Online flow size prediction for improved network routing

Poupart, Pascal; Chen, Zhitang; Jaini, Priyank; Fung, Fred; Susanto, Hengky; Geng, Yanhui; Chen, Li; Chen, Kai; Jiang, Hao

doi:10.1109/icnp.2016.7785324

Cited by 75 publications

(51 citation statements)

References 6 publications

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“…To illustrate the relationship between these up-to-date advances and the MLN workflow, in Table 1 we divide literature studies into several application scenarios and show how they perform at each step of the MLN workflow. Without ML techniques, the typical solutions for these advances are involved with time-series analytics [1,9], statistical methods [1,5,7,8] and rule-based heuristic algorithms [2][3][4][5]10], which are often more interpretable and easier to implement. However, ML-based methods have a stronger ability to provide a fine-grained strategy and can achieve higher prediction accuracy by extracting hidden information from historical data.…”

Section: Overview Of Recent Advancesmentioning

confidence: 99%

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Machine Learning for Networking: Workflow, Advances and Opportunities

et al. 2018

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Recently, machine learning has been used in every possible field to leverage its amazing power. For a long time, the net-working and distributed computing system is the key infrastructure to provide efficient computational resource for machine learning. Networking itself can also benefit from this promising technology. This article focuses on the application of Machine Learning techniques for Networking (MLN), which can not only help solve the intractable old network questions but also stimulate new network applications. In this article, we summarize the basic workflow to explain how to apply the machine learning technology in the networking domain. Then we provide a selective survey of the latest representative advances with explanations on their design principles and benefits. These advances are divided into several network design objectives and the detailed information of how they perform in each step of MLN workflow is presented. Finally, we shed light on the new opportunities on networking design and community building of this new inter-discipline. Our goal is to provide a broad research guideline on networking with machine learning to help and motivate researchers to develop innovative algorithms, standards and frameworks.Comment: 8 pages, 2 figure

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Section: Overview Of Recent Advancesmentioning

confidence: 99%

“…Another method is inspired by the end-to-end deep learning approach. It takes some easily obtained information (e.g., bits of a header in the first few flow packets) as direct input and extract features automatically with the help of the learning model [10].…”

Section: Traffic Prediction and Classificationmentioning

confidence: 99%

Machine Learning for Networking: Workflow, Advances and Opportunities

et al. 2018

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“…Poupart et al [365] explore the use of different ML techniques for flow size prediction and elephant flow detection. These techniques include gaussian processes regression (GPR), online bayesian moment matching (oBMM) and a (106, 60, 40, 1) MLP-NN.…”

Section: Traffic Prediction As a Non-tsf Problemmentioning

confidence: 99%

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

Boutaba

Salahuddin

Limam

et al. 2018

J Internet Serv Appl

764

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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“…Pouper et al [21] propose the use of neural networks, Gaussian mixture models and Gaussian process regression for the prediction of the flow size. Xiao et al [22] use C4.5 decision trees and Viljoen et al [23] are using a neural network to classify flows into mice and elephant.…”

Section: B Elephant Flow Detectionmentioning

confidence: 99%

Network Function Offloading Through Classification of Elephant Flows

Durner

Kellerer

2020

IEEE Trans. Netw. Serv. Manage.

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With the move from traditional hardware appliance based network functions to Network Function Virtualization, software development is decoupled from the hardware. However, as a network function is no longer optimized for hardware, beneficial features of networking hardware may not be used any more. Solutions such as SDN or NIC offloading aim to overcome this antipodes by integrating networking hardware into the packet processing pipelines. On the one hand, offloading traffic of network functions to hardware can increase throughput and reduce resource consumption. On the other hand, the number of parallel flows in a network can be very high, exhausting the capacity of the tables of the networking hardware and force the system to fall back to software processing. Fortunately, it is known that a large portion of the flows in the internet are mice flows, whereas the majority of the traffic is constituted by elephant flows. If the elephant flows can be detected efficiently, the hardware tables can be used more efficiently as a larger share of the traffic can be offloaded. We introduce a machine learning based approach that takes its decision with the first packet of a flow. A fundamentally different approach is using packet sampling for the offloading decision. We are evaluating both approaches in terms of complexity, offloaded share of the traffic and table occupation. The results show that a machine learning based offloading decision is possible with the first packet. The sampling approach only reaches a comparable performance at very high sampling rates.

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Online flow size prediction for improved network routing

Cited by 75 publications

References 6 publications

Machine Learning for Networking: Workflow, Advances and Opportunities

Machine Learning for Networking: Workflow, Advances and Opportunities

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

Network Function Offloading Through Classification of Elephant Flows

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