Enterprise networks (modern techniques for analysis, measurement and performance improvement)

Dans l'évaluation d'Internet au cours des années, un grand nombre d'applications apparaissent, avec différentes exigences de service en termes de bande passante, délai et ainsi de suite. Pourtant, le trafic Internet présente encore une propriété de haute variabilité. Plusieurs études révèlent que les flux court sont en général liés à des applications interactives-pour ceux-ci, on s'attend à obtenir de bonne performance que l'utilisateur perçoit, le plus souvent en termes de temps de réponse court. Cependant, le schéma classique FIFO/drop-tail déployé des routeurs/commutateurs d'aujourd'hui est bien connu de parti pris contre les flux courts. Pour résoudre ce problème sur un réseau best-effort, nous avons proposé un nouveau et simple algorithme d'ordonnancement appelé EFD (Early Flow Discard). Dans ce manuscrit, nous avons d'abord évaluer la performance d'EFD dans un réseau câblé avec un seul goulot d'étranglement au moyen d'étendu simulations. Nous discutons aussi des variantes possibles de EFD et les adaptations de EFD à 802.11 WLAN - se réfèrent principalement à EFDACK et PEFD, qui enregistre les volumes échangés dans deux directions ou compte simplement les paquets dans une direction, visant à améliorer l'équité à niveau flot et l'interactivité dans les WLANs. Enfin, nous nous consacrons à profiler le trafic de l'entreprise, en plus de elaborer deux modèles de trafic-l'une qui considère la structure topologique de l'entreprise et l'autre qui intègre l'impact des applications au-dessus de TCP - pour aider à évaluer et à comparer les performances des politiques d'ordonnancement dans les réseaux d'entreprise classiques.As the Internet evolves over the years, a large number of applications emerge with varying service requirements in terms of bandwidth, delay, loss rate and so on. Still, the Internet traffic exhibits a high variability property the majority of the flows are of small sizes while a small percentage of very long flows contribute to a large portion of the traffic volume. Several studies reveal that small flows are in general related to interactive applications for which one expects to obtain good user perceived performance, most often in terms of short response time. However, the classical FIFO/drop-tail scheme deployed in today s routers/switches is well known to bias against short flows over long ones. To tackle this issue over a best-effort network, we have proposed a novel and simple scheduling algorithm named EFD (Early Flow Discard). In this manuscript, we first evaluate the performance of EFD in a single-bottleneck wired network through extensive simulations. We then discuss the possible variants of EFD and EFD s adaptations to 802.11 WLANs mainly refer to EFDACK and PEFD. Finally, we devote ourselves to profiling enterprise traffic, and further devise two workload models - one that takes into account the enterprise topological structure and the other that incorporates the impact of the applications on top of TCP - to help to evaluate and compare the performance of scheduling policies in typical enterprise networks.PARIS-Télécom ParisTech (751132302) / SudocSudocFranceF

Uncovering the immune microenvironment and molecular subtypes of hepatitis B-related liver cirrhosis and developing stable a diagnostic differential model by machine learning and artificial neural networks

