Characterizing the Spatio-Temporal Inhomogeneity of Mobile Traffic in Large-scale Cellular Data Networks

Wang, Huandong; Ding, Jingtao; Li, Yong; Hui, Pan; Yuan, Jian; Jin, Depeng

doi:10.1145/2757513.2757518

Cited by 45 publications

(28 citation statements)

References 7 publications

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“…These factors, sometimes, compound with each other and further complicate our analysis. For example, significant traffic variation is observed at both fine-grained (hours) and coarse-grained (days) time scale, and across towers deployed in different locations [25,16]. By addressing these challenges, in this paper, we investigate how to extract and model the mobile traffic patterns of thousands of cellular towers in a large scale urban environment via credible dataset collected by one of the largest commercial mobile operators.…”

Section: Introductionmentioning

confidence: 99%

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

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Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis. Categories and Subject Descriptors Modeling techniquesPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from Permissions@acm.org. IMC '15, October 28-30, 2015, Tokyo, Japan.

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Section: Introductionmentioning

confidence: 99%

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Wang

et al. 2017

IEEE/ACM Trans. Networking

Self Cite

202

105

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“…Please note that the HO generation rate depends on the E-UTRAN density, which depends on the population density in our simulation tools. The aggregated signaling workload was modulated according to the temporal distribution measured in [14] (see Fig. 5).…”

Section: B Dynamic Resource Provisioning Algorithm Evaluationmentioning

confidence: 99%

A Queuing Based Dynamic Auto Scaling Algorithm for the LTE EPC Control Plane

Prados-Garzon

Laghrissi

Bagaa

et al. 2018

2018 IEEE Global Communications Conference (GLOBECOM)

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This is the accepted version of the original article published by IEEE.Abstract-Network softwarization paradigm, whose main enabler is Network Functions Virtualization (NFV), facilitates the automation of the management operations and orchestration of the future networks, thus reducing the operational expenditures of the network. The envisioned management practices include the introduction of automation in the scaling of network services. This may enable operators to handle workload fluctuations to keep the desired performance with great agility and reduced costs. This procedure introduces a non-negligible delay in allocating or releasing virtual resources. Therefore, waiting until the system is overloaded or underutilized so as to scale resources up or down could negatively impact user Quality of Experience, or lead to inefficient resource utilization. In this vein, this paper proposes a novel and agile Dynamic Auto Scaling Algorithm for the Long Term Evolution (LTE) virtualized Evolved Packet Core (vEPC) Control Plane (CP). The resources dimensioning stage of the algorithm is based on an original queuing model for the LTE CP. To model the LTE CP, we use an open network of G/G/m queues. We also provide expressions to derive the steady state transition probabilities of the queuing network. Finally, we validate the proper operation of our solution using accurate simulation tools.

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“…Third, the spatial traffic distribution is uneven over a large area. Due to the unequal population sizes in different regions and the difference in user behaviors, the spatial traffic distribution exhibits evident heterogeneity [40,41]. The traffic demand in hot-spots is much larger than that in suburbs.…”

Section: Motivation Contributions and Organizationmentioning

confidence: 99%

Joint Optimization of Content Placement and User Association in Cache‐Enabled Heterogeneous Cellular Networks Based on Flow‐Level Models

Ren

Zhao

et al. 2018

Wireless Communications and Mobile Computing

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Cache-enabled heterogeneous cellular networks (HCNs) have been investigated extensively to alleviate backhaul congestion and reduce content delivery delay. In this paper, we jointly optimize content placement and user association to minimize the average content delivery delay in cache-enabled HCNs based on flow-level models. This formulation considers (1) different timescales of content placement and content delivery, (2) locality of content popularity, and (3) the heterogeneity of spatial traffic distribution, which are often neglected in existing researches. The joint optimization problem is formulated as a mixed integer nonlinear programming problem in load-non-coupled and load-coupled models, respectively. We decouple this problem into two interrelated subproblems and resolve them individually. For the user association problem under a given content placement situation, we propose a content-level selective association algorithm, which allows the requests for different contents at the same location to connect to different base stations (BSs). In addition, we propose a greedy content caching algorithm to add contents to the caches of BSs in an iterative manner. These two algorithms are alternately executed until the caches of all the BSs are filled to capacity. Simulation results show that the proposed algorithm achieves better performance in terms of average delay and backhaul usage compared with traditional content placement and user association approaches.

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Characterizing the Spatio-Temporal Inhomogeneity of Mobile Traffic in Large-scale Cellular Data Networks

Cited by 45 publications

References 7 publications

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

A Queuing Based Dynamic Auto Scaling Algorithm for the LTE EPC Control Plane

Joint Optimization of Content Placement and User Association in Cache‐Enabled Heterogeneous Cellular Networks Based on Flow‐Level Models

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