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A Year in Lockdown: How the Waves of COVID-19 Impact Internet Traffic
Get PDFIn March 2020, the World Health Organization declared the Corona Virus 2019 (COVID-19) outbreak a global pandemic. As a result, billions of people were either encouraged or forced by their governments to stay home to reduce the spread of the virus. This caused many to turn to the Internet for work, education, social interaction, and entertainment. With the Internet demand rising at an unprecedented rate, the question of whether the Internet could sustain this additional load emerged. To answer this question, this paper will review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance. In order to keep our study broad, we collect and analyze Internet traffic data from multiple locations at the core and edge of the Internet. From this, we characterize how traffic and application demands change, to describe the "new normal," and explain how the Internet reacted during these unprecedented times.pu
TTrees: Automated Classification of Causes of Network Anomalies with Little Data
Get PDFLeveraging machine learning (ML) for the detection of network problems dates back to handling call-dropping issues in telephony. However, troubleshooting cellular networks is still a manual task, assigned to experts who monitor the network around the clock. We present here TTrees (from Troubleshooting Trees), a practical and interpretable ML software tool that implements a methodology we have designed to automate the identification of the causes of performance anomalies in a cellular network. This methodology is unsupervised and combines multiple ML algorithms (e.g., decision trees and clustering). TTrees requires small volumes of data and is quick at training.
Our experiments using real data from operational commercial mobile networks show that TTrees can automatically identify and accurately classify network anomalies—e.g., cases for which a network low performance is not apparently justified by operational conditions—training with just a few hundreds of data samples, hence enabling precise troubleshooting actions.TRUEpu
Estimating Active Cases of COVID-19
Get PDFHaving accurate and timely data on confirmed active COVID-19 cases is challenging, since it depends on
testing capacity and the availability of an appropriate infrastructure to perform tests and aggregate their
results. In this paper, we propose methods to estimate the number of active cases of COVID-19 from the
official data (of confirmed cases and fatalities) and from survey data. We show that the latter is a viable option in countries with reduced testing capacity or suboptimal infrastructures.Regional Government of MadridTRUEpu
Energy-Optimal Sampling of Edge-Based Feedback Systems
Get PDFWe study a problem of optimizing the sampling
interval in an edge-based feedback system, where sensor samples
are offloaded to a back-end server which process them and
generates a feedback that is fed-back to a user. Sampling the
system at maximum frequency results in the detection of events
of interest with minimum delay but incurs higher energy costs
due to the communication and processing of some redundant
samples. On the other hand, lower sampling frequency results in
a higher delay in detecting an event of interest thus increasing
the idle energy usage and degrading the quality of experience.
We propose a method to quantify this trade-off and compute the
optimal sampling interval, and use simulation to demonstrate the
energy savings.TRUEpu
SpectraGAN: spectrum based generation of city scale spatiotemporal mobile network traffic data
Get PDFCity-scale spatiotemporal mobile network traffic data can support numerous applications in and beyond networking. However, operators are very reluctant to share their data, which is curbing innovation and research reproducibility. To remedy this status quo, we propose SpectraGAN, a novel deep generative model that, upon training with real-world network traffic measurements, can produce high-fidelity synthetic mobile traffic data for new, arbitrary sized geographical regions over long periods. To this end, the model only requires publicly available context information about the target region, such as population census data. SpectraGAN is an original conditional GAN design with the defining feature of generating spectra of mobile traffic at all locations of the target region based on their contextual features. Evaluations with mobile traffic measurement datasets collected by different operators in 13 cities across two European countries demonstrate that SpectraGAN can synthesize more dependable traffic than a range of representative baselines from the literature. We also show that synthetic data generated with SpectraGAN yield similar results to that with real data when used in applications like radio access network infrastructure power savings and resource allocation, or dynamic population mapping.TRUEpu
Location-Based Analytics in 5G and Beyond
Get PDFLocation-based analytics leverage accurate location awareness enabled by the fifth generation (5G) mobile technology standard, as well as the integration of heterogeneous technologies, to empower a plethora of new services for 5G verticals and optimize the use of network resources. This article proposes an end-to-end architecture integrated in the 5G network infrastructure to provide location-based analytics as a service. Based on this architecture, we present an overview of cutting-edge applications in 5G and beyond, focusing on people-centric and network-centric location-based analytics.European UnionTRUEpu
On the Efficiency of Service and Data Handoff Protocols in Edge Computing Systems
Get PDFThe Multi-access Edge Computing (MEC) enables a new layer of edge middleboxes, acting as local proxies with virtualized resources deployed at edge localities. To support scalable, low-latency, and locally managed service provisioning, MEC relies on computation offloading, the process that outsources computing tasks from resourced constrained mobile devices and moves it to edge data centers. In this paper, we tackle a specific sub-problem within the umbrella of computation offloading. We argue that it is convenient to migrate a service because of the lack of computing resources in the anchor edge data center even if a device, such as industrial IoT devices, is not moving. In this paper, we extensively evaluate the efficiency of data and service handoff protocols. Specifically, we thoroughly assess protocols, that we designed in our past work, in a well-known edge computing emulator, i.e., openLEON. These protocols migrate data and service either in a reactive fashion, i.e., upon realizing of resource exhaustion, or proactively, i.e., beforehand to swiftly minimize the downtime. We experimentally verify their performance for a typical MEC use case, i.e., video. Our results show that by being proactive, the service interruption downtime reduces by a factor of 4 times.Ministerio de Ciencia e InnovaciónTRUEpu
Towards a traffic map of the Internet: Connecting the dots between popular services and users
Get PDFThe impact of Internet phenomena depends on how they im-
pact users, but researchers lack visibility into how to translate
Internet events into their impact. Distressingly, the research
community seems to have lost hope of obtaining this infor-
mation without relying on privileged viewpoints. We argue
for optimism thanks to new network measurement methods
and changes in Internet structure which make it possible to
construct an “Internet traffic map”. This map would identify
the locations of users and major services, the paths between
them, and the relative activity levels routed along these paths.
We sketch our vision for the map, detail new measurement
ideas for map construction, and identify key challenges that
the research community should tackle. The realization of
an Internet traffic ma p wi ll be an In ternet-scale research
effort with Internet-scale impacts that reach far beyond the
research community, and so we hope our fellow researchers
are excited to join us in addressing this challenge.TRUEpu
