FastScale: Fast Scaling Out of Network Functions

Chen, Xiang; Chen, Yuxin; Huang, Qun; Zhou, Haifeng; Zhang, Dong; Wu, Chunming; Xing, Junchi

doi:10.1109/infocomwkshps50562.2020.9162751

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…Chen et al 16 addressed the joint optimization problem of SFC elastic scaling and routing by proposing a greedy algorithm, which can reduce the routing cost caused by scaling operation. Chen et al 17 proposed a framework that generates a chainwide scale-out plan within a single step. The framework detects overloaded VNF using two algorithms based on topological sorting and maximum flow.…”

Section: Dynamic Deploymentmentioning

confidence: 99%

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Cai

Qian

Luo

et al. 2022

Int J of Intelligent Sys

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Network function virtualization is a promising technology for providing personalized services via agile service function chains (SFCs). Flexible SFC orchestration and rational resource allocation are pivotal for improving the SFC's quality of service (QoS). However, the requirements for computational load and resources have frequently been changing. Consequently, static resource allocation can result in resource insufficiency when SFCs turn busy and resource waste due to resource overplus when SFCs are idle. Since a dynamic resource allocation is necessary, the existing dynamic resource allocation methods' responses have often been delayed. This paper proposes an SFC active reconfiguration mechanism (SARM) based on computational load and resource demand. The SARM predicts nodes' computation loads and SFCs' resource demands and uses these predictions to estimate future QoS and develop the SFC reconfiguration strategy. The SARM considers multiple factors and applies a heuristic algorithm to achieve the tradeoff between migration cost and QoS preservation. The experiments demonstrate that the SARM can effectively predict the nodes' load and the resource demand of SFCs. In addition, the SARM can successfully identify the SFCs to reconfigure and reduce the QoS maintenance costs. The simulation results indicate that the average delays of the SFCs can be reduced by at least 26%.

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Section: Dynamic Deploymentmentioning

confidence: 99%

SARM: Service function chain active reconfiguration mechanism based on load and demand prediction

Cai

Qian

Luo

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

show abstract

“…This approach ensures sufficient resources for the time-varying traffic demand. FastScale [13] proposes a chain-wide scaling approach by considering the downstream VNFs. Similarly, there have been some efforts on vertical scaling approaches for the VNF scaling problem.…”

Section: Related Workmentioning

confidence: 99%

“…CPU, memory or bandwidth can easily become bottlenecks limiting the performance of a VNF [10], [11], [12]. Secondly, many works [13], [9] ignore VNF underutilization incurred by resource reservation which exacerbates the resource scarcity in edge clouds.…”

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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