Router-based algorithms for improving internet quality of service.

We begin this thesis by generalizing some results related to a recently proposed positive system model of TCP congestion control algorithms. Then, motivated by a mean ¯eld analysis of the positive system model, a novel, stateless, queue management scheme is designed: Multi-Level Comparisons with index l (MLC(l)). In the limit, MLC(l) enforces max-min fairness in a network of TCP flows. We go further, showing that counting past drops at a congested link provides su±cient information to enforce max-min fairness among long-lived flows and to reduce the flow completion times of short-lived flows. Analytical models are presented, and the accuracy of predictions are validated by packet level ns2 simulations. We then move our attention to e±cient measurement and monitoring techniques. A small active counter architecture is presented that addresses the problem of accurate approximation of statistics counter values at very-high speeds that can be both updated and estimated on a per-packet basis. These algorithms are necessary in the design of router-based flow control algorithms since on-chip Static RAM (SRAM) currently is a scarce resource, and being economical with its usage is an important task. A highly scalable method for heavy-hitter identifcation that uses our small active counters architecture is developed based on heuristic argument. Its performance is compared to several state-of-the-art algorithms and shown to out-perform them. In the last part of the thesis we discuss the delay-utilization tradeoff in the congested Internet links. While several groups of authors have recently analyzed this tradeoff, the lack of realistic assumption in their models and the extreme complexity in estimation of model parameters, reduces their applicability at real Internet links. We propose an adaptive scheme that regulates the available queue space to keep utilization at desired, high, level. As a consequence, in large-number-of-users regimes, sacrifcing 1-2% of bandwidth can result in queueing delays that are an order of magnitude smaller than in the standard BDP-bu®ering case. We go further and introduce an optimization framework for describing the problem of interest and propose an online algorithm for solving it

Router-based algorithms for improving internet quality of service.

We begin this thesis by generalizing some results related to a recently proposed positive system model of TCP congestion control algorithms. Then, motivated by a mean ¯eld analysis of the positive system model, a novel, stateless, queue management scheme is designed: Multi-Level Comparisons with index l (MLC(l)). In the limit, MLC(l) enforces max-min fairness in a network of TCP flows. We go further, showing that counting past drops at a congested link provides su±cient information to enforce max-min fairness among long-lived flows and to reduce the flow completion times of short-lived flows. Analytical models are presented, and the accuracy of predictions are validated by packet level ns2 simulations. We then move our attention to e±cient measurement and monitoring techniques. A small active counter architecture is presented that addresses the problem of accurate approximation of statistics counter values at very-high speeds that can be both updated and estimated on a per-packet basis. These algorithms are necessary in the design of router-based flow control algorithms since on-chip Static RAM (SRAM) currently is a scarce resource, and being economical with its usage is an important task. A highly scalable method for heavy-hitter identifcation that uses our small active counters architecture is developed based on heuristic argument. Its performance is compared to several state-of-the-art algorithms and shown to out-perform them. In the last part of the thesis we discuss the delay-utilization tradeoff in the congested Internet links. While several groups of authors have recently analyzed this tradeoff, the lack of realistic assumption in their models and the extreme complexity in estimation of model parameters, reduces their applicability at real Internet links. We propose an adaptive scheme that regulates the available queue space to keep utilization at desired, high, level. As a consequence, in large-number-of-users regimes, sacrifcing 1-2% of bandwidth can result in queueing delays that are an order of magnitude smaller than in the standard BDP-bu®ering case. We go further and introduce an optimization framework for describing the problem of interest and propose an online algorithm for solving it

Use of antenatal corticosteroids and tocolytic drugs in preterm births in 29 countries: an analysis of the WHO Multicountry Survey on Maternal and Newborn Health

