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Designing the Switch/Router
Published in James Aweya, Designing Switch/Routers, 2023
During metering, packets that violate the assigned contract can be marked for treatment in various ways including the following:Unconditionally drop the packetDrop packet if there are no internal resources available to handle the packetPass on the packet to the downstream device to make the dropping decision.The egress line card can also mark packets to allow downstream devices to decide whether to drop such packets when they experience local resource oversubscription, or when local traffic metering indicates that the packet stream does not conform to some defined traffic profile. The egress line card may also perform traffic shaping as discussed in Chapter 9 of Volume 1. Traffic shaping is a mechanism used to smooth out the flow of packets; it is used to regulate the rate and volume of traffic sent into the external network.
ATM Networking: Implementation Considerations
Published in P. S. Neelakanta, ATM Telecommunications, 2018
Traffic shaping, (see Chapter 5) refers to the controlling of the traffic load offered to the network, in order to minimize congestion. It smoothes out traffic flow and reduces cell clumping, which results in a fair allocation of resources and reduced average delay time. Traffic shaping takes place on the transmitting interface, in conformance with the traffic contract and uses a form of the leaky-bucket algorithm known as token bucket.
Rate Management Mechanisms in Switch/Routers
Published in James Aweya, Designing Switch/Routers, 2023
Traffic shaping is the process of smoothing out packet flows by regulating the rate and volume of traffic admitted into the network. Typically, traffic shaping is used to adjust the flow rate of packets when certain criteria are met/matched. The criteria can be, all packets arriving at the shaper, or certain packets identified based on some defined bits in the packet headers (e.g., IP Precedence, IP Differentiated Services Code Point (DSCP)).
Adaptive learning on mobile network traffic data
Published in Connection Science, 2019
Zhen Liu, Nathalie Japkowicz, Ruoyu Wang, Deyu Tang
In recent years, the area of mobile traffic classification has grown dramatically. This was caused, mainly, by the ubiquity of mobile devices and cellular data networks. The ability to classify mobile network traffic has significant implications in many domains including bandwidth allocation, traffic shaping and QoS provision (Baccarelli et al., 2016; Shojafar, Cordeschi, Amendola, & Baccarelli, 2015). Machine learning based traffic classification techniques attract a lot of researchers. Typically, the mobile traffic classification problem is characterised as follows: IP packets with the same five tuples {srcIP, dstIP, srcPort, dstPort, Protocol} are grouped into flows and each flow is represented by flow features, so as to build flow samples for training a classification model. However, the flow feature value distribution/class distribution may vary due to the non-stationary nature of the network environment, user habits and app versions. In such dynamic environments, data distributions can change over time yielding a situation known as the concept drift on data stream (Gama, Žliobaitė, Bifet, Pechenizkiy, & Bouchachia, 2014). As a consequence, the static model built prior to a concept drift is unable to make correct decisions on data that occurs after that concept drift.
SDN in the home: A survey of home network solutions using Software Defined Networking
Published in Cogent Engineering, 2018
Abdalkrim M. Alshnta, Mohd Faizal Abdollah, Ahmed Al-Haiqi
The most popular subject in this category is the QoS and quality of user experience (QoE) when using home network applications (Abuteir, Fladenmuller, & Fourmaux, 2016; Agapiou, Papafili, & Agapiou, 2014; Bakhshi & Ghita, 2016; Bozkurt & Benson, 2016; Eghbali & Wong, 2015; Gharakheili, Bass, Exton, & Sivaraman, 2014; Jang, Huang, & Yeh, 2016; Kumar, Gharakheili, & Sivaraman, 2013; Moyano et al., 2017; Trajkovska, Aeschlimann, Marti, Bohnert, & Salvachúa, 2014; Yang, Wang, Nguyen, & Lu, 2016). The target application in these works is generally multimedia and video streaming, and the aim is to optimize bandwidth allocation for different network applications to improve the user experience. This optimisation is mostly based on the user preferences or profile, but can also be derived from dynamic traffic shaping based on collected traffic statistics (Abuteir, Fladenmuller, & Fourmaux, 2016), automatic identification of applications (Yang, Wang, Nguyen, & Lu, 2016) or a proposed bandwidth allocation algorithm (Jang, Huang, & Yeh, 2016). Most of the works enable the ISP of controlling the service quality from the cloud, though few works depend on local solution using in-home SDN controller (Abuteir et al., 2016), (Bozkurt & Benson, 2016), (Bakhshi & Ghita, 2016). One work also proposes a novel pricing scheme for ISPs, who can implement time-dependent hybrid pricing through SDN APIs (Eghbali & Wong, 2015).