Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding

• URL: http://arxiv.org/abs/2202.02139v1
• Date: Thu, 3 Feb 2022 07:26:10 GMT
• Title: Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding
• Authors: Chao Wang, Tao Dong, Youxiang Duan, Qifeng Sun, and Peiying Zhang
• Abstract summary: This paper implements a multi-objective optimization VNE algorithm for wireless network resource allocation. According to the proposed objective formula, we consider the optimization mapping cost, network delay and VNR acceptance rate.
• Score: 6.019028366261091
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid development of virtual network architecture makes it possible for wireless network to be widely used. With the popularity of artificial intelligence (AI) industry in daily life, efficient resource allocation of wireless network has become a problem. Especially when network users request wireless network resources from different management domains, they still face many practical problems. From the perspective of virtual network embedding (VNE), this paper designs and implements a multi-objective optimization VNE algorithm for wireless network resource allocation. Resource allocation in virtual network is essentially a problem of allocating underlying resources for virtual network requests (VNRs). According to the proposed objective formula, we consider the optimization mapping cost, network delay and VNR acceptance rate. VNE is completed by node mapping and link mapping. In the experiment and simulation stage, it is compared with other VNE algorithms, the cross domain VNE algorithm proposed in this paper is optimal in the above three indicators. This shows the effectiveness of the algorithm in wireless network resource allocation.

Related papers

• GNN-Based Joint Channel and Power Allocation in Heterogeneous Wireless Networks [9.031738020845586]
  This article proposes a GNN-based algorithm to address the joint resource allocation problem in heterogeneous wireless networks. Our proposed algorithm achieves satisfactory performance but with higher computational efficiency compared to traditional optimisation algorithms.
  arXiv  Detail & Related papers  (2024-07-28T04:51:00Z)
• 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)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• State-Augmented Learnable Algorithms for Resource Management in Wireless Networks [124.89036526192268]
  We propose a state-augmented algorithm for solving resource management problems in wireless networks. We show that the proposed algorithm leads to feasible and near-optimal RRM decisions.
  arXiv  Detail & Related papers  (2022-07-05T18:02:54Z)
• Network Resource Allocation Strategy Based on Deep Reinforcement Learning [4.751282319342761]
  This paper proposes a two-stage VNE algorithm based on deep reinforcement learning (DRL) (TS-DRL-VNE) For the problem that the existing VNE algorithm based on ML often ignores the importance of substrate network representation and training mode, a DRL VNE algorithm based on full attribute matrix (FAM-DRL-VNE) is proposed. Experimental results show that the above algorithm is superior to other algorithms.
  arXiv  Detail & Related papers  (2022-02-03T06:53:00Z)
• IoV Scenario: Implementation of a Bandwidth Aware Algorithm in Wireless Network Communication Mode [49.734868032441625]
  This paper proposes a bandwidth aware multi domain virtual network embedding algorithm (BA-VNE) The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode. In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm.
  arXiv  Detail & Related papers  (2022-02-03T03:34:06Z)
• Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning [15.394489762270162]
  This paper proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation.
  arXiv  Detail & Related papers  (2022-02-03T02:37:45Z)
• Learning Autonomy in Management of Wireless Random Networks [102.02142856863563]
  This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. We develop a flexible deep neural network formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology.
  arXiv  Detail & Related papers  (2021-06-15T09:03:28Z)
• Cognitive Radio Network Throughput Maximization with Deep Reinforcement Learning [58.44609538048923]
  Radio Frequency powered Cognitive Radio Networks (RF-CRN) are likely to be the eyes and ears of upcoming modern networks such as Internet of Things (IoT) To be considered autonomous, the RF-powered network entities need to make decisions locally to maximize the network throughput under the uncertainty of any network environment. In this paper, deep reinforcement learning is proposed to overcome the shortcomings and allow a wireless gateway to derive an optimal policy to maximize network throughput.
  arXiv  Detail & Related papers  (2020-07-07T01:49:07Z)

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.