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Monitoring Platform Evolution towards Serverless Computing for 5G and Beyond Systems
Get PDFFifth generation (5G) and beyond systems require flexible and efficient monitoring platforms to guarantee optimal key performance indicators (KPIs) in various scenarios. Their applicability in Edge computing environments requires lightweight monitoring solutions. This work evaluates different candidate technologies to implement a monitoring platform for 5G and beyond systems in these environments. For monitoring data plane technologies, we evaluate different virtualization technologies, including bare metal servers, virtual machines, and orchestrated containers. We show that containers not only offer superior flexibility and deployment agility, but also allow obtaining better throughput and latency. In addition, we explore the suitability of the Function-as-a-Service (FaaS) serverless paradigm for deploying the functions used to manage the monitoring platform. This is motivated by the event oriented nature of those functions, designed to set up the monitoring infrastructure for newly created services. When the FaaS warm start mode is used, the platform gives users the perception of resources that are always available. When a cold start mode is used, containers running the application"s modules are automatically destroyed when the application is not in use. Our analysis compares both of them with the standard deployment of microservices. The experimental results show that the cold start mode produces a significant latency increase, along with potential instabilities. For this reason, its usage is not recommended despite the potential savings of computing resources. Conversely, when the warm start mode is used for executing configuration tasks ofmonitoring infrastructure, it can provide similar execution times to a microservice-based deployment. In addition, the FaaS approach significantly simplifies the code logic in comparison with microservices, reducing lines of code to less than 38%, thus reducing development time. Thus, FaaS in warm start mode represents the best candidate technology to implements such management functions.TRUEpu
Scalable Outlier Detection Methods for Functional Data
Get PDFRecent technological advances have led to an exponential growth in the volume of data generated. The quest to make sense of these data, some of which are usually complex, has led to recent interest in development of statistical methods for analysing data with complex structures. One such field of interest is functional data analysis (FDA), which deals with the analysis of data that can be considered as functions, curves, or surfaces observed over a domain set. Outlier detection is a challenging but important part of the exploratory analysis process in FDA because functional observations can exhibit outlyingness in various ways compared to the bulk of the data. This thesis addresses the problem of detecting and classifying outliers in functional data with three main contributions.
First, the fdaoutlier R package is presented in Chapter 2. The package contains implementations of some of the state-of-the-art functional outlier detection methods in the literature. Some of the methods implemented include directional outlyingness, magnitude-shape plot, sequential transformations, total variation depth, and modified shape similarity index. Detailed illustrations of the functions of the package are provided, using various simulated and real functional datasets curated from the functional outlier detection literature. Overviews of the functional outlier detection methods implemented in the package are also presented in Chapter 2. This chapter therefore, serves as a review of some of the current literature in outlier detection for functional data.
Next, two new methods, named Semifast-MUOD and Fast-MUOD, are presented in Chapter 3. These methods work by computing for each curve three indices (magnitude, amplitude and shape index) that measure the outlyingness of that curve in terms of its magnitude, amplitude and shape. Semifast-MUOD computes these indices with respect to (w.r.t.) a random sample of the dataset, while Fast-MUOD computes these indices w.r.t. to the point-wise or L1 median. The classical boxplot is then used as a cutoff on the three indices to identify curves that are outliers of different types. A by-product of the methods is an unsupervised classification of the outliers into different types, without the need for visualisation. Performance evaluation of the methods, using various real and simulated datasets, shows that Fast-MUOD is the better of the two new proposed methods for outlier detection, in addition to being very scalable. Comparisons with latest functional outlier detection methods in the literature also show superior or comparable outlier detection performance.
In Chapter 4, some theoretical properties of the Fast-MUOD indices are presented. These include some definitions of the indices, as well as convergence proofs of the sample approximations. Some properties of the indices under simple transformations are also presented in this chapter. Finally, three techniques are presented in Chapter 5 for extending the Fast-MUOD indices to outlier detection in multivariate functional data observed on the same domain. These techniques include the use of random projections and identifying outliers on the marginal components of the multivariate functional data. The use of random projections showed the best result in performance evaluations with various real and simulated datasets.