Background: Hepatitis B-related liver cirrhosis (HBV-LC) is a common clinical disease that evolves from chronic hepatitis B (CHB). The development of cirrhosis can be suppressed by pharmacological treatment. When CHB progresses to HBV-LC, the patient’s quality of life decreases dramatically and drug therapy is ineffective. Liver transplantation is the most effective treatment, but the lack of donor required for transplantation, the high cost of the procedure and post-transplant rejection make this method unsuitable for most patients.Methods: The aim of this study was to find potential diagnostic biomarkers associated with HBV-LC by bioinformatics analysis and to classify HBV-LC into specific subtypes by consensus clustering. This will provide a new perspective for early diagnosis, clinical treatment and prevention of HCC in HBV-LC patients. Two study-relevant datasets, GSE114783 and GSE84044, were retrieved from the GEO database. We screened HBV-LC for feature genes using differential analysis, weighted gene co-expression network analysis (WGCNA), and three machine learning algorithms including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF) for a total of five methods. After that, we constructed an artificial neural network (ANN) model. A cohort consisting of GSE123932, GSE121248 and GSE119322 was used for external validation. To better predict the risk of HBV-LC development, we also built a nomogram model. And multiple enrichment analyses of genes and samples were performed to understand the biological processes in which they were significantly enriched. And the different subtypes of HBV-LC were analyzed using the Immune infiltration approach.Results: Using the data downloaded from GEO, we developed an ANN model and nomogram based on six feature genes. And consensus clustering of HBV-LC classified them into two subtypes, C1 and C2, and it was hypothesized that patients with subtype C2 might have milder clinical symptoms by immune infiltration analysis.Conclusion: The ANN model and column line graphs constructed with six feature genes showed excellent predictive power, providing a new perspective for early diagnosis and possible treatment of HBV-LC. The delineation of HBV-LC subtypes will facilitate the development of future clinical treatment of HBV-LC

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

BackgroundPancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.MethodsIn this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.ResultsOur analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.ConclusionOur study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease

Enterprise networks : modern techniques for analysis, measurement and performance improvement

Dans l'évaluation d'Internet au cours des années, un grand nombre d'applications apparaissent, avec différentes exigences de service en termes de bande passante, délai et ainsi de suite. Pourtant, le trafic Internet présente encore une propriété de haute variabilité. Plusieurs études révèlent que les flux court sont en général liés à des applications interactives-pour ceux-ci, on s'attend à obtenir de bonne performance que l'utilisateur perçoit, le plus souvent en termes de temps de réponse court. Cependant, le schéma classique FIFO/drop-tail déployé des routeurs/commutateurs d'aujourd'hui est bien connu de parti pris contre les flux courts. Pour résoudre ce problème sur un réseau best-effort, nous avons proposé un nouveau et simple algorithme d'ordonnancement appelé EFD (Early Flow Discard). Dans ce manuscrit, nous avons d'abord évaluer la performance d'EFD dans un réseau câblé avec un seul goulot d'étranglement au moyen d'étendu simulations. Nous discutons aussi des variantes possibles de EFD et les adaptations de EFD à 802.11 WLAN - se réfèrent principalement à EFDACK et PEFD, qui enregistre les volumes échangés dans deux directions ou compte simplement les paquets dans une direction, visant à améliorer l'équité à niveau flot et l'interactivité dans les WLANs. Enfin, nous nous consacrons à profiler le trafic de l'entreprise, en plus de elaborer deux modèles de trafic-l'une qui considère la structure topologique de l'entreprise et l'autre qui intègre l'impact des applications au-dessus de TCP - pour aider à évaluer et à comparer les performances des politiques d'ordonnancement dans les réseaux d'entreprise classiques.As the Internet evolves over the years, a large number of applications emerge with varying service requirements in terms of bandwidth, delay, loss rate and so on. Still, the Internet traffic exhibits a high variability property – the majority of the flows are of small sizes while a small percentage of very long flows contribute to a large portion of the traffic volume. Several studies reveal that small flows are in general related to interactive applications – for which one expects to obtain good user perceived performance, most often in terms of short response time. However, the classical FIFO/drop-tail scheme deployed in today’s routers/switches is well known to bias against short flows over long ones. To tackle this issue over a best-effort network, we have proposed a novel and simple scheduling algorithm named EFD (Early Flow Discard). In this manuscript, we first evaluate the performance of EFD in a single-bottleneck wired network through extensive simulations. We then discuss the possible variants of EFD and EFD’s adaptations to 802.11 WLANs – mainly refer to EFDACK and PEFD. Finally, we devote ourselves to profiling enterprise traffic, and further devise two workload models - one that takes into account the enterprise topological structure and the other that incorporates the impact of the applications on top of TCP - to help to evaluate and compare the performance of scheduling policies in typical enterprise networks