Background: Despite the global burden of morbidity and mortality associated with preterm birth, little evidence is available for use of antenatal corticosteroids and tocolytic drugs in preterm births in low-income and middle-income countries. We analysed data from the WHO Multicountry Survey on Maternal and Newborn Health (WHOMCS) to assess coverage for these interventions in preterm deliveries. Methods: WHOMCS is a facility-based, cross-sectional survey database of birth outcomes in 359 facilities in 29 countries, with data collected prospectively from May 1, 2010, to Dec 31, 2011. For this analysis, we included deliveries after 22 weeks’ gestation and we excluded births that occurred outside a facility or quicker than 3 h after arrival. We calculated use of antenatal corticosteroids in women who gave birth between 26 and 34 weeks’ gestation, when antenatal corticosteroids are known to be most beneficial. We also calculated use in women at 22–25 weeks’ and 34–36 weeks’ gestation. We assessed tocolytic drug use, with and without antenatal corticosteroids, in spontaneous, uncomplicated preterm deliveries at 26–34 weeks’ gestation. Findings: Of 303 842 recorded deliveries after 22 weeks’ gestation, 17 705 (6%) were preterm. 3900 (52%) of 7547 women who gave birth at 26–34 weeks’ gestation, 94 (19%) of 497 women who gave birth at 22–25 weeks’ gestation, and 2276 (24%) of 9661 women who gave birth at 35–36 weeks’ gestation received antenatal corticosteroids. Rates of antenatal corticosteroid use varied between countries (median 54%, range 16–91%; IQR 30–68%). Of 4677 women who were potentially eligible for tocolysis drugs, 1276 (27%) were treated with bed rest or hydration and 2248 (48%) received no treatment. β-agonists alone (n=346, 7%) were the most frequently used tocolytic drug. Only 848 (18%) of potentially eligible women received both a tocolytic drug and antenatal corticosteroids. Interpretation: Use of interventions was generally poor, despite evidence for their benefit for newborn babies. A substantial proportion of antenatal corticosteroid use occurred at gestational ages at which benefit is controversial, and use of less effective or potentially harmful tocolytic drugs was common. Implementation research and contextualised health policies are needed to improve drug availability and increase compliance with best obstetric practice

Stochastic programming approaches to air traffic flow management under the uncertainty of weather

As air traffic congestion grows, air traffic flow management (ATFM) is becoming a great concern. ATFM deals with air traffic and the efficient utilization of the airport and airspace. Air traffic efficiency is heavily influenced by unanticipated factors, or uncertainties, which can come from several sources such as mechanical breakdown; however, weather is the main unavoidable cause of uncertainty. Because weather is unpredictable, it poses a critical challenge for ATFM in current airport and airspace operations. Convective weather results in congestion at airports as well as in airspace sectors. During times of congestion, the decision as how and when to send aircraft toward an airspace sector in the presence of weather is difficult. To approach this problem, we first propose a two-stage stochastic integer program by emphasizing a given single sector. By considering ground delay, cancellation, and cruise speed for each flight on the ground in the first stage, as well as air holding and diversion recourse actions for each flight in the air in the second stage, our model determines how aircraft are sent toward a sector under the uncertainty of weather. However, due to the large number of weather scenarios, the model is intractable in practice. To overcome the intractability, we suggest a rolling horizon method to solve the problem to near optimal. Lagrangian relaxation and subgradient method are used to justify the rolling horizon method. Since the rolling horizon method can be solved in real time, we can apply it to actual aircraft schedules to reduce the costs incurred on the ground as well as in airspace. We then extend our two-stage model to a multistage stochastic program, which increases the number of possible weather realizations and results a more efficient schedule in terms of costs. The rolling horizon method as well as Lagrangian relaxation and subgradient method are applied to this multistage model. An overall comparison among the previously described methodologies are presented.Ph.D.Committee Chair: Johnson, Ellis; Committee Co-Chair: Clarke, John-Paul; Committee Member: Ahmed, Shabbir; Committee Member: Sokol, Joel; Committee Member: Solak, Sena