Weighted Scheduling of Time-Sensitive Coflows

Datacenter networks commonly facilitate the transmission of data in distributed computing frameworks through coflows, which are collections of parallel flows associated with a common task. Most of the existing research has concentrated on scheduling coflows to minimize the time required for their completion, i.e., to optimize the average dispatch rate of coflows in the network fabric. Nevertheless, modern applications often produce coflows that are specifically intended for online services and mission-crucial computational tasks, necessitating adherence to specific deadlines for their completion. In this paper, we introduce \wdcoflow,~ a new algorithm to maximize the weighted number of coflows that complete before their deadline. By combining a dynamic programming algorithm along with parallel inequalities, our heuristic solution performs at once coflow admission control and coflow prioritization, imposing a $\sigma$-order on the set of coflows. With extensive simulation, we demonstrate the effectiveness of our algorithm in improving up to $3\times$ more coflows that meet their deadline in comparison the best SoA solution, namely $\mathtt{CS\text{-}MHA}$. Furthermore, when weights are used to differentiate coflow classes, \wdcoflow~ is able to improve the admission per class up to $4\times$, while increasing the average weighted coflow admission rate.Comment: Submitted to IEEE Transactions on Cloud Computing. Parts of this work have been presented at IFIP Networking 202

COVID-19 symptoms at hospital admission vary with age and sex: results from the ISARIC prospective multinational observational study

Background: The ISARIC prospective multinational observational study is the largest cohort of hospitalized patients with COVID-19. We present relationships of age, sex, and nationality to presenting symptoms. Methods: International, prospective observational study of 60 109 hospitalized symptomatic patients with laboratory-confirmed COVID-19 recruited from 43 countries between 30 January and 3 August 2020. Logistic regression was performed to evaluate relationships of age and sex to published COVID-19 case definitions and the most commonly reported symptoms. Results: ‘Typical’ symptoms of fever (69%), cough (68%) and shortness of breath (66%) were the most commonly reported. 92% of patients experienced at least one of these. Prevalence of typical symptoms was greatest in 30- to 60-year-olds (respectively 80, 79, 69%; at least one 95%). They were reported less frequently in children (≤ 18 years: 69, 48, 23; 85%), older adults (≥ 70 years: 61, 62, 65; 90%), and women (66, 66, 64; 90%; vs. men 71, 70, 67; 93%, each P < 0.001). The most common atypical presentations under 60 years of age were nausea and vomiting and abdominal pain, and over 60 years was confusion. Regression models showed significant differences in symptoms with sex, age and country. Interpretation: This international collaboration has allowed us to report reliable symptom data from the largest cohort of patients admitted to hospital with COVID-19. Adults over 60 and children admitted to hospital with COVID-19 are less likely to present with typical symptoms. Nausea and vomiting are common atypical presentations under 30 years. Confusion is a frequent atypical presentation of COVID-19 in adults over 60 years. Women are less likely to experience typical symptoms than men

DCoflow: Deadline-Aware Scheduling Algorithm for Coflows in Datacenter Networks

International audienceDatacenter networks routinely support the data transfers of distributed computing frameworks in the form of coflows, i.e., sets of concurrent flows related to a common task. The vast majority of the literature has focused on the problem of scheduling coflows for completion time minimization, i.e., to maximize the average rate at which coflows are dispatched in the network fabric. Modern applications, though, may generate coflows dedicated to online services and missioncritical computing tasks which have to comply with specific completion deadlines. In this paper, we introduce DCoflow, a lightweight deadline-aware scheduler for time-critical coflows in datacenter networks. The algorithm combines an online joint admission control and scheduling logic and returns a σ-order schedule which maximizes the number of coflows that attain their deadlines. Extensive numerical results demonstrate that the proposed solution outperforms existing ones

DCoflow: Deadline-Aware Scheduling Algorithm for Coflows in Datacenter Networks

International audienceDatacenter networks routinely support the data transfers of distributed computing frameworks in the form of coflows, i.e., sets of concurrent flows related to a common task. The vast majority of the literature has focused on the problem of scheduling coflows for completion time minimization, i.e., to maximize the average rate at which coflows are dispatched in the network fabric. Modern applications, though, may generate coflows dedicated to online services and missioncritical computing tasks which have to comply with specific completion deadlines. In this paper, we introduce DCoflow, a lightweight deadline-aware scheduler for time-critical coflows in datacenter networks. The algorithm combines an online joint admission control and scheduling logic and returns a σ-order schedule which maximizes the number of coflows that attain their deadlines. Extensive numerical results demonstrate that the proposed solution outperforms existing ones

DCoflow: Deadline-Aware Scheduling Algorithm for Coflows in Datacenter Networks

Datacenter networks routinely support the data transfers of distributed computing frameworks in the form of coflows, i.e., sets of concurrent flows related to a common task. The vast majority of the literature has focused on the problem of scheduling coflows for completion time minimization, i.e., to maximize the average rate at which coflows are dispatched in the network fabric. Modern applications, though, may generate coflows dedicated to online services and mission-critical computing tasks which have to comply with specific completion deadlines. In this paper, we introduce $\mathtt{DCoflow}$, a lightweight deadline-aware scheduler for time-critical coflows in datacenter networks. The algorithm combines an online joint admission control and scheduling logic and returns a $\sigma$-order schedule which maximizes the number of coflows that attain their deadlines. Extensive numerical results demonstrate that the proposed solution outperforms existing ones.Comment: Accepted to IFIP Networking 2022 (Catania, Italy

Impact on disease mortality of clinical, biological, and virological characteristics at hospital admission and overtime in COVID‐19 patients

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Rilpivirine in HIV-1-positive women initiating pregnancy: to switch or not to switch?

International audienceBackgroundSafety data about rilpivirine use during pregnancy remain scarce, and rilpivirine plasma concentrations are reduced during second/third trimesters, with a potential risk of viral breakthroughs. Thus, French guidelines recommend switching to rilpivirine-free combinations (RFCs) during pregnancy.ObjectivesTo describe the characteristics of women initiating pregnancy while on rilpivirine and to compare the outcomes for virologically suppressed subjects continuing rilpivirine until delivery versus switching to an RFC.MethodsIn the ANRS-EPF French Perinatal cohort, we included women on rilpivirine at conception in 2010–18. Pregnancy outcomes were compared between patients continuing versus interrupting rilpivirine. In women with documented viral suppression (<50 copies/mL) before 14 weeks of gestation (WG) while on rilpivirine, we compared the probability of viral rebound (≥50 copies/mL) during pregnancy between subjects continuing rilpivirine versus those switching to RFC.ResultsAmong 247 women included, 88.7% had viral suppression at the beginning of pregnancy. Overall, 184 women (74.5%) switched to an RFC (mostly PI/ritonavir-based regimens) at a median gestational age of 8.0 WG. Plasma HIV-1 RNA nearest delivery was <50 copies/mL in 95.6% of women. Among 69 women with documented viral suppression before 14 WG, the risk of viral rebound was higher when switching to RFCs than when continuing rilpivirine (20.0% versus 0.0%, P = 0.046). Delivery outcomes were similar between groups (overall birth defects, 3.8/100 live births; pregnancy losses, 2.0%; preterm deliveries, 10.6%). No HIV transmission occurred.ConclusionsIn virologically suppressed women initiating pregnancy, continuing rilpivirine was associated with better virological outcome than changing regimen. We did not observe a higher risk of adverse pregnancy outcomes