The recent popularity growth of Deep Learning (DL) re-ignited the interest towards traffic classification, with several studies demonstrating the accuracy of DL-based classifiers to identify Internet applications' traffic. Even with the aid of hardware accelerators (GPUs, TPUs), DL model training remains expensive, and limits the ability to operate frequent model updates necessary to fit to the ever evolving nature of Internet traffic, and mobile traffic in particular. To address this pain point, in this work we explore Incremental Learning (IL) techniques to add new classes to models without a full retraining, hence speeding up model's updates cycle. We consider iCarl, a state of the art IL method, and MIRAGE-2019, a public dataset with traffic from 40 Android apps, aiming to understand "if there is a case for incremental learning in traffic classification". By dissecting iCarl internals, we discuss ways to improve its design, contributing a revised version, namely iCarl+. Despite our analysis reveals their infancy, IL techniques are a promising research area on the roadmap towards automated DL-based traffic analysis systems.
Accurate mobile traffic forecast is important for efficient network planning and operations. However, existing traffic forecasting models have high complexity, making the forecasting process slow and costly. In this paper, we analyze some characteristics of mobile traffic such as periodicity, spatial similarity and short term relativity. Based on these characteristics, we propose a \emph{Block Regression} ({BR}) model for mobile traffic forecasting. This model employs seasonal differentiation so as to take into account of the temporally repetitive nature of mobile traffic. One of the key featu…
It is crucial for the service provider to comprehend and forecast mobile traffic in large-scale cellular networks in order to govern and manage mechanisms for base station placement, load balancing, and network planning. The purpose of this article is to extract and simulate traffic patterns from more than 14,000 cells that have been installed in different metropolitan areas. To do this, we create, implement, and assess a method in which cells are first categorized by their point of interest and then clustered based on the temporal distribution of cells in each region. The proposed model has b…
Large-scale mobile traffic analytics is becoming essential to digital infrastructure provisioning, public transportation, events planning, and other domains. Monitoring city-wide mobile traffic is however a complex and costly process that relies on dedicated probes. Some of these probes have limited precision or coverage, others gather tens of gigabytes of logs daily, which independently offer limited insights. Extracting fine-grained patterns involves expensive spatial aggregation of measurements, storage, and post-processing. In this paper, we propose a mobile traffic super-resolution techni…
University of Edinburgh（爱丁堡大学）；CNR-IEIIT（意大利国家研究委员会电子、信息与电信工程研究所）
Network slicing is increasingly used to partition network infrastructure between different mobile services. Precise service-wise mobile traffic forecasting becomes essential in this context, as mobile operators seek to pre-allocate resources to each slice in advance, to meet the distinct requirements of individual services. This paper attacks the problem of multi-service mobile traffic forecasting using a sequence-to-sequence (S2S) learning paradigm and convolutional long short-term memories (ConvLSTMs). The proposed architecture is designed so as to effectively extract complex spatiotemporal …
Liquid Neural Network-based Adaptive Learning vs. Incremental Learning for Link Load Prediction amid Concept Drift due to Network Failures
University of Applied Sciences of Southern Switzerland（瑞士南应用科学大学）；Wrocław University of Science and Technology（弗罗茨瓦夫理工大学）；Chalmers University of Technology（查尔姆斯理工大学）；Politecnico di Torino（都灵理工大学）
With the explosive growth in demand for mobile traffic, one of the promising solutions is to offload cellular traffic to small base stations for better system efficiency. Due to increasing system complexity, network operators are facing severe challenges and looking for machine learning-based solutions. In this work, we propose an energy-aware mobile traffic offloading scheme in the heterogeneous network jointly apply deep Q network (DQN) decision making and advanced traffic demand forecasting. The base station control model is trained and verified on an open dataset from a major telecom opera…
Critical Path Analysis (CPA) studies the delivery of webpages to identify page resources, their interrelations, as well as their impact on the page loading latency. Despite CPA being a generic methodology, its mechanisms have been applied only to browsers and web traffic, but those do not directly apply to study generic mobile apps. Likewise, web browsing represents only a small fraction of the overall mobile traffic. In this paper, we take a first step towards filling this gap by exploring how CPA can be performed for generic mobile applications. We propose Mobile Critical Path Analysis (MCPA…