Chapter 6 contains some concluding remarks and possible future research work.MathematicsUniversidad Carlos III de Madrid, Spai
Factors affecting congestion-aware routing in complex networks
Get PDFAn important issue in complex networks is to find efficient strategies for information delivery between a given sending node and its destination host. Whereas there is no doubt that the use of the congestion provides significant advantages for routing, it has been observed that there are some factors that greatly influence the behavior of the routing protocols that use it. In this paper we study the effect of two factors that greatly influence the behavior of congestion-aware routing protocols: updating all the paths at the same time and using information from a subset of the nodes to compute the paths. On one hand, we give explanations to these behaviors and, on the other hand, we quantitatively evaluate the effect of these factors on the performance of the routing protocols.Spanish State Research Agency - Spanish Ministry of Science and InnovationTRUEpu
Multipath TCP in smartphones equipped with millimeter wave radios
Get PDFThe well-known susceptibility of millimeter wave links to human blockage and client mobility has recently motivated researchers to propose approaches that leverage both 802.11ad radios (operating in the 60 GHz band) and legacy 802.11ac radios (operating in the 5 GHz band) in dual-band commercial off-the-shelf devices to simultaneously provide Gbps throughput and reliability. One such approach is via Multipath TCP (MPTCP), a transport layer protocol that is transparent to applications and requires no changes to the underlying wireless drivers. However, MPTCP (as well as other bundling approaches) have only been evaluated to date in 60 GHz WLANs with laptop clients.
In this work, we port for first time the MPTCP source code to a dual-band smartphone equipped with an 802.11ad and an 802.11ac radio. We discuss the challenges we face and the system-level optimizations required to enable the phone to support Gbps data rates and yield optimal MPTCP throughput (i.e., the sum of the individual throughputs of the two radios) under ideal conditions. We also evaluate for first time the power consumption of MPTCP in a dual-band 802.11ad/ac smartphone and provide recommendations towards the design of an energy-aware MPTCP scheduler. We make our source code publicly available to enable other researchers to experiment with MPTCP in smartphones equipped with millimeter wave radios.TRUEpu
Using Survey Data to Estimate the Impact of the Omicron Variant on Vaccine Efficacy against COVID-19 Infection
Get PDFData collected in the Global COVID-19 Trends and Impact Surveys
(UMD Global CTIS), and data on variants sequencing from GISAID,
are used to evaluate the impact of the Omicron variant (in South
Africa and other countries) on the prevalence of COVID-19 among
unvaccinated and vaccinated population, in general and discrim-
inating by the number of doses. In South Africa, we observe that
the prevalence of COVID-19 in December (with strong presence
of Omicron) among the unvaccinated population is comparable to
the prevalence during the previous wave (in August-September), in
which Delta was the variant with the largest presence. However,
among vaccinated, the prevalence of COVID-19 in December is
much higher than in the previous wave. In fact, a significant re-
duction of the vaccine efficacy is observed from August-September
to December. For instance, the efficacy drops from 0.81 to 0.30 for
those vaccinated with 2 doses, and from 0.51 to 0.09 for those vacci-
nated with one dose. The study is then extended to other countries
in which Omicron has been detected, comparing the situation in
October (before Omicron) with that of December. While the re-
duction measured is smaller than in South Africa, we still found,
for instance, an average drop in vaccine efficacy from 0.53 to 0.45
among those vaccinated with two doses. Moreover, we found a
significant negative (Pearson) correlation of around −0.6 between
the measured prevalence of Omicron and the vaccine efficacy.TRUEpu
Experimenting with Localization Management Functions in 5G Core Networks
Get PDFLocalization has achieved great attention in 5G networks, pushed by standardization. However, experimentation in 5G networks lacks the integration of network function modules designed for localization. We present our implementation of the 5G Localization Management Function. It complies with the 3GPP standard and OpenAirInterface, the most advanced framework that implements a full 5G-New Radio stack. We show that we are able to extend the functionality of OpenAirInterface, enabling location services. Finally, we demonstrate that the tool’s performance satisfies the 5G Key Performance Indicators required by 3GPP for localization.TRUEpu
Detecting State Transitions of a Markov Source: Sampling Frequency and Age Trade-off
Get PDFWe consider a finite-state Discrete-Time Markov Chain
(DTMC) source that can be sampled for detecting the events when
the DTMC transits to a new state. Our goal is to study the trade-off
between sampling frequency and staleness in detecting the events. We
argue that, for the problem at hand, using Age of Information (AoI) for
quantifying the staleness of a sample is conservative and therefore, study
another freshness metric age penalty, which is defined as the time elapsed
since the first transition out of the most recently observed state. We
