Inter-data-center network traffic prediction with elephant flows

Li, Yang; Liu, Hong; Yang, Wenjun; Hu, Dianming; Xu, Wei

doi:10.1109/noms.2016.7502814

Cited by 27 publications

(16 citation statements)

References 18 publications

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“…Non-TSF approaches were investigated in [94,274,365] to infer traffic volumes from flow count and packet header fields. Although higher prediction error rates are experienced, these rates remain relatively low not only for NNs but also for other supervised learning techniques, such as GPR and oBMM.…”

Section: Discussionmentioning

confidence: 99%

“…to reduce monitoring overhead have been proposed in the literature. Unfortunately, the current ML-based solution proposed in [274], is not conclusive and shows contradicting prediction accuracy results. Instead, Poupart et al [365] use classifiers to identify elephant flows.…”

Section: Cost Of Predictionsmentioning

confidence: 98%

“…In contrast to TSF methods, network traffic can be predicted leveraging other methods and features. For instance, Li et al [274] propose a frequency domain based method for network traffic flows, instead of just traffic volume. The focus is on predicting incoming and outgoing traffic volume on an inter-data center link dominated by elephant flows.…”

Section: Traffic Prediction As a Non-tsf Problemmentioning

confidence: 99%

“…Recently, two solutions have been investigated in order to overcome this issue, (i) traffic sampling and interpolation [274], and (ii) leveraging features other than traffic volume for traffic prediction [365]. Indeed, various flow sampling techniques (stochastic/deterministic, spacial/temporal, etc.)…”

Section: Cost Of Predictionsmentioning

confidence: 99%

See 3 more Smart Citations

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Boutaba

Salahuddin

Limam

et al. 2018

J Internet Serv Appl

794

391

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Machine Learning (ML) has been enjoying an unprecedented surge in applications that solve problems and enable automation in diverse domains. Primarily, this is due to the explosion in the availability of data, significant improvements in ML techniques, and advancement in computing capabilities. Undoubtedly, ML has been applied to various mundane and complex problems arising in network operation and management. There are various surveys on ML for specific areas in networking or for specific network technologies. This survey is original, since it jointly presents the application of diverse ML techniques in various key areas of networking across different network technologies. In this way, readers will benefit from a comprehensive discussion on the different learning paradigms and ML techniques applied to fundamental problems in networking, including traffic prediction, routing and classification, congestion control, resource and fault management, QoS and QoE management, and network security. Furthermore, this survey delineates the limitations, give insights, research challenges and future opportunities to advance ML in networking. Therefore, this is a timely contribution of the implications of ML for networking, that is pushing the barriers of autonomic network operation and management.

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Section: Discussionmentioning

confidence: 99%

Section: Cost Of Predictionsmentioning

confidence: 98%

Section: Traffic Prediction As a Non-tsf Problemmentioning

confidence: 99%

Section: Cost Of Predictionsmentioning

confidence: 99%

See 2 more Smart Citations

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Boutaba

Salahuddin

Limam

et al. 2018

J Internet Serv Appl

794

391

View full text Add to dashboard Cite

show abstract

“…Previous studies [7]- [9] investigated machine learningbased traffic prediction for realizing proactive control. The authors in [7] utilized artificial neural networks, while those in [8] utilized deep learning, which is a type of neural network. However, a neural network-based computational process generally requires a large amount of training data and its learning procedure is time consuming owing to its complex learning model.…”

Section: Resource Adjustmentsmentioning

confidence: 99%

Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

Hirayama

Miyazawa

Jibiki

et al. 2021

IEICE Trans. Inf. & Syst.

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Network function virtualization (NFV) enables network operators to flexibly provide diverse virtualized functions for services such as Internet of things (IoT) and mobile applications. To meet multiple quality of service (QoS) requirements against time-varying network environments, infrastructure providers must dynamically adjust the amount of computational resources, such as CPU, assigned to virtual network functions (VNFs). To provide agile resource control and adaptiveness, predicting the virtual server load via machine learning technologies is an effective approach to the proactive control of network systems. In this paper, we propose an adjustment mechanism for regressors based on forgetting and dynamic ensemble executed in a shorter time than that of our previous work. The framework includes a reducing training data method based on sparse model regression. By making a short list of training data derived from the sparse regression model, the relearning time can be reduced to about 57% without degrading provisioning accuracy.

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AI Algorithms in Networks

2022

Artificial Intelligence and Quantum Computing for Advanced Wireless Networks

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Inter-data-center network traffic prediction with elephant flows

Cited by 27 publications

References 18 publications

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Sparse Regression Model-Based Relearning Architecture for Shortening Learning Time in Traffic Prediction

AI Algorithms in Networks

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