From BGP to RTT and Beyond: Matching BGP Routing Changes and Network Delay Variations with an Eye on Traceroute Paths

Many organizations have the mission of assessing the quality of broadband access services offered by Internet Service Providers (ISPs). They deploy network probes that periodically perform network measures towards selected Internet services. By analyzing the data collected by the probes it is often possible to gain a reasonable estimate of the bandwidth made available by the ISP. However, it is much more difficult to use such data to explain who is responsible of the fluctuations of other network qualities. This is especially true for latency, that is fundamental for several nowadays network services. On the other hand, there are many publicly accessible BGP routers that collect the history of routing changes and that are good candidates to be used for understanding if latency fluctuations depend on interdomain routing. In this paper we provide a methodology that, given a probe that is located inside the network of an ISP and that executes latency measures and given a set of publicly accessible BGP routers located inside the same ISP, decides which routers are best candidates (if any) for studying the relationship between variations of network performance recorded by the probe and interdomain routing changes. We validate the methodology with experimental studies based on data gathered by the RIPE NCC, an organization that is well-known to be independent and that publishes both BGP data within the Routing Information Service (RIS) and probe measurement data within the Atlas project

Intra-Domain Pathlet Routing

Internal routing inside an ISP network is the foundation for lots of services that generate revenue from the ISP's customers. A fine-grained control of paths taken by network traffic once it enters the ISP's network is therefore a crucial means to achieve a top-quality offer and, equally important, to enforce SLAs. Many widespread network technologies and approaches (most notably, MPLS) offer limited (e.g., with RSVP-TE), tricky (e.g., with OSPF metrics), or no control on internal routing paths. On the other hand, recent advances in the research community are a good starting point to address this shortcoming, but miss elements that would enable their applicability in an ISP's network. We extend pathlet routing by introducing a new control plane for internal routing that has the following qualities: it is designed to operate in the internal network of an ISP; it enables fine-grained management of network paths with suitable configuration primitives; it is scalable because routing changes are only propagated to the network portion that is affected by the changes; it supports independent configuration of specific network portions without the need to know the configuration of the whole network; it is robust thanks to the adoption of multipath routing; it supports the enforcement of QoS levels; it is independent of the specific data plane used in the ISP's network; it can be incrementally deployed and it can nicely coexist with other control planes. Besides formally introducing the algorithms and messages of our control plane, we propose an experimental validation in the simulation framework OMNeT++ that we use to assess the effectiveness and scalability of our approach.Comment: 13 figures, 1 tabl

Mining Network Events using Traceroute Empathy

In the never-ending quest for tools that enable an ISP to smooth troubleshooting and improve awareness of network behavior, very much effort has been devoted in the collection of data by active and passive measurement at the data plane and at the control plane level. Exploitation of collected data has been mostly focused on anomaly detection and on root-cause analysis. Our objective is somewhat in the middle. We consider traceroutes collected by a network of probes and aim at introducing a practically applicable methodology to quickly spot measurements that are related to high-impact events happened in the network. Such filtering process eases further in- depth human-based analysis, for example with visual tools which are effective only when handling a limited amount of data. We introduce the empathy relation between traceroutes as the cornerstone of our formal characterization of the traceroutes related to a network event. Based on this model, we describe an algorithm that finds traceroutes related to high-impact events in an arbitrary set of measurements. Evidence of the effectiveness of our approach is given by experimental results produced on real-world data.Comment: 8 pages, 7 figures, extended version of Discovering High-Impact Routing Events using Traceroutes, in Proc. 20th International Symposium on Computers and Communications (ISCC 2015

A framework for multi‐provider virtual private networks in software‐defined federated networks

