We consider the design of a scheduling policy for video streaming in a wireless network formed by several users and helpers (e.g., base stations). In such networks, any user is typically in the range ...of multiple helpers. Hence, an efficient policy should allow the users to dynamically select the helper nodes to download from and determine adaptively the quality level of the requested video segment. In order to obtain a tractable formulation, we follow a "divide and conquer" approach. First, we formulate a network utility maximization (NUM) problem where the network utility function is a concave and component-wise nondecreasing function of the time-averaged users' requested video quality index, and maximization is subject to the stability of all queues in the system. Second, we solve the NUM problem by using a Lyapunov drift plus penalty approach, obtaining a dynamic adaptive scheme that decomposes into two building blocks: 1) adaptive video quality and helper selection (run at the user nodes); and 2) dynamic allocation of the helper-to-user transmission rates (run at the help nodes). Our solution provably achieves NUM optimality in a strong per-sample path sense (i.e., without assumptions of stationarity and ergodicity). Third, we observe that, since all queues in the system are stable, all requested video chunks shall be eventually delivered. Fourth, in order to translate the requested video quality into the effective video quality at the user playback, it is necessary that the chunks are delivered within their playback deadline. This requires that the largest delay among all queues at the helpers serving any given user is less than the pre-buffering time of that user at its streaming session startup phase. In order to achieve this condition with high probability, we propose an effective and decentralized (albeit heuristic) scheme to adaptively calculate the pre-buffering and re-buffering time at each user. In this way, the system is forced to work in the "smooth streaming regime," i.e., in the regime of very small playback buffer underrun rate. Through simulations, we evaluate the performance of the proposed algorithm under realistic assumptions of a network with densely deployed helper and user nodes, including user mobility, variable bit-rate video coding, and users joining or leaving the system at arbitrary times.
We hypothesized that dosing vancomycin to achieve trough concentrations of >15 mg/liter overdoses many adults compared to area under the concentration-time curve (AUC)-guided dosing. We conducted a ...3-year, prospective study of vancomycin dosing, plasma concentrations, and outcomes. In year 1, nonstudy clinicians targeted trough concentrations of 10 to 20 mg/liter (infection dependent) and controlled dosing. In years 2 and 3, the study team controlled vancomycin dosing with BestDose Bayesian software to achieve a daily, steady-state AUC/MIC ratio of ≥400, with a maximum AUC value of 800 mg · h/liter, regardless of trough concentration. For Bayesian estimation of AUCs, we used trough samples in years 1 and 2 and optimally timed samples in year 3. We enrolled 252 adults who were ≥18 years old with ≥1 available vancomycin concentration. Only 19% of all trough concentrations were therapeutic versus 70% of AUCs (
< 0.0001). After enrollment, median trough concentrations by year were 14.4, 9.7, and 10.9 mg/liter (
= 0.005), with 36%, 7%, and 6% over 15 mg/liter (
< 0.0001). Bayesian AUC-guided dosing in years 2 and 3 was associated with fewer additional blood samples per subject (3.6, 2.0, and 2.4;
= 0.003), shorter therapy durations (8.2, 5.4, and 4.7 days;
= 0.03), and reduced nephrotoxicity (8%, 0%, and 2%;
= 0.01). The median inpatient stay was 20 days among nephrotoxic patients versus 6 days (
= 0.002). There was no difference in efficacy by year, with 42% of patients having microbiologically proven infections. Compared to trough concentration targets, AUC-guided, Bayesian estimation-assisted vancomycin dosing was associated with decreased nephrotoxicity, reduced per-patient blood sampling, and shorter length of therapy, without compromising efficacy. These benefits have the potential for substantial cost savings. (This study has been registered at ClinicalTrials.gov under registration no. NCT01932034.).
This paper considers optimization of time averages in systems with variable length renewal frames. Applications include power-aware and profit-aware scheduling in wireless networks, peer-to-peer ...networks, and transportation systems. Every frame, a new policy is implemented that affects the frame size and that creates a vector of attributes. The policy can be a single decision in response to a random event observed on the frame, or a sequence of such decisions. The goal is to choose policies on each frame in order to maximize a time average of one attribute, subject to additional time average constraints on the others. Two algorithms are developed, both based on Lyapunov optimization concepts. The first makes decisions to minimize a "drift-plus-penalty" ratio over each frame. The second is similar but does not involve a ratio. For systems that make only a single decision on each frame, both algorithms are shown to learn efficient behavior without a-priori statistical knowledge. The framework is also applicable to large classes of constrained Markov decision problems. Such problems are reduced to finding an approximate solution to a simpler unconstrained stochastic shortest path problem on each frame. Approximations for the simpler problem may still suffer from a curse of dimensionality for systems with large state space. However, our results are compatible with any approximation method, and demonstrate an explicit tradeoff between performance and convergence time.
Purpose
This Position Paper aims to review and discuss the available data on therapeutic drug monitoring (TDM) of antibacterials, antifungals and antivirals in critically ill adult patients in the ...intensive care unit (ICU). This Position Paper also provides a practical guide on how TDM can be applied in routine clinical practice to improve therapeutic outcomes in critically ill adult patients.
