Impact of the Correlation between Flow Rates and Durations on the Large-Scale Properties of Aggregate Network Traffic

Since the discovery of long-range dependence in network traffic in 1993, many models have appeared to reproduce this property, based on heavy-tailed distributions of some flow-scale properties of the traffic. However, none of these models consider the correlation existing between flow rates and flow durations. In this work, we extend previously proposed models to include this correlation. Based on a planar Poisson process setting, which describes the flow-scale traffic structure, we analytically compute the auto-covariance function of the aggregate traffic's bandwidth and show that it exhibits long-range dependence with a different Hurst parameter. In uncorrelated case, the model that we propose is consistent with existing models, and predict the same Hurst parameter. We also prove that pseudo long-range dependence with a different index can arise from highly variable flow rates. The pertinence of our model choices is validated on real web traffic traces

Investigating self-similarity and heavy tailed distributions on a large scale experimental facility

After seminal work by Taqqu et al. relating self-similarity to heavy tail distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like WEB file sizes and flow lengths, were heavy tailed. However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated either using numerical or network simulations, or from uncontrolled web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarii, prescribing specific heavy-tail distributions, and estimating confidence intervals. In the present work, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, to investigate the prediction observability on real networks. To this end we organize a large number of controlled traffic circulation sessions on a nation-wide real network involving two hundred independent hosts. We use a FPGA-based measurement system, to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow size distributions, independently. Comparison of these two estimated parameters, enables us to discuss the practical applicability conditions of the theoretical prediction

Investigating self-similarity and heavy-tailed distributions on a large scale experimental facility

International audienceAfter the seminal work by Taqqu et al. relating selfsimilarity to heavy-tailed distributions, a number of research articles verified that aggregated Internet traffic time series show self-similarity and that Internet attributes, like Web file sizes and flow lengths, were heavy-tailed. However, the validation of the theoretical prediction relating self-similarity and heavy tails remains unsatisfactorily addressed, being investigated either using numerical or network simulations, or from uncontrolled Web traffic data. Notably, this prediction has never been conclusively verified on real networks using controlled and stationary scenarii, prescribing specific heavy-tailed distributions, and estimating confidence intervals. With this goal in mind, we use the potential and facilities offered by the large-scale, deeply reconfigurable and fully controllable experimental Grid5000 instrument, to investigate the prediction observability on real networks. To this end we organize a large number of controlled traffic circulation sessions on a nation-wide real network involving two hundred independent hosts. We use a FPGA-based measurement system, to collect the corresponding traffic at packet level. We then estimate both the self-similarity exponent of the aggregated time series and the heavy-tail index of flow size distributions, independently. On the one hand, our results complement and validate with a striking accuracy some conclusions drawn from a series of pioneer studies. On the other hand, they bring in new insights on the controversial role of certain components of real networks

Modeling TCP Throughput: an Elaborated Large-Deviations-Based Model and its Empirical Validation *

Abstract In today's Internet, a large part of the traffic is carried using the TCP transport protocol. Characterization of the variations of TCP traffic is thus a major challenge, both for resource provisioning and Quality of Service purposes. However, most existing models are limited to the prediction of the (almost-sure) mean TCP throughput and are unable to characterize deviations from this value. In this paper, we propose a method to describe the deviations of a long TCP flow's throughput from its almost-sure mean value. This method relies on an ergodic large-deviations result, which was recently proved to hold on almost every single realization for a large class of stochastic processes. Applying this result to a Markov chain modeling the congestion window's evolution of a long-lived TCP flow, we show that it is practically possible to quantify and to statistically bound the throughput's variations at different scales of interest for applications. Our Markov-chain model can take into account various network conditions and we demonstrate the accuracy of our method's prediction in different situations using simulations, experiments and real-world Internet traffic. In particular, in the classical case of Bernoulli losses, we demonstrate: i) the consistency of our method with the widely-used square-root formula predicting the almost-sure mean throughput, and ii) its ability to additionally predict finer properties reflecting the traffic variability at different scales

CTLA-4 +49A/G and CT60 gene polymorphisms in primary Sjögren syndrome

CTLA-4 encodes cytotoxic T lymphocyte-associated antigen-4, a cell-surface molecule providing a negative signal for T-cell activation. CTLA-4 gene polymorphisms have been widely studied in connection with genetic susceptibility to various autoimmune diseases, but studies have led to contradictory results in different populations. This case-control study sought to investigate whether CTLA-4 CT60 and/or +49A/G polymorphisms were involved in the genetic predisposition to primary Sjögren syndrome (pSS). We analysed CTLA-4 CT60 and +49A/G polymorphisms in a first cohort of 142 patients with pSS (cohort 1) and 241 controls, all of Caucasian origin. A replication study was performed on a second cohort of 139 patients with pSS (cohort 2). In cohort 1, the CTLA-4 +49A/G*A allele was found on 73% of chromosomes in patients with pSS, compared with 66% in controls (p = 0.036; odds ratio (OR) 1.41, 95% confidence interval (CI) 1.02 to 1.95). No difference in CTLA-4 CT60 allelic or genotypic distribution was observed between patients (n = 142) and controls (n = 241). In the replication cohort, the CTLA-4 +49A/G*A allele was found on 62% of chromosomes in patients with pSS, compared with 66% in controls (p = 0.30; OR 0.85, 95% CI 0.63 to 1.16). Thus, the CTLA-4 +49A/G*A allele excess among patients from cohort 1 was counterbalanced by its under-representation in cohort 2. When the results from the patients in both cohorts were pooled (n = 281), there was no difference in CTLA-4 +49A/G allelic or genotypic distribution in comparison with controls. Our results demonstrate a lack of association between CTLA-4 CT60 or +49A/G polymorphisms and pSS. Premature conclusions might have been made if a replication study had not been performed. These results illustrate the importance of case-control studies performed on a large number of patients. In fact, sampling bias may account for some contradictory results previously reported for CTLA-4 association studies in autoimmune diseases

