7,704 research outputs found
On Factors Affecting the Usage and Adoption of a Nation-wide TV Streaming Service
Using nine months of access logs comprising 1.9 Billion sessions to BBC
iPlayer, we survey the UK ISP ecosystem to understand the factors affecting
adoption and usage of a high bandwidth TV streaming application across
different providers. We find evidence that connection speeds are important and
that external events can have a huge impact for live TV usage. Then, through a
temporal analysis of the access logs, we demonstrate that data usage caps
imposed by mobile ISPs significantly affect usage patterns, and look for
solutions. We show that product bundle discounts with a related fixed-line ISP,
a strategy already employed by some mobile providers, can better support user
needs and capture a bigger share of accesses. We observe that users regularly
split their sessions between mobile and fixed-line connections, suggesting a
straightforward strategy for offloading by speculatively pre-fetching content
from a fixed-line ISP before access on mobile devices.Comment: In Proceedings of IEEE INFOCOM 201
Characterizing and Improving the Reliability of Broadband Internet Access
In this paper, we empirically demonstrate the growing importance of
reliability by measuring its effect on user behavior. We present an approach
for broadband reliability characterization using data collected by many
emerging national initiatives to study broadband and apply it to the data
gathered by the Federal Communications Commission's Measuring Broadband America
project. Motivated by our findings, we present the design, implementation, and
evaluation of a practical approach for improving the reliability of broadband
Internet access with multihoming.Comment: 15 pages, 14 figures, 6 table
Big Data Meets Telcos: A Proactive Caching Perspective
Mobile cellular networks are becoming increasingly complex to manage while
classical deployment/optimization techniques and current solutions (i.e., cell
densification, acquiring more spectrum, etc.) are cost-ineffective and thus
seen as stopgaps. This calls for development of novel approaches that leverage
recent advances in storage/memory, context-awareness, edge/cloud computing, and
falls into framework of big data. However, the big data by itself is yet
another complex phenomena to handle and comes with its notorious 4V: velocity,
voracity, volume and variety. In this work, we address these issues in
optimization of 5G wireless networks via the notion of proactive caching at the
base stations. In particular, we investigate the gains of proactive caching in
terms of backhaul offloadings and request satisfactions, while tackling the
large-amount of available data for content popularity estimation. In order to
estimate the content popularity, we first collect users' mobile traffic data
from a Turkish telecom operator from several base stations in hours of time
interval. Then, an analysis is carried out locally on a big data platform and
the gains of proactive caching at the base stations are investigated via
numerical simulations. It turns out that several gains are possible depending
on the level of available information and storage size. For instance, with 10%
of content ratings and 15.4 Gbyte of storage size (87% of total catalog size),
proactive caching achieves 100% of request satisfaction and offloads 98% of the
backhaul when considering 16 base stations.Comment: 8 pages, 5 figure
Insights from Analysis of Video Streaming Data to Improve Resource Management
Today a large portion of Internet traffic is video. Over The Top (OTT)
service providers offer video streaming services by creating a large
distributed cloud network on top of a physical infrastructure owned by multiple
entities. Our study explores insights from video streaming activity by
analyzing data collected from Korea's largest OTT service provider. Our
analysis of nationwide data shows interesting characteristics of video
streaming such as correlation between user profile information (e.g., age, sex)
and viewing habits, viewing habits of users (when do the users watch? using
which devices?), viewing patterns (early leaving viewer vs. steady viewer),
etc. Video on Demand (VoD) streaming involves costly (and often limited)
compute, storage, and network resources. Findings from our study will be
beneficial for OTTs, Content Delivery Networks (CDNs), Internet Service
Providers (ISPs), and Carrier Network Operators, to improve their resource
allocation and management techniques.Comment: This is a preprint electronic version of the article accepted to IEEE
CloudNet 201
Modeling of Packet Streaming Services in Information Communication Networks
Application of the term video streaming in contemporary usage denotes compression techniques and
data buffering, which can transmit video in real time over the network. There is currently a rapid growth
and development of technologies using wireless broadband technology as a transport, which is a seri-
ous alternative to cellular communication systems. Adverse effect of the aggressive environment used
in wireless networks transmission results in data packets undergoing serious distortions and often get-
ting lost in transit. All existing research in this area investigate the known types of errors separately. At
present there are no standard approaches to determining the effect of errors on transmission quality of
services. Besides, the spate in popularity of multimedia applications has led to the need for optimization
of bandwidth allocation and usage in telecommunication networks. Modern telecommunication networks
should by their definition be able to maintain the quality of different applications with different Quality
of Service (QoS) levels. QoS requirements are generally dependent on the parameters of network and
application layers of the OSI model. At the application layer QoS depends on factors such as resolution,
bit rate, frame rate, video type, audio codecs, and so on. At the network layer, distortions (such as delay,
jitter, packet loss, etc.) are introduced
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
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.Comment: To appear at IMC 201
Not all Apps are created equal: analysis of spatiotemporal heterogeneity in nationwide mobile service usage
Proceeding of: 13th International Conference on emerging Networking EXperiments and Technologies (CoNEXT '17)We investigate how individual mobile services are consumed at a national scale, by studying data collected in a 3G/4G mobile network deployed over a major European country. Through correlation and clustering analyses, our study unveils a strong heterogeneity in the demand for different mobile services, both in time and space. In particular, we show that: (i) somehow surprisingly, almost all considered services exhibit quite different temporal usage patterns; (ii) in contrast to such temporal behavior, spatial patterns are fairly uniform across all services; (iii) when looking at usage patterns at different locations, the average traffic volume per user is dependent on the urbanization level, yet its temporal dynamics are not. Our findings do not only have sociological implications, but are also relevant to the orchestration of network resources.This research work has been performed in the framework of the H2020-ICT-2014-2 project 5G NORMA (Grant Agreement No. 671584)
Signalling Storms in 3G Mobile Networks
We review the characteristics of signalling storms that have been caused by
certain common apps and recently observed in cellular networks, leading to
system outages. We then develop a mathematical model of a mobile user's
signalling behaviour which focuses on the potential of causing such storms, and
represent it by a large Markov chain. The analysis of this model allows us to
determine the key parameters of mobile user device behaviour that can lead to
signalling storms. We then identify the parameter values that will lead to
worst case load for the network itself in the presence of such storms. This
leads to explicit results regarding the manner in which individual mobile
behaviour can cause overload conditions on the network and its signalling
servers, and provides insight into how this may be avoided.Comment: IEEE ICC 2014 - Communications and Information Systems Security
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