On the Distribution of Traffic Volumes in the Internet and its Implication

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that the log-normal distribution is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate a second heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which are a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate the utility of the log-normal distribution in two contexts: predicting the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show the log-normal distribution is a better predictor than Gaussian or Weibull distributions

On the Distribution of Traffic Volumes in the Internet and its Implications

In this edition of the Voice, the College’s Career Planning Placement Service offers a variety or workshops include one on life planning. Wooster Chief of Security and Dr. Startzman of the campus wellness center, speak to students on the topic of rape and safety at the College. The Wooster Board of Trustees begins the process to select a new president of the College of Wooster. The Art Center offers classes on quilting, plants, printmaking, drawing, and other artistic mediums, to students for eight weeks. Additionally, an article discusses the, then up and coming, Bicentennial of the United States.https://openworks.wooster.edu/voice1971-1980/1131/thumbnail.jp

Constraints and entropy in a model of network evolution

Barab´asi-Albert’s ‘Scale Free’ model is the starting point for much of the accepted theory of the evolution of real world communication networks. Careful comparison of the theory with a wide range of real world networks, however, indicates that the model is in some cases, only a rough approximation to the dynamical evolution of real networks. In particular, the exponent γ of the power law distribution of degree is predicted by the model to be exactly 3, whereas in a number of real world networks it has values between 1.2 and 2.9. In addition, the degree distributions of real networks exhibit cut offs at high node degree, which indicates the existence of maximal node degrees for these networks. In this paper we propose a simple extension to the ‘Scale Free’ model, which offers better agreement with the experimental data. This improvement is satisfying, but the model still does not explain why the attachment probabilities should favor high degree nodes, or indeed how constraints arrive in non-physical networks. Using recent advances in the analysis of the entropy of graphs at the node level we propose a first principles derivation for the ‘Scale Free’ and ‘constraints’ model from thermodynamic principles, and demonstrate that both preferential attachment and constraints could arise as a natural consequence of the second law of thermodynamics

Liquid data networking

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Addressing the clinical unmet needs in primary Sjögren's Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

For many decades, the clinical unmet needs of primary Sjögren's Syndrome (pSS) have been left unresolved due to the rareness of the disease and the complexity of the underlying pathogenic mechanisms, including the pSS-associated lymphomagenesis process. Here, we present the HarmonicSS cloud-computing exemplar which offers beyond the state-of-the-art data analytics services to address the pSS clinical unmet needs, including the development of lymphoma classification models and the identification of biomarkers for lymphomagenesis. The users of the platform have been able to successfully interlink, curate, and harmonize 21 regional, national, and international European cohorts of 7,551 pSS patients with respect to the ethical and legal issues for data sharing. Federated AI algorithms were trained across the harmonized databases, with reduced execution time complexity, yielding robust lymphoma classification models with 85% accuracy, 81.25% sensitivity, 85.4% specificity along with 5 biomarkers for lymphoma development. To our knowledge, this is the first GDPR compliant platform that provides federated AI services to address the pSS clinical unmet needs. © 2022 The Author(s

Filling the gaps of unused capacity through a fountain coded dissemination of information

Lowest Cost Denominator Networking (LCDnet) envisions "breaking the mould of thinking that law of economics should govern connectivity to all". It brings together a multi-layer resource pooling of Internet technologies at several levels to support benevolence in the Internet. One of the proposed levels of resource pooling involves better network and storage utilisation, as promised by Information-centric networking architectures. In this paper we present a transport and resource management approach on top of an information-centric network that enables efficient, multi-source and multi-path information dissemination as well as in-network caching and mobility support, characteristics that are well desired in the LCDnet context

Internet Traffic Volumes Are Not Gaussian - They Are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction

Getting good statistical models of traffic on network links is a well-known, often-studied problem. A lot of attention has been given to correlation patterns and flow duration. The distribution of the amount of traffic per unit time is an equally important but less studied problem. We study a large number of traffic traces from many different networks including academic, commercial and residential networks using state-of-the-art statistical techniques. We show that traffic obeys the log-normal distribution which is a better fit than the Gaussian distribution commonly claimed in the literature. We also investigate an alternative heavy-tailed distribution (the Weibull) and show that its performance is better than Gaussian but worse than log-normal. We examine anomalous traces which exhibit a poor fit for all distributions tried and show that this is often due to traffic outages or links that hit maximum capacity. We demonstrate that the data we look at is stationary if we consider samples of 15- minute long or even 1-hour long. This gives confidence that we can use the distributions for estimation and modelling purposes. We demonstrate the utility of our findings in two contexts: predicting that the proportion of time traffic will exceed a given level (for service level agreement or link capacity estimation) and predicting 95th percentile pricing. We also show that the log-normal distribution is a better predictor than Gaussian or Weibull distributions in both contexts

ASSOCIATION BETWEEN COPAYMENT, MEDICATION ADHERENCE AND OUTCOMES IN THE MANAGEMENT OF PATIENTS WITH DIABETES AND HEART FAILURE

Objective: To determine the association between copayment, medication adherence and outcomes in patients with Heart failure (HF) and Diabetes Mellitus (DM). Methods: PubMed, Scopus and Cochrane databases were searched using combinations of four sets of key words for: drug cost sharing; resource use, health and economic outcomes; medication adherence; and chronic disease. Results: Thirty eight studies were included in the review. Concerning the direct effect of copayment changes on outcomes, the scarcity and diversity of data, does not allow us to reach a clear conclusion, although there is some evidence indicating that higher copayments may result in poorer health and economic outcomes. Seven and one studies evaluating the relationship between copayment and medication adherence in DM and HF population, respectively, demonstrated an inverse statistically significant association. All studies (29) examining the relationship between medication adherence and outcomes, revealed that increased adherence is associated with health benefits in both DM and HF patients. Finally, the majority of studies in both populations, showed that medication adherence was related to lower resource utilization which in turn may lead to lower total healthcare cost. Conclusion: The results of our systematic review imply that lower copayments may result in higher medication adherence, which in turn may lead to better health outcomes and lower total healthcare expenses. Future studies are recommended to reinforce these findings. (C) 2017 Elsevier B.V. All rights reserved