Amélioration des performances des réseaux d'entreprise

As the Internet evolves over the years, a large number of applications emerge with varying service requirements in terms of bandwidth, delay, loss rate and so on. Still, the Internet traffic exhibits a high variability property – the majority of the flows are of small sizes while a small percentage of very long flows contribute to a large portion of the traffic volume. Several studies reveal that small flows are in general related to interactive applications – for which one expects to obtain good user perceived performance, most often in terms of short response time. However, the classical FIFO/drop-tail scheme deployed in today’s routers/switches is well known to bias against short flows over long ones. To tackle this issue over a best-effort network, we have proposed a novel and simple scheduling algorithm named EFD (Early Flow Discard). In this manuscript, we first evaluate the performance of EFD in a single-bottleneck wired network through extensive simulations. We then discuss the possible variants of EFD and EFD’s adaptations to 802.11 WLANs – mainly refer to EFDACK and PEFD. Finally, we devote ourselves to profiling enterprise traffic, and further devise two workload models - one that takes into account the enterprise topological structure and the other that incorporates the impact of the applications on top of TCP - to help to evaluate and compare the performance of scheduling policies in typical enterprise networks.Dans l'évaluation d'Internet au cours des années, un grand nombre d'applications apparaissent, avec différentes exigences de service en termes de bande passante, délai et ainsi de suite. Pourtant, le trafic Internet présente encore une propriété de haute variabilité. Plusieurs études révèlent que les flux court sont en général liés à des applications interactives-pour ceux-ci, on s'attend à obtenir de bonne performance que l'utilisateur perçoit, le plus souvent en termes de temps de réponse court. Cependant, le schéma classique FIFO/drop-tail déployé des routeurs/commutateurs d'aujourd'hui est bien connu de parti pris contre les flux courts. Pour résoudre ce problème sur un réseau best-effort, nous avons proposé un nouveau et simple algorithme d'ordonnancement appelé EFD (Early Flow Discard). Dans ce manuscrit, nous avons d'abord évaluer la performance d'EFD dans un réseau câblé avec un seul goulot d'étranglement au moyen d'étendu simulations. Nous discutons aussi des variantes possibles de EFD et les adaptations de EFD à 802.11 WLAN - se réfèrent principalement à EFDACK et PEFD, qui enregistre les volumes échangés dans deux directions ou compte simplement les paquets dans une direction, visant à améliorer l'équité à niveau flot et l'interactivité dans les WLANs. Enfin, nous nous consacrons à profiler le trafic de l'entreprise, en plus de elaborer deux modèles de trafic-l'une qui considère la structure topologique de l'entreprise et l'autre qui intègre l'impact des applications au-dessus de TCP - pour aider à évaluer et à comparer les performances des politiques d'ordonnancement dans les réseaux d'entreprise classiques

EFD: An efficient low-overhead scheduler

Part 3: Resource AllocationInternational audienceSize-based scheduling methods receive a lot of attention as they can greatly enhance the responsiveness perceived by the users. In effect, they give higher priority to small interactive flows which are the important ones for a good user experience. In this paper, we propose a new packet scheduling method, Early Flow Discard (EFD), which belongs to the family of Multi-Level Processor Sharing policies. Compared to earlier proposals, the key feature of EFD is the way flow bookkeeping is performed as flow entries are removed from the flow table as soon as there is no more corresponding packet in the queue. In this way, the active flow table remains of small size at all times. EFD is not limited to a scheduling policy but also incorporates a buffer management policy. We show through extensive simulations that EFD retains the most desirable property of more resource intensive size-based methods, namely low response time for short flows, while limiting lock-outs of large flows and effectively protecting low/medium rate multimedia transfers

The Impact of the Buffer Unit on the Performance in 802.11 Wireless LANs

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