Explosive demand for wireless internet services has posed critical challenges for wireless network due to its limited capacity. To tackle this hurdle, wireless Internet service providers (WISPs) take the smart data pricing to manage data traffic loads. Meanwhile, from the users' perspective, it is also reasonable and desired to employ mobile data traffic management under the pricing policies of WISPs to improve the economic efficiency of data consumption. In this paper we introduce a concept of cost efficiency for user's mobile data management, defined as the ratio of user's mobile data consum…
Forecasting with high accuracy the volume of data traffic that mobile users will consume is becoming increasingly important for precision traffic engineering, demand-aware network resource allocation, as well as public transportation. Measurements collection in dense urban deployments is however complex and expensive, and the post-processing required to make predictions is highly non-trivial, given the intricate spatio-temporal variability of mobile traffic due to user mobility. To overcome these challenges, in this paper we harness the exceptional feature extraction abilities of deep learning…
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 patt…
University of Science and Technology Beijing（北京科技大学）；COMSATS University Islamabad（伊斯兰堡COMSATS大学）；University of Engineering and Technology Lahore（拉合尔工程技术大学）
The internet activity records (IARs) of a mobile cellular network posses significant information which can be exploited to identify the network's efficacy and the mobile users' behavior. In this work, we extract useful information from the IAR data and identify a healthy predictability of spatio-temporal pattern within the network traffic. The information extracted is helpful for network operators to plan effective network configuration and perform management and optimization of network's resources. We report experimentation on spatiotemporal analysis of IAR data of the Telecom Italia. Based o…
In 5G research, it is traditionally assumed that vertical industries (a.k.a verticals) set the performance requirements for the services they want to offer to mobile users, and the mobile operators alone are in charge of orchestrating their resources so as to meet such requirements. Motivated by the observation that successful orchestration requires reliable traffic predictions, in this paper we investigate the effects of having the verticals, instead of the mobile operators, performing such predictions. Leveraging a real-world, large-scale, crowd-sourced trace, we find that involving the vert…
Mobile messaging services have gained a large share in global telecommunications. Unlike conventional services like phone calls, text messages or email, they do not feature a standardized environment enabling a federated and potentially local service architecture. We present an extensive and large-scale analysis of communication patterns for four popular mobile messaging services between 28 countries and analyze the locality of communication and the resulting impact on user privacy. We show that server architectures for mobile messaging services are highly centralized in single countries. This…
New Mexico Tech（新墨西哥理工学院）；Amir Kabir University of Technology（阿米尔·卡比尔理工大学）；Utah Valley university（犹他谷大学）
Temporary high traffic requests in cellular networks is a challenging problem to address. Recent advances in Unmanned Aerial Vehicles applied to cover these types of traffics. UAV -Mounted Mobile Base Stations placement is a challenging problem to achieve high performance. Different approaches have been proposed; however, user required traffic is not considered in UAV placement. We propose a new feature to apply to K-means clustering to find the optimum clusters. User required traffic is defined as a new feature to assign users to the UAVs. Our simulation results show that UAVs could be placed…
Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic
Department of Electronic Engineering, BNRist, Tsinghua University（电子工程系，北京理工大学，清华大学）；State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications（网络与交换技术国家重点实验室，北京邮电大学）；China Mobile Communications Group Guangxi Co., Ltd.（中国移动通信集团广西有限公司）
Over the recent years a considerable amount of effort has been devoted towards the performance evaluation and prediction of Mobile Networks. Performance modeling and evaluation of mobile networks are very important in view of their ever expending usage and the multiplicity of their component parts together with the complexity of their functioning. The present paper addresses current issues in traffic management and congestion control by (signal to interference plus noise ratio) SINR prediction congestion control, routing and optimization of cellular mobile networks.
