FlexNFV: Flexible Network Service Chaining with Dynamic Scaling

Fei, Xincai; Liu, Fangming; Jin, Hai; Li, Bo

doi:10.1109/mnet.001.1900483

Cited by 15 publications

(5 citation statements)

References 7 publications

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“…The FlexNFV framework includes an adaptive NF scaling mechanism across network service chains proposed to effectively manage traffic variations [22]. FlexNFV monitors network function (NF) loads and dynamically scales instances according to chain-level information to avoid performance degradation and guarantee optimal resource use.…”

Section: B Time-series Forecasting Methodsmentioning

confidence: 99%

Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning

Taha,

Sanjalawe,

Al-Daraiseh

et al. 2024

IEEE Access

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Auto-scaler system enables high Quality of Service (QoS) with low cost to survive in a competitive market. Indeed, the auto-scaling of Virtual Network Functionality (VNFs) can adaptively allocate the Cloud resources for various VNFs based on workload demands at any time. However, the intensity of workload is dynamically changed because of the variation in service demand over time. The predominant auto-scaling approaches use scaling rules (threshold-based reactive approach) or scaling policies (schedulebased proactive approach) to adapt resources and meet the performance requirements of each VNF. The reactive approaches can significantly degrade the VNF performance for improper reconfiguration or variation of auto-scaling rules. Conversely, the proactive approaches dynamically adjust the scaling policies according to the workload variation. These approaches rely on accurate workload predictive models (e.g., time-series models). This paper proposes a real-time proactive auto-scalar system based on a deep learning model that can efficiently predict the future values of CPU, Memory, and Bandwidth for VNFs for a Service Function Chain (SFC) to proactively auto-scale the resources allocated to each VNF in a Cloud platform. A hybrid model of MLP-LSTM is used to forecast the values of different features. Auto-correlation is used to identify the abnormal events of instances in the Cloud platform by measuring the repeated pattern for each identified impact feature. Moreover, the auto-scalar system enables to predict the abnormal values for some features during the online stage using the Auto-regression model to meet the QoS requirements of an SFC.

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Section: B Time-series Forecasting Methodsmentioning

confidence: 99%

Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning

Taha,

Sanjalawe,

Al-Daraiseh

et al. 2024

IEEE Access

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“…Many research efforts have focused on horizontal scaling, aiming to accommodate the time-varying network traffic. FlexNFV [8] leverages horizontal scaling to distribute the traffic demand among multiple SF instances. Luo et al [7] also investigate the VNF scaling problem by leveraging an online horizontal scaling approach.…”

Section: Related Workmentioning

confidence: 99%

“…However, most existing approaches largely overlook the inherent nature of VNFs and edge clouds. Many of them such as [7], [8], [9] assume that VNF instances can be scaled without limitations which is not viable in reality. CPU, memory or bandwidth can easily become bottlenecks limiting the performance of a VNF [10], [11], [12].…”

Section: Introductionmentioning

confidence: 99%

B-Scale: Bottleneck-aware VNF Scaling and Flow Routing in Edge Clouds

Chen

Nagel

Cui

et al. 2022

2022 IEEE Symposium on Computers and Communications (ISCC)

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With the ever-growing demand for low-latency network applications, edge computing emerges as a new paradigm that provides computation and storage resources in close proximity to end-users. Many research efforts have resorted to network function virtualization, wherein network applications are provisioned as service function chains at edge clouds. However, due to the traffic dynamics and limited resource capacity at the network edge, how to efficiently embed service chains with latency optimization and resource efficiency remains as a challenging problem.As most existing research efforts largely overlook the bottlenecked resources of VNFs in the VNF scaling, we seek a more realistic approach to provisioning VNF instances across multiple edge clouds. Also, given the limited resources at the edge, it is of significant importance to improve the VNF utilization rate. Specifically, we formulate the VNF scaling problem as an integer linear programming (ILP) problem, aiming to minimize the endto-end latency for service function chains. To solve this problem, we devise a novel bottleneck-aware algorithm that manages the number and deployment of newly created instances. After that, we propose an online algorithm for traffic steering to improve the utilization rates of VNF instances and avoid congestion on hotspot links. The proposed algorithm is shown to provide good performance by trace-driven simulation in real-world topologies.

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“…Driven by the diversity of heterogeneous network security services, the network framework design has been transiting from the monolithic pattern to the softwaried paradigm, which is mainly supported by the network function virtualization (NFV) and software-defined network (SDN) technologies [6][7][8][9]. These technologies present themselves as revolution pivotal network architectural design concepts that leverage the virtualization and cloud infrastructure elasticity for the purpose of supporting this quantum leap of the existing packet core which, in turn, leads to the remarkable improvement of provisioned security network services.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for Resource Efficient Security Service Chain Embedding Oriented to Cloud Datacenter Networks

Qiao

Liu

et al. 2021

IEEE Access

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An important and promising application for the network function virtualization (NFV) technology is in network security, where can dynamically and flexibly accomplish the chaining virtualized security network functions (VSNFs), e.g., network address translation, antispam and packet filter firewall, etc., and thus inspect, monitor or filter traffic flows in the cloud datacenter networks. However, the traffic flows addressed by the VSNFs mainly depend on the security service requirements from mobile users, such as the network security level, end-to-end latency, and security resource, etc. Considering the dynamic nature of cloud datacenter networks, determining the embedding of VSNFs and routing security service paths that optimizes the security resource utilization is a challenging problem, particularly without violating the end-to-end delay constraints and security service requirements. This can also be called security service chain dynamic embedding problem (SSC-DMP). In this paper, we present an NFV-enabled framework for a system that achieves the SSC dynamic embedding in the cloud datacenter networks. We first formulate an integer linear programming (ILP) model to solve the SSC-DMP exactly in small-scale network topology. Then, in order to reduce the time complexity when applying the large-scale network topology, we propose an efficient SSC dynamic embedding solution that is based on the particle swarm optimization. Extensive simulation results show that the proposed algorithm could significantly outperform the current benchmarks at least 35.2% and 23.1% in terms of resource consumption and end-to-end delay, respectively.

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FlexNFV: Flexible Network Service Chaining with Dynamic Scaling

Cited by 15 publications

References 7 publications

Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning

Proactive Auto-Scaling for Service Function Chains in Cloud Computing Based on Deep Learning

B-Scale: Bottleneck-aware VNF Scaling and Flow Routing in Edge Clouds

A Novel Method for Resource Efficient Security Service Chain Embedding Oriented to Cloud Datacenter Networks

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