Measurements and analysis of a major adult video portal

Today the Internet is a large multimedia delivery infrastructure, with websites such as YouTube appearing at the top of most measurement studies. However, most traffic studies have ignored an important domain: adult multimedia distribution. Whereas, traditionally, such services were provided primarily via bespoke websites, recently these have converged towards what is known as "Porn 2.0". These services allow users to upload, view, rate and comment on videos for free (much like YouTube). Despite their scale, we still lack even a basic understanding of their operation This paper addresses this gap by performing a large-scale study of one of the most popular Porn 2.0 websites: YouPorn. Our measurements reveal a global delivery infrastructure that we have repeatedly crawled to collect statistics (on 183k videos). We use this data to characterise the corpus, as well as to inspect popularity trends and and how they relate to other features, e.g., categories and ratings. To explore our discoveries further, we use a small-scale user study, highlighting key system implications

Daleel:simplifying cloud instance selection using machine learning

Decision making in cloud environments is quite challenging due to the diversity in service offerings and pricing models, especially considering that the cloud market is an incredibly fast moving one. In addition, there are no hard and fast rules; each customer has a specific set of constraints (e.g. budget) and application requirements (e.g. minimum computational resources). Machine learning can help address some of the complicated decisions by carrying out customer-specific analytics to determine the most suitable instance type(s) and the most opportune time for starting or migrating instances. We employ machine learning techniques to develop an adaptive deployment policy, providing an optimal match between the customer demands and the available cloud service offerings. We provide an experimental study based on extensive set of job executions over a major public cloud infrastructure

The measurement and modeling of a P2P streaming video service

Most of the work on grid technology in the video area has been generally restricted to aspects of resource scheduling and replica management. The traffic of such a service has a lot of characteristics in common with that of the traditional video service. However the architecture and user behavior in grid networks are quite different from those of the traditional Internet. Considering the potential of grid networks and video sharing services, measuring and analyzing P2P IPTV traffic are important and fundamental works in the field of grid networks. This paper investigates the features of PPLive, the most popular streaming service in China and based on P2P technology. Through monitoring and analyzing PPLive traffic streams, the characteristics of P2P streaming services have been studied. The analyses are carried out in respect of bearing protocols, geographical distribution and the self-similarity properties of the traffic. A streaming service traffic model has been created and verified with the simulation. The simulation results indicate that the proposed streaming service traffic model complies well with the real IPTV streaming service. It can also function as a step towards studying video-sharing services on grids

It bends but would it break?:topological analysis of BGP infrastructures in Europe

The Internet is often thought to be a model of resilience, due to a decentralised, organically-grown architecture. This paper puts this perception into perspective through the results of a security analysis of the Border Gateway Protocol (BGP) routing infrastructure. BGP is a fundamental Internet protocol and its intrinsic fragilities have been highlighted extensively in the literature. A seldom studied aspect is how robust the BGP infrastructure actually is as a result of nearly three decades of perpetual growth. Although global black-outs seem unlikely, local security events raise growing concerns on the robustness of the backbone. In order to better protect this critical infrastructure, it is crucial to understand its topology in the context of the weaknesses of BGP and to identify possible security scenarios. Firstly, we establish a comprehensive threat model that classifies main attack vectors, including but non limited to BGP vulnerabilities. We then construct maps of the European BGP backbone based on publicly available routing data. We analyse the topology of the backbone and establish several disruption scenarios that highlight the possible consequences of different types of attacks, for different attack capabilities. We also discuss existing mitigation and recovery strategies, and we propose improvements to enhance the robustness and resilience of the backbone. To our knowledge, this study is the first to combine a comprehensive threat analysis of BGP infrastructures withadvanced network topology considerations. We find that the BGP infrastructure is at higher risk than already understood, due to topologies that remain vulnerable to certain targeted attacks as a result of organic deployment over the years. Significant parts of the system are still uncharted territory, which warrants further investigation in this direction

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

WiFi ad-hoc message propagation over GPRS networks

Mobile ad hoc networks (MANETs) are networks that can be created spontaneously with no prior set up. Arbitrary nodes can form such networks by intercommunicating directly in a peer-to-peer fashion using wireless communication technologies, such as IEEE 802.11 (or WiFi). The number of WiFi-enabled handheld devices is escalating as the services they offer attract an increasing number of customers. These devices have the capability of creating MANETs regardless of any infrastructure. Such networks can be used to circulate data across the network at high rates. However, MANETs are networks of very special nature. They feature delays of unexpected lengths, high retransmission rates (due to radio interference, obstacles, and packet collisions), and unpredictable node relocation. For these reasons, MANETs tend to have unstable topologies. In this project, we present a solution for exchanging messages in MANETs formed by WiFi-enabled handheld devices. We implement the proposed solution using the network simulator ns2, and investigate its behaviour and performance under different network conditions

Diversity of Grid Traffic: A Survey-based Study

Grid computing offers the prospect of harnessing huge amounts of computational resources. However, it is being argued that such potential cannot be fully exploited due to the nature of the Internet architecture which is not suitable for high-speed communication of large volumes of data. This has motivated EC-GIN, a European project which aims to exploit the network in a way that better suits the needs of Grid applications. In order to reach such a goal, the network requirements of Grid applications first need to be understood. We have conducted a survey to investigate the requirements and characteristics of a number of Grid applications used in scientific research. Among other things, the survey results have revealed the diversity of Grid traffic, suggesting that there is more to Grid traffic than just transfers of huge bulks and tiny control signals. In this paper, we present these results and identify different classes of traffic behaviour that have been observed within the results. We then validate our findings by looking in detail at two of the applications that we have surveyed

A Survey-based Study of Grid Traffic

Grid computing offers the prospect of harnessing huge amounts of computational resources. However, it is being argued that such potential cannot be fully exploited due to the nature of the Internet architecture which is not suitable for high-speed communication of large volumes of data. This has motivated EC-GIN, a European project which aims to exploit the network in a way that better suits the needs of Grid applications. In order to reach such a goal, the network requirements of Grid applications first need to be understood. We have conducted a survey to investigate the requirements and characteristics of a number of Grid applications used in scientific research. Among other things, the survey results have revealed the diversity of Grid traffic, suggesting that there is more to Grid traffic than just transfers of huge bulks and tiny control signals. In this paper, we present these results and identify different classes of traffic behavior that have been observed within the results. We then validate our findings by looking in detail at two of the applications that we have surveyed

Monitoring, analysing and predicting network performance in grids

The grid computing paradigm has facilitated the instrumentation of complex, highly-demanding collaborative applications. The technologies that make grid computing possible have mostly evolved from parallel and cluster systems. Although this has certainly empowered the grid computing field, part of the heritage has been the perception that required network resources are taken for granted. This is precarious, considering that most grids rely on public IP networks, like the Internet, as the underlying network. This assumption has obstructed the path of grid computing. This thesis aims to improve the performance of grid applications by facilitating network-aware grid scheduling. This is achieved by providing network performance information to grid schedulers, allowing them to adapt to changes in the network. The contribution of this thesis is twofold: a novel approach to network measurement that is particularly suitable for grid environments; and a distributed system that collects and manages these measurements, predicts future network performance, and disseminates this information to schedulers. The accuracy and effectiveness of this system is evaluated on a production grid infrastructure used for e-science applications. The outcomes of this evaluation provide a strong argument for the introduction of network-aware grid schedulers, information systems, and job and resource description standards