Cache-equipped Base-Stations (CBSs) is an attractive alternative to offload the rapidly growing backhaul traffic in a mobile network. New 5G technology and dense femtocell enable one user to connect to multiple base-stations simultaneously. Practical implementation requires the caches in BSs to be regarded as a cache server, but few of the existing works considered how to offload traffic, or how to schedule HTTP requests to CBSs. In this work, we propose a DNS-based HTTP traffic allocation framework. It schedules user traffic among multiple CBSs by DNS resolution, with the consideration of loa…
Many mobile ad hoc network protocols use simple flooding, in order to adapt to changes in time varying network topology. Most of the times, a network-wide flood results in redundant packets and increases network congestion, probability of packet collision, low utilization of available bandwidth, and most important, higher power consumption. In this paper, we propose a new cross-layer broadcast scheme to minimize broadcast traffic in mobile ad hoc networks. Our scheme is based on use of received signal strength indicator, RSSI, value to reduce the number of broadcast packets. The effectiveness …
One of the core technologies being standardized by 3GPP for LTE-A is the introduction of Relay Nodes (RNs). RNs are intended for ensuring coverage at cell edges as well as for the provision of enhanced capacity at hot spot areas. An extension to this concept is the Mobile Relay (MR). MRs can be mounted on vehicles and the original idea is to serve users inside high speed trains thus counter fighting the inherent severe fading and vehicle penetration loss. In this work we present a framework for exploiting Mobile Relay (MRs) even at low speeds in urban environments for bypassing traffic from ne…
Multi-access edge computing (MEC) enables placing video content at the edge of the network aiming to improve the quality of experience (QoE) of the mobile clients. Video content caching at edge servers also reduces traffic in the backhaul of the mobile network, hence reducing operational costs for mobile network operators (MNOs). However, minimizing the rate of cache misses and maximizing the average video quality may sometimes be at odds with each other, particularly when the cache size is constrained. Our objective in this article is two fold: First, we explore the impact of fixed video cont…
Vehicular traffic is a foremost problem in modern cities. Huge amount of time and resources are wasted while traveling due to traffic congestion. With the introduction of sophisticated traffic management systems, such as those incorporating dynamic traffic assignments, more stringent demands are being placed upon the available real time traffic data. In this paper we have proposed mobile agent as a mechanism to handle the traffic problem on road. Mobile software agents can be used to provide the better QoS (Quality of Service) in vehicular ad hoc network to improve the safety application and d…
Mobile network traffic prediction is an important input in to network capacity planning and optimization. Existing approaches may lack the speed and computational complexity to account for bursting, non-linear patterns or other important correlations in time series mobile network data. We compare the performance of two deep learning architectures - Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) - for predicting mobile Internet traffic using two months of Telecom Italia data for the metropolitan area of Milan. K-Means clustering was used a priori to group cells based on Internet a…
The concept of mobility prediction represents one of the key enablers for an efficient management of future cellular networks, which tend to be progressively more elaborate and dense due to the aggregation of multiple technologies. In this letter we aim to investigate the problem of cellular traffic prediction over a metropolitan area and propose a deep regression (DR) approach to model its complex spatio-temporal dynamics. DR is instrumental in capturing multi-scale and multi-domain dependences of mobile data by solving an image-to-image regression problem. A parametric relationship between i…
Scalable and comprehensive analysis of rapidly evolving mobile device application traffic is extremely important but a challenging problem for the Deep Packet Inspection (DPI) engines to perform effective policy management. We present a test framework in which a test driver can automate/orchestrate traffic generation by invoking appropriate method (intent) of real mobile applications (as opposed to traffic replay) in regression or functional testing of mobile application traffic analysis engines in a virtualized environment, without real hardware. We demonstrate the concept by automating a rea…
