2022
DOI: 10.1109/tpds.2022.3204209
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ScaleFlux: Efficient Stateful Scaling in NFV

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Cited by 7 publications
(3 citation statements)
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References 55 publications
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“…Vincenzo Eramo et al [20] proposed a solution for NFV environments resource orchestration in which the different values of the over-provisioning and under-provisioning cost was considered. Liu et al [21] proposed a complete stateful scaling system that efficiently reduces flow-level latency and achieves near-optimal resource usage to deal with time-varying loads in NFV environment. Mahsa Moradi et al [22] analyzed and compared three algorithms of machine learning.…”
Section: Related Researchmentioning
confidence: 99%
See 1 more Smart Citation
“…Vincenzo Eramo et al [20] proposed a solution for NFV environments resource orchestration in which the different values of the over-provisioning and under-provisioning cost was considered. Liu et al [21] proposed a complete stateful scaling system that efficiently reduces flow-level latency and achieves near-optimal resource usage to deal with time-varying loads in NFV environment. Mahsa Moradi et al [22] analyzed and compared three algorithms of machine learning.…”
Section: Related Researchmentioning
confidence: 99%
“…To evaluate the effectiveness of the method, we selected the original LSTM and the ABCNN-LSTM [21] as the comparison algorithms. Both of them compute loss with the difference between predicted value of network traffic and actual value of network traffic, without considering the operation of the NFV network.…”
Section: Contrast Algorithmmentioning
confidence: 99%
“…This is due to the continous state update between the active and stand-by instances which would result in a high consumption of bandwidth resources, potentially resulting in network congestion, rendering the above approaches unsuited for a practical scenario in which a number of VNFs are stateful. While considering stateful VNFs, the works in [74][75][76][77], deal with the problem of how to manage and transfer states from one VNF instance to a new VNF instance while considering elastic control events such as scale-in/-out and load balancing. Under such events, the states can be stored in the local state memory of the current VNF instance, then transferred to the new VNF instance only when an elastic control event is triggered.…”
Section: Fault Tolerant Orchestration Of Virtual Network Functionsmentioning
confidence: 99%