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Rate Management Mechanisms in Switch/Routers
Published in James Aweya, Designing Switch/Routers, 2023
Traffic policers typically use the token bucket algorithm for enforcing the average transmit or receive rate of traffic at a network interface while allowing bursts of traffic up to a maximum value (Figure 9.4). The token bucket algorithm offers more flexibility than the leaky-bucket algorithm as a queue because the policer can allow a specified amount of traffic bursts before it starts to discard packets or apply a penalty to packets (based on the queuing priority or packet drop priority).
A Survey of Interest Flooding Attack in Named-Data Networking: Taxonomy, Performance and Future Research Challenges
Published in IETE Technical Review, 2022
Ren-Ting Lee, Yu-Beng Leau, Yong Jin Park, Mohammed Anbar
Rule-based detection adds an extra node or modification to the existing NDN environment. It sets the rule to determine the adaptive threshold and uses this value to identify the abnormality of the network through some measurable statistics. In 2013, other than proposing a rate-based detection, Afanasyev et al. [20] presented a different detection mechanism based on the interfaces. In this method, by using the defined capacity of corresponding interfaces, the Interest limit to control the number of forwarding Interest out from the interfaces is determined. The author modified the Token Bucket Algorithm that widely applied in packet switch networks. The token is needed to forward the Interest from a router. The problem has arisen if all the tokens have been used to transmit malicious Interest. The router cannot send any incoming legitimate Interests, although the pending malicious Interests are still valid. As such, this method is less effective to prevent the attacker from accessing the content requested by authorized users. The relatively modest volume of malicious Interests sent by the attacker in this scenario has increased the resource consumption and limited the router to serve authorized users further.
Feature selection for interest flooding attack in named data networking
Published in International Journal of Computers and Applications, 2021
Naveen Kumar, Ashutosh Kumar Singh, Shashank Srivastava
Afanasyev et al. in [5] have proposed three algorithms, i.e. token bucket with per interface fairness (TBIF), satisfaction-based interest acceptance (SIA), and satisfaction-based pushback (SP) algorithm. TBIF is similar to the classical token bucket algorithm, with the difference that the tokens available for each outgoing interface are equally divided among all the incoming interfaces. In SIA, each interface learns from the past unsatisfied interest packets. The router maintains the satisfaction ratio for each interface and allows interest packets based on this satisfaction ratio. The interest packet will be dropped or not depends on a random variable that is a function of satisfaction ratio. In SP, each incoming interface has its limit to satisfy the interest packet. Routers announce these limits to downstream neighbors, according to which neighbors set their limits. This approach helps legitimate interfaces to improve their statistics. Satisfaction-based pushback is better than the other two techniques in mitigating IFA.
A fair dynamic content store-based congestion control strategy for named data networking
Published in Systems Science & Control Engineering, 2022
Yan-Hong Liu, Feng-Hua Huang, Hua Yang
With the expansion of network scale, all kinds of contradictions and problems of IP network for address-centric have highlighted. To solve these problems, the communication mode of information-centric network (ICN) has evolved from host–host to host–network and the forwarding mechanism has converted from traditional storage-forwarding to buffer-forwarding, which solves the efficient transmission of massive information. Among them, the NDN is the most representative network architecture. It focuses more on the content name, which can effectively solve a series of problems caused by the IP address. In recent years, the problem of NDN congestion control has received significant attentions. According to the role responsibility for reacting to network congestion, the NDN congestion control schemes can be broadly classified into two categories: implicit congestion control and explicit congestion control (C. C. Li et al., 2017). In implicit congestion control scheme, the requester judges the occurrence of network congestion through the retransmission timeout mechanism (RTO), and the network congestion is controlled or alleviated by reducing the sending rate of interest packets. In explicit congestion control scheme, congestion occurrence is detected by the intermediate node, then the congestion information is explicitly feeded back to the receiver, the receiver adjusts the sending rate of interest packets to control the return rate of data packets (Shi et al., 2016). Implicit congestion control scheme is suitable for end-to-end communication transmission protocol. The NDN architecture advocates the pull-based paradigm where in-network caching and multi-path forwarding are pervasive and the data source cannot be determined, which makes it very difficult to estimate timeout, so the implicit congestion control scheme is difficult to implement in NDN. In explicit congestion control scheme, the congestion state is detected by the intermediate node of the router and feeded back to the downstream node or receiver to adjust the forwarding rate of interest packets (data packets). This kind of congestion control scheme is suitable for multi-path forwarding strategy and has attracted a lot of attention of scholars (Zhang et al., 2016). A congestion control algorithm is proposed by routing the content request to the potential content source (Huang et al., 2017). Considering the impact of link delay and interruption, a minimum-delay congestion control algorithm combined with reinforcement learning is given to realize the intelligent forwarding (Y. D. Wang et al., 2020). In reference W. J. Wang and Luo (2018), the weight of the data stream is dynamically adjusted according to its forwarding rate. The token bucket algorithm is adopted to slow down the forwarding rate of data stream exceeding the fair rate, and the congestion information is feeded back to the downstream node through the explicit feedback mechanism. Based on the characteristic of in-network cache in NDN, a store-based congestion control algorithm is designed to solve the burst flows by considering the interaction between router buffer size and congestion control mechanism in Xia and Xu (2013).