The usage of the mobile app is unassailable in this digital era. While tons of data are generated daily, user privacy security concerns become an important issue. Nowadays, tons of techniques, such as machine learning and deep learning traffic classifiers, have been applied to analyze users app traffic. These techniques allow the monitor to get the fingerprints of using apps while the user traffic is still encrypted, which raises a severe privacy issue. In order to fight against this type of data analysis, people have been researching obfuscation algorithms to confuse feature-based machine lea…
The volume and types of traffic data in mobile cellular networks have been increasing continuously. Meanwhile, traffic data change dynamically in several dimensions such as time and space. Thus, traffic modeling is essential for theoretical analysis and energy efficient design of future ultra-dense cellular networks. In this paper, the authors try to build a tractable and accurate model to describe the traffic variation pattern for a single base station in real cellular networks. Firstly a sinusoid superposition model is proposed for describing the temporal traffic variation of multiple base s…
Smartphones and other mobile devices are today pervasive across the globe. As an interesting side effect of the surge in mobile communications, mobile network operators can now easily collect a wealth of high-resolution data on the habits of large user populations. The information extracted from mobile network traffic data is very relevant in the context of population mapping: it provides a tool for the automatic and live estimation of population densities, overcoming the limitations of traditional data sources such as censuses and surveys. In this paper, we propose a new approach to infer pop…
The first decade of the 21st century has seen tremendous improvements in mobile internet and its technologies. The high traffic volume of services such as video conference and other real-time traffic applications are imposing a great challenge on networks. In the meantime, demand for the use of mobile devices in computation and communication such as smart phones, personal digital assistants, and mobile-enabled laptops has grown rapidly. These services have driven the demand for increasing and guaranteing bandwidth requirements in the network. A direction of this paper is in the case of resourc…
Today in fast technology development in wireless mobile adhoc network there is vast scope for research. As it is known that wireless communication for mobile network has many application areas like routing services, security services etc. The mobile adhoc network is the wireless network for communication in which the mobile nodes are organized without any centralized administrator. There are many Manet routing protocols like reactive, proactive, hybrid etc. In this paper the reactive Manet routing protocol like DSR is simulated for traffic analysis for 50 mobile nodes for IP traffic flows. Als…
Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results cons…
Data traffic over cellular networks is exhibiting an ongoing exponential growth, increasing by an order of magnitude every year and has already surpassed voice traffic. This increase in data traffic demand has led to a need for solutions to enhance capacity provision, whereby traffic offloading to Wi-Fi is one means that can enhance realised capacity. Though offloading to Wi-Fi networks has matured over the years, a number of challenges are still being faced by operators to its realization. In this article, we carry out a survey of the practical challenges faced by operators in data traffic of…
In this paper, we first carry out to our knowledge the first in-depth characterization of control-plane traffic, using a real-world control-plane trace for 37,325 UEs sampled at a real-world LTE Mobile Core Network (MCN). Our analysis shows that control events exhibit significant diversity in device types and time-of-day among UEs. Second, we study whether traditional probability distributions that have been widely adopted for modeling Internet traffic can model the control-plane traffic originated from individual UEs. Our analysis shows that the inter-arrival time of the control events as wel…
Nowadays mobile communication is growing fast in the 5G communication industry. With the increasing capacity requirements and requirements for quality of experience, mobility prediction has been widely applied to mobile communication and has becoming one of the key enablers that utilizes historical traffic information to predict future locations of traffic users, Since accurate mobility prediction can help enable efficient radio resource management, assist route planning, guide vehicle dispatching, or mitigate traffic congestion. However, mobility prediction is a challenging problem due to the…
We present procedures that exploit mobility prediction and prefetching to enhance offloading of traffic from mobile networks to WiFi hotspots, for both delay tolerant and delay sensitive traffic. We evaluate the procedures in terms of the percentage of offloaded traffic, the data transfer delay, and the cache size used for prefetching. The evaluation considers empirical measurements and shows how various parameters influence the performance of the procedures, and their robustness to time and throughput estimation errors.