Occupational exposure to pesticides and central nervous system tumors: results from the CERENAT case-control study

BACKGROUND: The etiology of the central nervous system (CNS) tumors remains largely unknown. The role of pesticide exposure has been suggested by several epidemiological studies, but with no definitive conclusion. OBJECTIVE: To analyze associations between occupational pesticide exposure and primary CNS tumors in adults in the CERENAT study. METHODS: CERENAT is a multicenter case-control study conducted in France in 2004-2006. Data about occupational pesticide uses-in and outside agriculture-were collected during detailed face-to-face interviews and reviewed by experts for consistency and exposure assignment. Odds ratios (ORs) and 95% confidence intervals (95% CI) were estimated with conditional logistic regression. RESULTS: A total of 596 cases (273 gliomas, 218 meningiomas, 105 others) and 1 192 age- and sex-matched controls selected in the general population were analyzed. Direct and indirect exposures to pesticides in agriculture were respectively assigned to 125 (7.0%) and 629 (35.2%) individuals and exposure outside agriculture to 146 (8.2%) individuals. For overall agricultural exposure, we observed no increase in risk for all brain tumors (OR 1.04, 0.69-1.57) and a slight increase for gliomas (OR 1.37, 0.79-2.39). Risks for gliomas were higher when considering agricultural exposure for more than 10 years (OR 2.22, 0.94-5.24) and significantly trebled in open field agriculture (OR 3.58, 1.20-10.70). Increases in risk were also observed in non-agricultural exposures, especially in green space workers who were directly exposed (OR 1.89, 0.82-4.39), and these were statistically significant for those exposed for over 10 years (OR 2.84, 1.15-6.99). DISCUSSION: These data support some previous findings regarding the potential role of occupational exposures to pesticides in CNS tumors, both inside and outside agriculture

Age-adjusted recipient pretransplantation telomere length and treatment-related mortality after hematopoietic stem cell transplantation

Telomere attrition induces cell senescence and apoptosis. We hypothesized that age-adjusted pretransplantation telomere length might predict treatment-related mortality (TRM) after hematopoietic stem cell transplantation (HSCT). Between 2000 and 2005, 178 consecutive patients underwent HSCT from HLA-identical sibling donors after myeloablative conditioning regimens, mainly for hematologic malignancies (n = 153). Blood lymphocytes' telomere length was measured by real-time quantitative PCR before HSCT. Age-adjusted pretransplantation telomere lengths were analyzed for correlation with clinical outcomes. After age adjustment, patients' telomere-length distribution was similar among all 4 quartiles except for disease stage. There was no correlation between telomere length and engraftment, GVHD, or relapse. The overall survival was 62% at 5 years (95% confidence interval [CI], 54-70). After a median follow-up of 51 months (range, 1-121 months), 43 patients died because of TRM. The TRM rate inversely correlated with telomere length. TRM in patients in the first (lowest telomere length) quartile was significantly higher than in patients with longer telomeres (P = .017). In multivariate analysis, recipients' age (hazard ratio, 1.1; 95% CI, .0-1.1; P = .0001) and age-adjusted telomere length (hazard ratio, 0.4; 95% CI; 0.2-0.8; P = .01) were independently associated with TRM. In conclusion, age-adjusted recipients' telomere length is an independent biologic marker of TRM after HSCT. (Blood. 2012;120(16):3353-3359)National Institutes of Health Intramural Research Program, National Heart, Lung, and Blood InstituteAA and Myelodysplastic Syndrome International FoundationFrance HPNFAPES

Phenotype Frequencies of Autosomal Minor Histocompatibility Antigens Display Significant Differences among Populations

Minor histocompatibility (H) antigens are allogeneic target molecules having significant roles in alloimmune responses after human leukocyte antigen–matched solid organ and stem cell transplantation (SCT). Minor H antigens are instrumental in the processes of transplant rejection, graft-versus-host disease, and in the curative graft-versus-tumor effect of SCT. The latter characteristic enabled the current application of selected minor H antigens in clinical immunotherapeutic SCT protocols. No information exists on the global phenotypic distribution of the currently identified minor H antigens. Therefore, an estimation of their overall impact in human leukocyte antigen–matched solid organ and SCT in the major ethnic populations is still lacking. For the first time, a worldwide phenotype frequency analysis of ten autosomal minor H antigens was executed by 31 laboratories and comprised 2,685 randomly selected individuals from six major ethnic populations. Significant differences in minor H antigen frequencies were observed between the ethnic populations, some of which appeared to be geographically correlated