Cognitive Packet Network for Bilateral Asymmetric Connections

Gelenbe, Erol; Kazhmaganbetova, Zarina

doi:10.1109/tii.2014.2321740

Cited by 30 publications

(21 citation statements)

References 49 publications

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“…One approach for consideration in future work is to resequence the data [36] so as to drop data items before they enter into the learning algorithm if they have been superseded by fresher and more relevant items. Another important direction is to consider bandwidth optimisation or bandwidth guarantees, in addition to delay minimisation, as is done in recent work with CPN where applications require asymmetric QoS (delay for upstream and bandwidth for downstream) [37], as well as energy consumption aspects in the network as a whole [38]. Another question of interest would be to consider the time is takes [39], [40], as well as the energy consumption, for finding users or other resources such as virtual machines or files, in a large overlay network, prior to setting up connections to the appropriate overlay node or to a destination node that can be reached from several overlay nodes.…”

Section: Discussionmentioning

confidence: 99%

Big Data for Autonomic Intercontinental Overlays

Brun

Wang

Gelenbe

2016

IEEE J. Select. Areas Commun.

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Abstract-This paper uses Big Data and Machine Learning for the real-time management of Internet scale Qualityof-Service Route Optimisation with an overlay network. Based on the collection of data sampled each 2 minutes over a large number of source-destinations pairs, we show that intercontinental Internet Protocol (IP) paths are far from optimal with respect to Quality of Service (QoS) metrics such as end-to-end round-trip delay. We therefore develop a machine learning based scheme that exploits large scale data collected from communicating node pairs in a multi-hop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from Cognitive Packet Network protocol, it uses Random Neural Networks with Reinforcement Learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some 2 × 10 6 measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently.

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Section: Discussionmentioning

confidence: 99%

Big Data for Autonomic Intercontinental Overlays

Brun

Wang

Gelenbe

2016

IEEE J. Select. Areas Commun.

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“…Future work can exploit the insight gained in this paper for the detection and mitigation of signalling attacks in mobile networks. One aspect that requires attention is the identification of possible locations, such as specific cells, where attacks may originate, and methods related to search and smart traffic routing may prove valuable in this context [50,51]. Another important aspect relates to identifying sets of representative features for the detection of signalling attacks and storms, and of the misbehaving UEs.…”

Section: Discussionmentioning

confidence: 99%

Storms in mobile networks

Görbil¹,

Abdelrahman²,

Pavloski³

et al. 2014

Proceedings of the 10th ACM Symposium on QoS and Security for Wireless and Mobile Networks

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Abstract-Mobile networks are vulnerable to signalling attacks and storms that are caused by traffic patterns that overload the control plane, and differ from distributed denial of service (DDoS) attacks in the Internet since they directly attack the control plane, and also reserve wireless bandwidth without actually using it. Such attacks can result from malware and mobile botnets, as well as from poorly designed applications, and can cause service outages in 3G and 4G networks which have been experienced by mobile operators. Since the radio resource control (RRC) protocol in 3G and 4G networks is particularly susceptible to such attacks, we analyze their effect with a mathematical model that helps to predict the congestion that is caused by an attack. A detailed simulation model of a mobile network is used to better understand the temporal dynamics of user behavior and signalling in the network and to show how RRC based signalling attacks and storms cause significant problems in the control plane and the user plane of the network. Our analysis also serves to identify how storms can be detected, and to propose how system parameters can be chosen to mitigate their effect.

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“…CDMA evolution data only (1xEV-DO), 3GPP high speed packet data access (HSPA), WiMAX, and LTE are widely used cellular packet data networks [10][11][12][13]. They have network asymmetry with respect to network performance [27,28]. To satisfy demanding mobile subscribers, networks allocate more downlink bandwidth than uplink bandwidth because mobile subscribers generally download content from the internet.…”

Section: Problem Statementmentioning

confidence: 99%

Protecting Download Traffic from Upload Traffic over Asymmetric Wireless Links

Kim

Lee

Kim

et al. 2018

Wireless Communications and Mobile Computing

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Many varied mobile device networks have been developed with the advancement of communication and network technologies. Cellular data networks are currently the most widely used, and the number of cellular network subscriptions has increased steadily. Most recent wireless access technologies employ asymmetric uplinks and downlinks because mobile subscribers usually download contents from the Internet. Therefore, most cellular network service providers allocate more bandwidth to downlinks than uplinks for mobile subscribers. However, this asymmetry can have unexpected influence on network performance, particularly TCP performance. When the uplink interface is congested, TCP ACK packets are delayed by TCP data packets on the uplink, causing considerable TCP retransmissions on the downlink channel. Thus, downlink bandwidth cannot be fully utilized, which results in significantly degraded downlink throughput. To resolve this problem, this paper proposes a feedback scheme, network traffic chunk regulator (NCR). We analyzed the aforementioned problem through the empirical study, and we designed and implemented NCR based on the analysis. NCR adaptively controls TCP according to the degree of link usage asymmetry. We evaluate NCR performance through simulations and experiments with real devices. We verify that the proposed scheme allows the downlink traffic to not interfere with the aggressive uplink traffic. Thus, NCR increases total link utilization and aggregated throughput significantly, without imposing additional overhead on base or mobile stations.

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Cognitive Packet Network for Bilateral Asymmetric Connections

Abstract: 14.10.14 KB. Ok to add accepted version to spira

Cited by 30 publications

References 49 publications

Big Data for Autonomic Intercontinental Overlays

Big Data for Autonomic Intercontinental Overlays

Storms in mobile networks

Protecting Download Traffic from Upload Traffic over Asymmetric Wireless Links

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