A Reliability-Aware Approach for Resource Efficient Virtual Network Function Deployment

Sun, Jian; Zhu, Guangyang; Sun, Gang; Liu, Dan; Li, Yao; Sangaiah, Arun Kumar; Ramachandran, Muthu; Chang, Victor

doi:10.1109/access.2018.2815614

Cited by 43 publications

(29 citation statements)

References 43 publications

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“…The placement of intermediate boxes of service chain was considered in [15], which is also the premise of SFC mapping. [16]- [17] focus on the reliability of SFC deployment. In [16], a SFC deployment scheme which maximizes capital and operational expenditure is proposed to ensure reliability.…”

Section: Related Workmentioning

confidence: 99%

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An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network

Zhu

Liu

2020

IEEE Access

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As one of key technologies of the fifth-generation (5G) communication system, network slicing can share the underlying infrastructure with different application requirements and ensure that the slices can be isolated from each other. This paper proposes an end-to-end (E2E) network slicing resource allocation algorithm based on Deep Q-Networks (DQN), which is suitable for multi-slice and multi-service scenarios. This algorithm jointly considers the radio access network slices and core network slices to dynamically allocate resources to maximize the number of access users. First we build such a model, which is a mixed integer programming problem and it needs to be dynamically adjusted according to the changes of environment. We propose to use DQN algorithm to solve this problem, which can perceive changes in the environment and make dynamic decisions. Under each decision, we need to calculate the reward value of DQN, so we divide the problem into the core side and the access side. Then the dynamic knapsack algorithm and the link mapping algorithm are used to obtain the reward. The simulation results show that the average access rate of DQN scheme is higher than 97%. Compared with the optimal allocation scheme of access side, the average access rate is increased by 9% for delay constrained slices and 5% for rate constrained slices in a dynamic environment.

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Section: Related Workmentioning

confidence: 99%

“…[16]- [17] focus on the reliability of SFC deployment. In [16], a SFC deployment scheme which maximizes capital and operational expenditure is proposed to ensure reliability. In [17], a reliability-aware and delay-constrained (READ) routing optimization framework is established.…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network

Zhu

Liu

2020

IEEE Access

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“…Obviously, 0 < r j < 1. It has mentioned in [11] that to avoid load imbalances and improve reliability, any non-parallel VNFs cannot be deployed on the same node. Meanwhile, for a service to work properly, all involved VNFs need to be executed appropriately.…”

Section: Problem Formulation a High-reliability Dedicated Backumentioning

confidence: 99%

Network Function Parallelization for High Reliability and Low Latency Services

2020

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In 5G-and-beyond wireless communication systems, Network Function Virtualization (NFV) has been widely acknowledged as an important network architecture solution to meet diverse service requirements in various scenarios. However, with the increase of network functions, the introduction of NFV may significantly increase the delay of traffic flows, which is much undesired, especially for Ultra Reliable and Low Latency Communication (URLLC) service. Network Function Parallelism (NFP) architecture has been recently proposed as an effective technique to address the bottleneck of NFV technology. NFP can potentially improve the reliability and reduce the delay of service function chains (SFCs). In this paper, we propose a learning based SFC deployment strategy under NFP architecture with aim to improve the service reliability while reducing the end-to-end service delay. Specifically, service reliability is improved by deploying backup virtual network function (VNF) nodes, while the flow delay is reduced via parallel network function processing. We formulate the VNF deployment as an integer-programming problem with objective of minimizing the reserved computing and bandwidth resources, while guaranteeing the service reliability and end-to-end delay. Considering the hardness and properties of the problem, we transform it as a Markov Decision Process (MDP), and employ a reinforcement-learning algorithm to solve it. We conduct simulations and the numerical results demonstrate that the proposed strategy can significantly improve the service reliability and delay performance, which are crucial for URLLC service.

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“…A survey of the literature revealed that several works have reported on intra-DC network architecture [11]- [13], [23]- [26] and addressed different optimization issues separately. The service chain composition problem in NFV networks was discussed by D'Oro et al in [23].…”

Section: B Related Work 1) Intra-dcmentioning

confidence: 99%

“…An Eigen-decomposition-based approach for the placement of network function chains was presented in [25]. Sun et al in [26] proposed a reliability cost saving algorithm to condense the capital expenditures (CAPEX) and operational expenditures (OPEX) of telecommunication service providers, by reducing the reliability of the SFC deployments. In [12], Liu et al discussed the optimal deployment of new service function chains and readjustment of the in-service chains dynamically.…”

Section: B Related Work 1) Intra-dcmentioning

confidence: 99%

Communication and Computing Cost Optimization of Meshed Hierarchical NFV Datacenters

Kar

Lin³

2020

IEEE Access

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Telecommunication carriers of 5G-MEC are re-architecting their central offices and mobile base stations as datacenters with network function virtualization (NFV) technology. These datacenters (DCs) are known as edge datacenters that help network operators speed deployment and reduce costs. Previously, the use of NFV was limited to within a datacenter (DC) known as intra-DC. Recently, many studies have been conducted into the use of NFV across DCs, i.e., inter-DC. However, these NFV inter-DC architectures have limited communication between DCs with either horizontal or vertical connectivity. In this paper, we propose a generic architecture of such edge NFV datacenters with both horizontal and vertical connectivity, and demonstrate the consequences of both vertical and horizontal connectivity between DCs in terms of communication and computing costs. We formulate a cost optimization problem with latency and capacity as constraints by estimating the traffic dispatch rate between DCs. We propose a vertical-horizontal communication (VHC) heuristic solution to the NP-hard problem. Compared to horizontal connectivity, our results show that vertical connectivity helps to reduce computing costs by 10-30%. However, both vertical and horizontal communications together can help to reduce such costs by 20-30% compared to only vertical communication. INDEX TERMS Inter-DC connectivity, VNF placement, service chaining, communication, computing. BINAYAK KAR (Member, IEEE) received the Ph.D. degree in computer science and information engineering from National Central University

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A Reliability-Aware Approach for Resource Efficient Virtual Network Function Deployment

Cited by 43 publications

References 43 publications

An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network

An End-to-End Network Slicing Algorithm Based on Deep Q-Learning for 5G Network

Network Function Parallelization for High Reliability and Low Latency Services

Communication and Computing Cost Optimization of Meshed Hierarchical NFV Datacenters

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