Mobile network traffic: A user behaviour model

Tsompanidis, Ilias; Zahran, Ahmed H.; Sreenan, Cormac J.

doi:10.1109/wmnc.2014.6878862

Cited by 24 publications

(9 citation statements)

References 21 publications

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“…As an example, Li et al (2015) show that the behavior-based popularity of Android applications follows the Pareto principle. Tsompanidis et al (2014) also discover that web traffic flow size can be explained by the Pareto distribution. Similarly, researchers presented a list of social and organizational power laws, one kind of heavy-tailed distribution, to describe human behavior see Scholz (2015) and Andriani & McKelvey (2009).…”

Section: Related Workmentioning

confidence: 89%

Modeling Bursts and Heavy Tails in Inter-Arrival Claims in Non-Life Insurance

Hanafy¹

2020

JFRM

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Current insurance models, assuming that inter-arrival time of claims, are distributed randomly and thus well approximated by Poisson processes. Here we provide clear proof that the timing of inter-claims fits by non-Poisson patterns, marked by rapid events, separated by long periods of inactivity. The time of inter-arrival claims will be heavy tailed, most claims will be executed quickly, while a few will have very long waiting times. We will model and analysis of insurance based on claim inter-arrival time, the time interval between two successive claims and the ability to carry out such modeling was limited by a lack of ecologically relevant data collected on claims inter-arrival. We propose a structured process behavior model based on data from Egyptian fire insurance company. Our analysis shows that claim activities can be represented by non-Poisson processes and that the subsequent distribution of inter-arrival activity times follows the Pareto distribution. These results will help researchers understand daily behavioral trends and create more sophisticated predictive models of claims.

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Section: Related Workmentioning

confidence: 89%

Modeling Bursts and Heavy Tails in Inter-Arrival Claims in Non-Life Insurance

Hanafy¹

2020

JFRM

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“…1(a)) and a link capacity ranging from ω i,j = 100 Mb/s to ω i,j = 25 Gb/s. We study a scenario composed of a single CU connected to a set of RRHs from 4 to 16 (R = 4 to R = 16) and adopt the values of [38]- [40] to define three types of applications, i.e., medical, IoT, and video streaming applications. We consider s = 1 for medical applications (uRLLC) which use split 1 with λ 1 u = 120 Kb/s, s = 2 for IoT messages (mMTC) which use split 2 with λ 2 u = 30 Kb/s, and finally s = 3 for video streaming applications (eMBB) which need a higher degree of centralization (i.e., split 3) with λ 3 u = 20 Mb/s.…”

Section: A Simulation Scenariomentioning

confidence: 99%

SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Ojaghi

Adelantado

Antonopoulos

et al. 2022

IEEE Systems Journal

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5G mobile networks are envisioned to substantiate new vertical services with diverse performance requirements. Slicing in the Radio Access Network (RAN) promises an efficient solution for these diversified needs of 5G networks, which foresees the separation of the Base Station (BS) functionality between the Central Unit (CU) and the distributed Remote Radio Heads (RRHs). In this paper, we formulate a Mixed Integer Programming (MIP) framework that maximizes the throughput by jointly selecting the optimal Functional Split (FS) and the routing path from a connected User Equipment (UE) to the CU, while satisfying the agreed Service Level Agreements (SLAs) of each service. Furthermore, we propose an effective heuristic, SlicedRAN, which creates isolated RAN slices premised on the service requirements connected through a Fronthaul/Backhaul (FH/BH) network and obtains near-optimal solutions in a short computing time compared to the MIP framework. Our results show that there is a trade-off between the architecture of the FH/BH network and the minimum SLA of each slice, which provides a solution to efficiently design a virtualized network infrastructure. According to the results, the SlicedRAN outperforms existing State-of-the-Art (SoA) up to 112% gain in throughput. Results are shown close to the optimal results, with a loss below 5%.

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Section: A Experimental Platformmentioning

confidence: 99%

“…A User typically opens a page, spends some time on it (dwell time) before proceeding to the next page [19]. The dwell times depend on the type of the content and attention span of the user [19]. Dwell times are modelled with a Weibull distribution, whose scale parameter λ depends on the type of content, as justified and detailed in [20].…”

Section: A Experimental Platformmentioning

confidence: 99%

Sizing Network Buffers: An HTTP Adaptive Streaming Perspective

Raca

Zahran

Sreenan

2016

2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW)

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Access to the full text of the published version may require a subscription. Abstract-HTTP Adaptive video Streaming (HAS) is the dominant traffic type on the Internet. When multiple video clients share a bottleneck link many problems arise, notably bandwidth underutilisation, unfairness and instability. Key findings from previous papers show that the "ON-OFF" behaviour of adaptive video clients is the main culprit. In this paper we focus on the network, and specifically the effects of network queue size when multiple video clients share network resources. We conducted experiments using the Mininet virtual network environment streaming real video content to open-source GPAC video clients. We explored how different network buffer sizes, ranging from 1xBDP to 30xBDP (bandwidth-delay-product), affect clients sharing a bottleneck link. Within GPAC, we implemented the published state-of-the-art adaptive video algorithms FESTIVE and BBA-2. We also evaluated impact of web cross-traffic. RightsOur main findings indicate that the "rule-of-thumb" 1xBDP for network buffer sizing causes bandwidth underutilisation, limiting available bandwidth to 70% for all video clients across different round-trip-times (RTT). Interaction between web and HAS clients depends on multiple factors, including adaptation algorithm, bitrate distribution and offered web traffic load. Additionally, operating in an environment with heterogeneous RTTs causes unfairness among competing HAS clients.Based on our experimental results, we propose 2xBDP as a default network queue size in environments when multiple users share network resources with homogeneous RTTs. With heterogeneous RTTs, a BDP value based on the average RTTs for all clients improves fairness among competing clients by 60%.

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Mobile network traffic: A user behaviour model

Cited by 24 publications

References 21 publications

Modeling Bursts and Heavy Tails in Inter-Arrival Claims in Non-Life Insurance

Modeling Bursts and Heavy Tails in Inter-Arrival Claims in Non-Life Insurance

SlicedRAN: Service-Aware Network Slicing Framework for 5G Radio Access Networks

Sizing Network Buffers: An HTTP Adaptive Streaming Perspective

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