Methods
Literature review and analysis were performed by Panel Members nominated by the endorsing organisations, European Society of Intensive Care Medicine (ESICM), Pharmacokinetic/Pharmacodynamic and Critically Ill Patient Study Groups of European Society of Clinical Microbiology and Infectious Diseases (ESCMID), International Association for Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT) and International Society of Antimicrobial Chemotherapy (ISAC). Panel members made recommendations for whether TDM should be applied clinically for different antimicrobials/classes.
Results
TDM-guided dosing has been shown to be clinically beneficial for aminoglycosides, voriconazole and ribavirin. For most common antibiotics and antifungals in the ICU, a clear therapeutic range has been established, and for these agents, routine TDM in critically ill patients appears meritorious. For the antivirals, research is needed to identify therapeutic targets and determine whether antiviral TDM is indeed meritorious in this patient population. The Panel Members recommend routine TDM to be performed for aminoglycosides, beta-lactam antibiotics, linezolid, teicoplanin, vancomycin and voriconazole in critically ill patients.
Conclusion
Although TDM should be the standard of care for most antimicrobials in every ICU, important barriers need to be addressed before routine TDM can be widely employed worldwide.
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We develop a dynamic control strategy for minimizing energy expenditure in a time-varying wireless network with adaptive transmission rates. The algorithm operates without knowledge of traffic rates ...or channel statistics, and yields average power that is arbitrarily close to the minimum possible value achieved by an algorithm optimized with complete knowledge of future events. Proximity to this optimal solution is shown to be inversely proportional to network delay. We then present a similar algorithm that solves the related problem of maximizing network throughput subject to peak and average power constraints. The techniques used in this paper are novel and establish a foundation for stochastic network optimization
It is well known that max-weight policies based on a queue backlog index can be used to stabilize stochastic networks, and that similar stability results hold if a delay index is used. Using Lyapunov ...optimization, we extend this analysis to design a utility maximizing algorithm that uses explicit delay information from the head-of-line packet at each user. The resulting policy is shown to ensure deterministic worst-case delay guarantees and to yield a throughput utility that differs from the optimally fair value by an amount that is inversely proportional to the delay guarantee. Our results hold for a general class of 1-hop networks, including packet switches and multiuser wireless systems with time-varying reliability .
The backpressure algorithm has been widely used as a distributed solution to the problem of joint rate control and routing in multi-hop data networks. By controlling an algorithm parameter, the ...backpressure algorithm can achieve an arbitrarily small utility optimality gap. However, this in turn brings in a large queue length at each node and hence causes large network delay. This phenomenon is known as the fundamental utility-delay tradeoff. The best known utility-delay tradeoff for general networks is O(∈), O(1/∈) and is attained by a backpressure algorithm based on a drift-plus-penalty technique. This may suggest that to achieve an arbitrarily small utility optimality gap, backpressure-based algorithms must incur arbitrarily large queue lengths. However, this paper proposes a new backpressure algorithm that has a vanishing utility optimality gap, so utility converges to exact optimality as the algorithm keeps running, while queue lengths are bounded throughout by a finite constant. The technique uses backpressure and drift concepts with a new method for convex programming.
This paper considers a wireless link with randomly arriving data that are queued and served over a time-varying channel. It is known that any algorithm that comes within ε of the minimum average ...power required for queue stability must incur average queue size at least Ω(log(1/ε)). However, the optimal convergence time is unknown. This paper develops a scheduling algorithm that, for any ε > 0, achieves the optimal O(log(1/ε)) average queue size tradeoff with a convergence time of O(log(1/ε)/ε). An example system is presented for which all algorithms require convergence time at least Ω(1/ε), and so the proposed algorithm is within a logarithmic factor of the optimal convergence time. The method uses the simple drift-plus-penalty technique with an improved convergence time analysis.
Summary Infections in critically ill patients are associated with persistently poor clinical outcomes. These patients have severely altered and variable antibiotic pharmacokinetics and are infected ...by less susceptible pathogens. Antibiotic dosing that does not account for these features is likely to result in suboptimum outcomes. In this Review, we explore the challenges related to patients and pathogens that contribute to inadequate antibiotic dosing and discuss how to implement a process for individualised antibiotic therapy that increases the accuracy of dosing and optimises care for critically ill patients. To improve antibiotic dosing, any physiological changes in patients that could alter antibiotic concentrations should first be established; such changes include altered fluid status, changes in serum albumin concentrations and renal and hepatic function, and microvascular failure. Second, antibiotic susceptibility of pathogens should be confirmed with microbiological techniques. Data for bacterial susceptibility could then be combined with measured data for antibiotic concentrations (when available) in clinical dosing software, which uses pharmacokinetic/pharmacodynamic derived models from critically ill patients to predict accurately the dosing needs for individual patients. Individualisation of dosing could optimise antibiotic exposure and maximise effectiveness.
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