study two optimization problems: minimize average age penalty subject
to an average sampling frequency constraint, and minimize average
sampling frequency subject to an average age penalty constraint; both
are Constrained Markov Decision Problems. We solve them using the
Lagrangian MDP approach, where we also provide structural results
that reduce the search space. Our numerical results demonstrate that the
computed Markov policies not only outperform optimal periodic sampling
policies, but also achieve sampling frequencies close to or lower than that
of an optimal clairvoyant (non-causal) sampling policy, if a small age
penalty is allowed.TRUEpu
An Analysis of Android Malware Classification Services
Get PDFThe increasing number of Android malware forced antivirus (AV) companies to rely on automated classification techniques to determine the family and class of suspicious samples. The research community relies heavily on such labels to carry out prevalence studies of the threat ecosystem and to build datasets that are used to validate and benchmark novel detection and classification methods. In this work, we carry out an extensive study of the Android malware ecosystem by surveying whitepapers and reports from 6 keyplayers in the industry as well as 81 papers from 8 top security conferences to understand how malware datasets are used by both. We, then, explore the limitations associated with the use of available malware classification services, namely VirusTotal (VT) engines, for determining the family of an Android sample. Using a dataset of 2.47M Android malware samples, we find that the detection coverage of VT's AVs is generally very low, that the percentage of samples flagged by any 2 AV engines does not go beyond 52%, and that common families between any pair of AV engines is at best 29%. We rely on clustering to determine the extent to which different AV engine pairs agree upon which samples belong to the same family (regardless of the actual family name), and find that there are discrepancies that can introduce noise in automatic label unification schemes. We also observe the usage of generic labels and inconsistencies within the labels of top AV engines, suggesting that their efforts are directed towards accurate detection rather than classification. Our results contribute to a better understanding of the limitations of using Android malware family labels as supplied by common AV engines.TRUEpu
Fast and Efficient Online Selection of Sensors for Transmitter Localization
Get PDFThe increase in cost and usage of RF spectrum has made it increasingly necessary to monitor its usage and protect it from unauthorized use.
A number of prior studies have designed algorithms to localize unauthorized transmitters using crowdsourced sensors.
To reduce the cost of crowdsourcing, these studies select the most relevant sensors a priori to localize such transmitters.
In this work, we instead argue for online selection to localize such transmitters.
Online selection can lead to more accurate localization using limited number of sensors, as compared to selecting sensors a priori, albeit at the cost of higher latency. To account for the trade-off between accuracy and latency, we add a constraint on the number of selection rounds.
For the case where the number of rounds is equal to the number of selected sensors, we propose a heuristic based on Thompson Sampling and show using trace-driven simulation that it provides 23\% better accuracy compared to a number of proposed baseline algorithms.
For restricted number of rounds, we show that using conventional parallel version of the modified Thompson Sampling which selects equal number of sensors in each round results in a substantial reduction in accuracy. To this end, we propose a strategy of selecting decreasing number of sensors in subsequent rounds of the modified Parallel Thompson Sampling.
Our evaluation shows that the proposed heuristic leads to only 3 % reduction in accuracy in contrast to 22 % using modified Parallel Thompson Sampling, when we select 50 sensors in 20 rounds.TRUEpu
Global Mobile Network Aggregators: Taxonomy, Roaming Performance and Optimization
Get PDFA new model of global virtual Mobile Network Operator (MNO) – the Mobile Network Aggregator (MNA) – has recently been gaining significant traction.MNAs provide mobile communications services to their customers by leveraging multiple MNOs, and connecting through the one that best match their customers’ needs at any point in time (and space). MNAs naturally provide optimized global coverage by connecting through local MNOs across the different geographic regions they provide service. In this paper,we dissect the operations of three MNAs, namely, Google Fi, Twilio and Truphone. We perform measurements using the three selected MNAs to assess their performance for three major applications, namely, DNS, web
browsing and video streaming. We benchmark their performance comparing it to the one of a traditional MNO. We find that even MNAs provide some delay penalty compared to the service accessed through the local MNOs in the geographic area where the user is roaming, they can significantly improve performance compared to traditional roaming model of the MNOs (e.g. home routed roaming). Finally, in order to fully quantify the potential benefits that can be realized using the MNA model, we perform a set of emulations by deploying both control and user plane functions of open-source 5G implementations in different ocations of AWS, and measure the potential gains.TRUEpu