ARMA-Prediction-Based Online Adaptive Dynamic Resource Allocation in Wireless Virtualized Network

Tang, Lun; He, Xiaowen; Yang, Xixi; Wei, Yannan; Wang, Xiao; Chen, Qianbin

doi:10.1109/access.2019.2940435

Cited by 19 publications

(7 citation statements)

References 31 publications

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“…The increasing demand has accelerated the evolution of network architectures towards utilization. Internet service providers (ISPs) must meet the individual needs of their customers and therefore push for the usage [2][3][4]. Visualization technology is one of the research emphases and hotspots.…”

Section: Introductionmentioning

confidence: 99%

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Ren

2022

Wireless Communications and Mobile Computing

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In recent years, with the continuous development of IT industry, network virtualization technology has gradually developed into a hotspot of research and application in the field of communication because IT allows the construction of customized virtual network (VN) on shared physical infrastructure. In particular, the proposal of wireless network virtualization for the development of the enhanced Internet. Because these technologies require effective algorithms named virtual network mapping (VNE) to instantiate virtual networks on the SN, on the basis of satisfying the isolation between wireless network slices, a resource allocation mechanism based on bilateral auction is proposed. At present, many researchers have proposed that network virtualization technology is a feasible way to solve the rigid Internet architecture. In the field of existing research focuses on the cable network virtualization, mainly related to the data center network and backbone, then, the side of the wireless access network virtualization the key technology research is still in its infancy, in order to accelerate the process of wireless skill for innovation and meet the demand of the difference of the emerging business, wireless network virtualization becomes the research hotspot and focus of academia and industry. However, no one has applied the dynamic resource allocation algorithm to wireless network virtualization resource sharing field. Here, this paper analyzes and summarizes the existing virtual network resource allocation schemes at home and abroad. The wireless virtual network resource allocation model and cross-domain virtual network resource allocation model are established, and the network centrality theory in social network and complex network is introduced to analyze the topology properties of virtual network and physical network, and two efficient virtual network resource allocation methods are proposed.

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Section: Introductionmentioning

confidence: 99%

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Ren

2022

Wireless Communications and Mobile Computing

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“…However, the study's DQN model convergence time was long due to a small learning rate setting. Tang et al [24] proposed a multi-timescale online adaptive virtual resource allocation algorithm based on the autoregressive moving average (ARMA) prediction method, which allocates cache space and timefrequency resources in the network to meet the quality of service (QoS) requirements of each virtual network in terms of the overflow probability while minimising the network overhead, and solves the irrationality of the traditional methods due to the traffic uncertainty and the delay of information feedback. Zhang et al [25] proposed a deep learning-based predictive SFC dynamic deployment model with the objective of minimizing overall end-to-end delay by predicting future resource requirements for each VNF instance, and transform the dynamic deployment problem into an integer nonlinear programming (INLP) solution.…”

Section: Related Workmentioning

confidence: 99%

“…Tang et al. [24] proposed a multi‐timescale online adaptive virtual resource allocation algorithm based on the autoregressive moving average (ARMA) prediction method, which allocates cache space and time‐frequency resources in the network to meet the quality of service (QoS) requirements of each virtual network in terms of the overflow probability while minimising the network overhead, and solves the irrationality of the traditional methods due to the traffic uncertainty and the delay of information feedback. Zhang et al.…”

Section: Related Workmentioning

confidence: 99%

An improved resource allocation method for mapping service function chains based on A3C

Huang,

Tian,

Zhang

et al. 2024

IET Communications

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Network function virtualization (NFV) technology deploys network functions as software functions on a generalised hardware platform and provides customised network services in the form of service function chain (SFC), which improves the flexibility and scalability of network services and reduces network service costs. However, irrational resource allocation during service function chain mapping will cause problems such as low resource utilisation, long service request processing time and low mapping rate. To address the unreasonable problem of service mapping resource allocation, an improved service function chain mapping resource allocation method (SA3C) based on the Asynchronous advantageous action evaluation algorithm (A3C) is proposed. This study proposes an SFC mapping model and a mathematical model for joint allocation, which modeled the minimization of processing time as a Markov process. The main network was trained and multiple sub‐networks were generated in parallel using the ternary and deep reinforcement learning algorithm A3C, with the goal of identifying the optimal resource allocation strategy. The experimental simulation results show that compared with the Actor‐Critic (AC) and Policy Gradient (PG) methods, SA3C algorithm can improve the resource utilisation by 9.85%, reduce the total processing time by 10.72%, and improve the mapping rate by 6.72%, by reasonably allocating node computational resources and link bandwidth communication resources.

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“…In particular, the use of Auto-Regressive Moving Average (ARMA) models has been explored in [34] as a potential application-agnostic prediction method to perform resource allocation: as the orchestrator knows the state of the packet buffer for each slice, it can perform the moving average and allocate resources accordingly. However, ARMA models require a certain number of past samples, and this approach cannot discriminate between different applications: consequently, the initial performance will be lower when compared to an application-aware model that takes knowledge of the traffic source into account.…”

Section: Prediction-based Slicingmentioning

confidence: 99%

Temporal Characterization of VR Traffic for Network Slicing Requirement Definition

Chiariotti¹,

Drago²,

Testolina³

et al. 2022

Preprint

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Over the past few years, the concept of VR has attracted increasing interest thanks to its extensive industrial and commercial applications. Currently, the 3D models of the virtual scenes are generally stored in the VR visor itself, which operates as a standalone device. However, applications that entail multi-party interactions will likely require the scene to be processed by an external server and then streamed to the visors. However, the stringent Quality of Service (QoS) constraints imposed by VR's interactive nature require Network Slicing (NS) solutions, for which profiling the traffic generated by the VR application is crucial. To this end, we collected more than 4 hours of traces in a real setup and analyzed their temporal correlation. More specifically, we focused on the CBR encoding mode, which should generate more predictable traffic streams. From the collected data, we then distilled two prediction models for future frame size, which can be instrumental in the design of dynamic resource allocation algorithms. Our results show that even the state-of-the-art H.264 CBR mode can have significant fluctuations, which can impact the NS optimization. We then exploited the proposed models to dynamically determine the Service Level Agreement (SLA) parameters in an NS scenario, providing service with the required QoS while minimizing resource usage.

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ARMA-Prediction-Based Online Adaptive Dynamic Resource Allocation in Wireless Virtualized Network

Cited by 19 publications

References 31 publications

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

An improved resource allocation method for mapping service function chains based on A3C

Temporal Characterization of VR Traffic for Network Slicing Requirement Definition

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