Design and Implementation of Network-Aware VNF Migration Mechanism

Yi, Bo; Wang, Xingwei; Huang, Min; Li, Kexin

doi:10.1109/access.2020.2978002

Cited by 25 publications

(11 citation statements)

References 22 publications

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“…VNF migration can realize load balancing, rapid recovery of failures and rapid response to dynamic changes of network services [19]. In [20], a node-aware and link-aware VNF migration method is designed to migrate the VNFs which are deployed on overloaded server nodes. This method does not consider VNFs combination in the process of VNF migration, and the performance still has room for improvement.…”

Section: Vnf Vertical Scaling and Vnf Migrationmentioning

confidence: 99%

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

2021

KSII TIIS

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In the network function virtualization environment, dynamic changes in network traffic will lead to the dynamic changes of service function chain resource demand, which entails timely dynamic adjustment of service function chain resource configuration. At present, most researches solve this problem through virtual network function migration and link rerouting, and there exist some problems such as long service interruption time, excessive network operation cost and high penalty. This paper proposes a dynamic adjustment method of service function chain resource configuration for the dynamic changes of network traffic. First, a dynamic adjustment request of service function chain is generated according to the prediction of network traffic. Second, a dynamic adjustment strategy of service function chain resource configuration is determined according to substrate network resources. Finally, the resource configuration of a service function chain is pre-adjusted according to the dynamic adjustment strategy. Virtual network functions combination and virtual machine reusing are fully considered in this process. The experimental results show that this method can reduce the influence of service function chain resource configuration dynamic adjustment on quality of service, reduce network operation cost and improve the revenue of service providers.

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Section: Vnf Vertical Scaling and Vnf Migrationmentioning

confidence: 99%

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

2021

KSII TIIS

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“…A few scholars have recently turned to VNF scaling in network slicing [19]- [22]. L. Ruiz et al [23] analyzed VNF placement in 5G networks and estimated the maximum number of VNFs within a given period by using traffic prediction algorithms to estimate the maximum value of network slice traffic.…”

Section: Related Workmentioning

confidence: 99%

“…The learning factor is adjusted to balance particles to find the optimal solution globally and locally. The location update is shown in Formula (19).…”

Section: Volume X 2020mentioning

confidence: 99%

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Dynamic Network Slice Scaling Assisted by Prediction in 5G Network

2020

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Network slicing is a key technology in fifth-generation (5G) mobile networks. Slicing divides a physical network into multiple dedicated logical networks to meet the requirements of diverse use cases. Efficient slice deployment algorithms are critical in reducing network operators' costs and energy consumption and in providing users better service. Many researchers have focused on static deployment when investigating network slices, effectively ignoring network operators' requirements for the dynamic deployment and expansion of such slices. In this paper, we first construct a joint optimization problem of cost and energy consumption. Then, we propose a prediction-assisted adaptive network slice expansion algorithm to deploy network slices dynamically. The proposed algorithm consists of three parts. First, we devise a Holt-Winters (HW) prediction algorithm to determine traffic demand for network slices. This method is intended to avoid frequent changes in network topology. Second, we propose a virtual network function (VNF) adaptive scaling strategy to reasonably determine the number of VNFs and resources required for network slices to avoid resource wastage. Finally, we develop a proactive online algorithm to deploy network slices. This method deploys network slices reasonably via the VNF deployment algorithm and link-routing algorithm to ensure slices' service requirements. Resource capacity and delay requirements are also considered in our evaluation to ensure that network costs and energy consumption are minimized. We then perform a series of simulation experiments to compare the proposed method's performance to stateof-the-art dynamic network slicing technologies. Ultimately, our solution is deemed a suitable candidate for dynamic deployment of 5G network slices; the solution demonstrates advantages of high resource utilization, low deployment costs, and low energy consumption.

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“…The main advantages of NFV is the flexibility in allocating processing and bandwidth resources [1,2] and the possibility of running the Virtual Network Function Instances (VNFIs) where and when needed and appropriately dimensioning their memory, disk and processing resources. This increased flexibility allows to efficiently handle the traffic variations and it is achieved by applying some techniques: (i) The vertical scaling [3], whereby cloud resources are reconfigured, e.g., by changing the number of processing cores to the VNFIs as traffic changes; (ii) the VNFI migration [4], whereby the NFVI-PoP executing the VNFI is changed over time with the possibility of performing resource consolidation and consequently achieving cost saving.…”

Section: Introductionmentioning

confidence: 99%

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

Eramo¹,

Valente²,

Catena³

et al. 2021

Future Internet

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Resource prediction algorithms have been recently proposed in Network Function Virtualization architectures. A prediction-based resource allocation is characterized by higher operation costs due to: (i) Resource underestimate that leads to quality of service degradation; (ii) used cloud resource over allocation when a resource overestimate occurs. To reduce such a cost, we propose a cost-aware prediction algorithm able to minimize the sum of the two cost components. The proposed prediction solution is based on a convolutional and Long Short Term Memory neural network to handle the spatial and temporal correlations of the need processing capacities. We compare in a real network and traffic scenario the proposed technique to a traditional one in which the aim is to exactly predict the needed processing capacity. We show how the proposed solution allows for cost advantages in the order of 20%.

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Design and Implementation of Network-Aware VNF Migration Mechanism

Cited by 25 publications

References 22 publications

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

A Dynamic Adjustment Method of Service Function Chain Resource Configuration

Dynamic Network Slice Scaling Assisted by Prediction in 5G Network

Proposal and Investigation of a Convolutional and LSTM Neural Network for the Cost-Aware Resource Prediction in Softwarized Networks

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