Packet loss rate prediction using a universal indicator of traffic

Mehrvar, H.R.; Soleymani, M.R.

doi:10.1109/icc.2001.937277

Cited by 11 publications

(3 citation statements)

References 14 publications

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“…In other words, dropping packets occurs when the link is overloaded and the router's queue has overflowed [13]. On the other hand, NN is a great way of packet drop prediction as it was used widely in TCP/IP networking [14,15].…”

Section: Neural Network Designmentioning

confidence: 99%

A Neural Network Based Traffic-Aware Forwarding Strategy in Named Data Networking

Bazmi¹,

Keshtgary²

2016

IIUMEJ

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Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) Based Traffic-aware Forwarding strategy (NNTF) is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

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Section: Neural Network Designmentioning

confidence: 99%

A Neural Network Based Traffic-Aware Forwarding Strategy in Named Data Networking

Bazmi¹,

Keshtgary²

2016

IIUMEJ

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“…There are some analytical approaches that model packet loss, but they focus on the level of a single path. Mehrvar and Soleymani [8] developed a neural network model to predict packet loss rate at the level of the queue. P. S. Rossi, G. Romano, F. Palmieri, and G. Ianello [9] modeled end-to-end packet loss rate for UDP traffic using a hidden Markov model.…”

Section: Related Workmentioning

confidence: 99%

Load balancing algorithm based on time series prediction of packet loss rate

Cai

Wang

et al. 2009

2009 4th IEEE Conference on Industrial Electronics and Applications

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This paper proposes a new load balancing mechanism, called PPLR (prediction packet loss rate), which is based on time series prediction. Load balancing is needed in many fields; however, current approaches have problems such as lacking of preparing for sudden spikes. In this paper, we describe how PPLR dynamically predicts loss levels over all available network paths and adjust the allocation proportion of packets for next prediction period. We present our simulation work using NS-2 and discuss performance comparison between PPLR and ECMP mechanisms. The results show that PPLR not only significantly reduces the packet loss rate, but also has a good effect on the packet delay.Index Terms-load balancing, multipath routing, prediction, proactive probing I.

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“…In IP network, the packet dropping is used as a signal for congestion detection and avoidance [19]. Using the NN is one way of predicting the drop on the network [20] [21]. The authors in this paper, attempt to use the drop occurrence prediction in link as a signal to detect the congestion and to avoid it.…”

Section: Nn Based Congestion Control Algorithmmentioning

confidence: 99%

A neural network based congestion control algorithm for content-centric networks

Bazmi

Keshtgary

2014

JACST

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Communication across the Internet has transformed over the years, generated primarily by changes in the importance of content distribution. In the twenty-first century, people are more concerned with the content rather than the location of the information. Content-Centric Networking (CCN) is a new Internet architecture, which aims to access content by a name rather than the IP address of a host. Having the content, CCN which is natively pull-based functions based on the requests received from customers. It is also combined with the availability of in-network chaching. Because of the availability of in-network caching in CCN, chunks may be served by multiple sources. This multi-path transfer in CCN makes TCP-based congestion control mechanisms inefficient for CCN. In this paper a new congestion control algorithm is proposed, which is based on Neural Network prediction over content-centric networks. The designed NN is implemented in each router to predict adaptively the existence of the congestion on link given the current status of the network. The results demonstrate that the proposed congestion control algorithm can effectively improve throughput by 85.53%. This improvement is done by preventing queue overflow from happening, which will result in reductions in packet drop in the network.

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Packet loss rate prediction using a universal indicator of traffic

Cited by 11 publications

References 14 publications

A Neural Network Based Traffic-Aware Forwarding Strategy in Named Data Networking

A Neural Network Based Traffic-Aware Forwarding Strategy in Named Data Networking

Load balancing algorithm based on time series prediction of packet loss rate

A neural network based congestion control algorithm for content-centric networks

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