We present a detailed evaluation of procedures that exploit mobility prediction and prefetching to enhance offloading of traffic from mobile networks to WiFi hotspots, for both delay tolerant and delay sensitive traffic. We consider empirical measurements and evaluate the percentage of offloaded traffic, data transfer delay, and energy consumption of the proposed procedures. Our results illustrate how various factors such as mobile, WiFi and hotspot backhaul throughput, data size, number of hotspots, along with time and throughput estimation errors, influence the performance and energy efficie…
Renewable energy harvesting (EH) technology is expected to be pervasively utilized in the next generation (5G) mobile networks to support sustainable network developments and operations. However, the renewable energy supply is inherently random and intermittent, which could lead to energy outage, energy overflow, quality of service (QoS) degradation, etc. Accordingly, how to enhance renewable energy sustainability is a critical issue for green networking. To this end, an energy-sustainable traffic steering framework is proposed in this article, where the traffic load is dynamically adjusted to…
Institute for Informatics and Telematics, National Research Council（国家研究委员会信息与远程信息处理研究所）
The widespread diffusion of mobile phones is triggering an exponential growth of mobile data traffic that is likely to cause, in the near future, considerable traffic overload issues even in last-generation cellular networks. Offloading part of the traffic to other networks is considered a very promising approach and, in particular, in this paper, we consider offloading through opportunistic networks of users' devices. However, the performance of this solution strongly depends on the pattern of encounters between mobile nodes, which should therefore be taken into account when designing offload…
Multi-Scale Diffusion Transformer for Jointly Simulating User Mobility and Mobile Traffic Pattern
Department of Electronic Engineering, Beijing National Research Center for Information Science and Technology (BNRist), Tsinghua University（电子工程系，信息科学与技术国家研究中心（BNRist），清华大学）；International School, Beijing University of Posts and Telecommunications（国际学院，北京邮电大学）
Many network services and tools (e.g. network monitors, malware-detection systems, routing and billing policy enforcement modules in ISPs) depend on identifying the type of traffic that passes through the network. With the widespread use of mobile devices, the vast diversity of mobile apps, and the massive adoption of encryption protocols (such as TLS), large-scale encrypted traffic classification becomes increasingly difficult. In this paper, we propose a deep learning model for mobile app identification that works even with encrypted traffic. The proposed model only needs the payload of the …
An All-Optical Metro Network Architecture and QoS-Aware Wavelength Allocation Study for Converged Fixed, Mobile, and Edge Computing Multi-Granular Traffic
Security in mobile AD HOC network is a big challenge as it has no centralized authority which can supervise the individual nodes operating in the network. The attacks can come from both inside the network and from the outside. We are trying to classify the existing attacks into two broad categories: DATA traffic attacks and CONTROL traffic attacks. We will also be discussing the presently proposed methods of mitigating those attacks.
In this paper, we design a greedy routing on networks of mobile agents. In the greedy routing algorithm, every time step a packet in agent $i$ is delivered to the agent $j$ whose distance from the destination is shortest among searched neighbors of agent $i$. Based on the greedy routing, we study the traffic dynamics and traffic-driven epidemic spreading on networks of mobile agents. We find that the transportation capacity of networks and the epidemic threshold increase as the communication radius increases. For moderate moving speed, the transportation capacity of networks is the highest and…
Network traffic classification, a task to classify network traffic and identify its type, is the most fundamental step to improve network services and manage modern networks. Classical machine learning and deep learning method have developed well in the field of network traffic classification. However, there are still two major challenges. One is how to protect the privacy of users' traffic data, and the other is that it is difficult to obtain labeled data in reality. In this paper, we propose a novel approach using federated semi-supervised learning for network traffic classification. In our …
It is well known that apps running on mobile devices extensively track and leak users' personally identifiable information (PII); however, these users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties. In this paper, we present the design, implementation, and evaluation of ReCon: a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. ReCon leverages machine learning to rev…
Systems of networked mobile robots, such as unmanned aerial or ground vehicles, will play important roles in future military and commercial applications. The communications for such systems will typically be over wireless links and may require that the robots form an ad hoc network and communicate on a peer-to-peer basis. In this paper, we consider the problem of optimizing the network topology to minimize the total traffic in a network required to support a given set of data flows under constraints on the amount of movement possible at each mobile robot. In this paper, we consider a subclass …
This paper investigates the use of WiFi and mobile device-to-device networks, with vehicular ad hoc networks being a typical example, as a complementary means to offload and reduce the traffic load of cellular networks. A novel cooperative content dissemination strategy is proposed for a heterogeneous network consisting of different types of devices with different levels of mobility, ranging from static WiFi access points to mobile devices such as vehicles. The proposed strategy offloads a significant proportion of data traffic from cellular networks to WiFi or device-to-device networks. Detai…