Federated networks represent a remunerable operational way, allowing federated partners to increase their incomes through a sharing resource process. They have been primarily used in the context of cloud computing; nowadays, they are also used to provide connectivity services, like virtual private networks. However, providing such a service by using standard technologies in federated networks requires a nonnegligible effort from different points of view (e.g., configuration effort). In this paper, we propose an software‐defined network (SDN)–based framework aiming at overcoming limitations in currently adopted best practices to issue virtual private networks in federated networks. Relying on the SDN architecture, we propose a method allowing federated providers to quickly and easily create federated networks, reducing unneeded costs (e.g., new hardware), and a way for customers to fast‐access federated services, without any explicit actions from providers. We evaluate our framework by using SDNetkit. We focus on analyzing the impact of our implementation on both control and data plane, in terms of number of control messages exchanged in the network and size of the forwarding tables, respectively.BMBF, 01IS18025A, Verbundprojekt BIFOLD-BBDC: Berlin Institute for the Foundations of Learning and DataBMBF, 01IS18037A, Verbundprojekt BIFOLD-BZML: Berlin Institute for the Foundations of Learning and DataTU Berlin, Open-Access-Mittel – 202

Risk perception and affective state on work exhaustion in obstetrics during the COVID-19 pandemic

Amulticenter cross-sectional survey study involving four Italian University Hospitals was performed to test the hypothesis that negative affect and positive affect (affective dimensions) mediate the association between risk perception (perceived risk of infection and death; cognitive dimensions) and the feeling of work exhaustion (WE) among obstetrics healthcare providers (HCPs) during the Coronavirus Disease 2019 (COVID-19) pandemic. Totally, 570 obstetrics HCPs were invited to complete the 104-item IPSICO survey in May 2020. A theoretical model built on the tested hypothesis was investigated by structural equation modelling. The model explained 32.2% of the WE variance. Only negative affect mediated the association between cognitive dimensions and WE and also the association between WE and psychological well-being before the pandemic, experiences of stressful events, female gender, and dysfunctional coping. Non-mediated associations withWE were observed for work perceived as a duty, experience of stressful events, support received by colleagues, and the shift strategy. Only previous psychological well-being, support by colleagues, and shift strategies were inversely associated with WE. Based on study results, monitoring negative than positive affect appears superior in predicting WE, with practical implications for planning psychological interventions in HCPs at the individual, interpersonal, and organizational levels

Biopsychosocial model of resilience in young adults with multiple sclerosis (BPS-ARMS): an observational study protocol exploring psychological reactions early after diagnosis

INTRODUCTION: Multiple sclerosis (MS), the most common neurological disease causing disability in young adults, is widely recognised as a major stress factor. Studies have shown that the first years after the diagnosis are distressing in terms of adjustment to the disease and that MS negatively affects patients' psychological well-being, quality of life (QoL) and social functioning. However, the links between disease-specific variables at diagnosis, resilience and psychological adjustment of patients with MS remain largely unexplored, especially in adolescents and young adults. This observational study aims to fill the gap of knowledge on biopsychosocial characteristics and resilience of young adults with MS to evaluate the relationship among these variables and to develop a biopsychosocial model of resilience. METHODS AND ANALYSIS: Biological and clinical characteristics of young adults newly diagnosed with MS will be investigated by collecting clinical information, performing neurological examinations, MRI and analysing cerebrospinal fluid and blood biomarkers (eg, measures of inflammation), body composition, gut microbiota and movement/perceptual markers. Psychosocial characteristics (eg, psychological distress, coping strategies), QoL, psychological well-being and resilience will be assessed by self-report questionnaires. Comparative statistics (ie, analysis of variance or unpaired samples t-test, correlation and regression analyses) will be applied to evaluate the relationship among biological, psychological and social factors. The results are expected to allow a comprehensive understanding of the determinants of resilience in young patients with MS and to inform resilience interventions, tailored to young patients' specific needs, aiming to reduce the risk of maladaptive reactions to the disease and to improve psychological well-being and QoL. ETHICS AND DISSEMINATION: The study has been approved by the Verona University Hospital Ethics Committee (approval number: 2029CESC). The findings will be disseminated through scientific publications in peer-reviewed journals, conference presentations, social media and specific websites. TRIAL REGISTRATION NUMBER: ClinicalTrials.gov (NCT03825055)