Topology Aware Internet Traffic Forecasting Using Neural Networks

Cortez, Paulo; Rio, Miguel; Sousa, Pedro; Rocha, Miguel

doi:10.1007/978-3-540-74695-9_46

Cited by 8 publications

(3 citation statements)

References 11 publications

(20 reference statements)

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“…The sender and receiver have been used to predict RTT and jitter [14]. Forecasting traffic on an Internet link is studied in [15], [16]. End-to-end performance forecasting is attempted in [17] but the data for validation is limited.…”

Section: A Backgroundmentioning

confidence: 99%

On the relationship between fundamental measurements in TCP flows

Clegg

Araújo

Landa

et al. 2013

2013 IEEE International Conference on Communications (ICC)

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Abstract-This paper considers fundamental measurements which drive TCP flows: throughput, RTT and loss. It is clear that throughput is, in some sense, a function of both RTT and loss. In their seminal paper Padyhe et al [1] begin with a mathematical model of the TCP sliding window evolution process and come up with an equation showing that TCP throughput is (roughly) proportional to 1/RT T √ p where p is the probability of packet loss. Their equation is shown to be consistent with data gathered on several links. This paper takes the opposite approach and analyses a large number of packet traces from well-known sources in order to create a data-driven estimate of the functions which relate TCP, loss and RTT. Regression analysis is used to fit models to connect the quantities. The fitted models show different behaviour from that expected in [1].

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Section: A Backgroundmentioning

confidence: 99%

On the relationship between fundamental measurements in TCP flows

Clegg

Araújo

Landa

et al. 2013

2013 IEEE International Conference on Communications (ICC)

Self Cite

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“…Studies of traffic modeling and prediction have been focused on the Internet. For example, neural network [18] is used to predict traffic flows. However, the behavior of Internet traffic is substantially different from the on-chip case.…”

Section: Related Workmentioning

confidence: 99%

On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

Wang

Yang

Jiang

et al. 2014

ACM Trans. Embed. Comput. Syst.

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Modern networks-on-chip (NoC) systems are required to handle complex run-time traffic patterns and unprecedented applications. Data traffics of these applications are difficult to be fully comprehended at design-time so as to optimize the network design. However, it has been discovered that the majority data flows in a network are dominated by less than 10% of the specific pathways. In this paper, we introduce a method that is capable of identifying critical pathways in a network at run-time and, then, can dynamically reconfigure the network to optimize for the network performance subjected to the identified dominated flows. An online learning and analysis scheme is employed to quickly discover the emerged dominated traffic flows and provides a statistical traffic prediction using regression analysis. The architecture of a self-tuning network is also discussed which can be reconfigured by setting up the identified point-to-point paths for the dominance data flows in large traffic volumes. The merits of this new approach are experimentally demonstrated using comprehensive NoC simulators. Compared to the conventional network architectures over a range of realistic applications, the proposed self-tuning network approach can effectively reduce the latency and power consumption by as much as 25% and 24%, respectively. We also evaluated the configuration time and additional hardware cost. This new approach demonstrates the capability of an adaptive NoC to handle more complex and dynamic applications.

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“…For example, neural network [18] is used to predict traffic flows. However, the behavior of Internet traffic is substantially different from the on-chip case.…”

Section: Related Workmentioning

confidence: 99%

On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

Wang

Mak

Yang

et al. 2013

2013 Seventh IEEE/ACM International Symposium on Networks-on-Chip (NoCS)

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Abstract-Modern networks-on-chip (NoC) systems are required to handle complex run-time traffic patterns and unprecedented applications. Data traffics of these applications are difficult to be fully comprehended at design-time so as to optimize the network design. However, it has been discovered that the majority data flows in a network are dominated by less than 10% of the specific pathways. In this paper, we introduce a method that is capable of identifying critical pathways in a network at run-time and, then, can dynamically reconfigure the network to optimize for the network performance subjected to the identified dominated flows. An online learning and analysis scheme is employed to quickly discover the emerged dominated traffic flows and provides a statistical traffic prediction using regression analysis. The architecture of a self-tuning network is also discussed which can be reconfigured by setting up the identified point-to-point paths for the dominance data flows in large traffic volumes. The merits of this new approach are experimentally demonstrated using comprehensive NoC simulators. Compared to the conventional network architectures over a range of realistic applications, the proposed self-tuning network approach can effectively reduce the latency and power consumption by as much as 25% and 24%, respectively. We also evaluated the configuration time and additional hardware cost. This new approach demonstrates the capability of an adaptive NoC to handle more complex and dynamic applications.

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Topology Aware Internet Traffic Forecasting Using Neural Networks

Cited by 8 publications

References 11 publications

On the relationship between fundamental measurements in TCP flows

On the relationship between fundamental measurements in TCP flows

On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

On self-tuning networks-on-chip for dynamic network-flow dominance adaptation

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