multi-access Edge Computing (MEC) can be implemented together with Open Radio Access Network (O-RAN) over commodity platforms to offer low-cost deployment. In this paper, a joint O-RAN/MEC orchestration using a Bayesian deep reinforcement learning (RL)-based framework is proposed.
  arXiv  Detail & Related papers  (2023-12-26T18:04:49Z)
• Intelligent Load Balancing and Resource Allocation in O-RAN: A Multi-Agent Multi-Armed Bandit Approach [4.834203844100679]
  We propose a multi-agent multi-armed bandit for load balancing and resource allocation (mmLBRA) scheme. We also present the mmLBRA-LB and mmLBRA-RA sub-schemes that can operate independently in non-realtime RAN intelligent controller (Non-RT RIC) and near-RT RIC, respectively.
  arXiv  Detail & Related papers  (2023-03-25T04:42:30Z)
• Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach [61.74489383629319]
  Massive random access of devices in the emerging Open Radio Access Network (O-RAN) brings great challenge to the access control and management. reinforcement-learning (RL)-assisted scheme of closed-loop access control is proposed to preserve sparsity of access requests. Deep-RL-assisted SAUD is proposed to resolve highly complex environments with continuous and high-dimensional state and action spaces.
  arXiv  Detail & Related papers  (2023-03-05T12:25:49Z)
• Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul [4.254099382808598]
  Network slicing envisions the 5th generation (5G) mobile network resource allocation to be based on different requirements for different services, such as Ultra-Reliable Low Communication (URLLC) and Enhanced Mobile Broadband (eMBB) This research demonstrates an RL compatible simplified edge network simulator with three components, user equipment(UE), Edge O-Cloud, and Regional O-Cloud. This simulator is later used to discover how to improve throughput for targeted network slice(s) by dynamically allocating unused bandwidth from other slices.
  arXiv  Detail & Related papers  (2022-11-14T15:48:13Z)
• 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)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• Self-play Learning Strategies for Resource Assignment in Open-RAN Networks [3.763743638851161]
  Open Radio Access Network (ORAN) is being developed with an aim to democratise access and lower the cost of future mobile data networks. In ORAN, network functionality is dis-aggregated into remote units (RUs), distributed units (DUs) and central units (CUs)
  arXiv  Detail & Related papers  (2021-03-03T19:31:29Z)

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.