Abstract-Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. The contribution of this paper is three-fold. First, we describe our implementation in the form of an HTTP proxy server, and show that both stability and fairness are strongly improved. Second, we present an analytical model that allows to compute the number of changes in video quality and the bitrate of a video stream. Third, we validate the accuracy of the model by comparing the modelbased estimations for the number of changes in video quality and for the mean bitrate of a video stream, with results in a real implementation of our HAS assistant. The results show that the model-based results are highly accurate. As such, this model is useful in practice for planning video delivery networks that use in-network HAS assistants, and enables us to analyze the stability and the mean bitrate of HAS streams prior to real deployment.

Science. It is sponsored by the Netherlands Organisation for Scientific Research (NWO).CWI is a founding member of ERCIM, the European Research Consortium for Informatics and Mathematics.CWI's research has a theme-oriented structure and is grouped into four clusters. Listed below are the names of the clusters and in parentheses their acronyms. From coordination to stochastic models of QoS ABSTRACT Reo is a channel-based coordination model whose operational semantics is given by Constraint Automata (CA). Quantitative Constraint Automata extend CA (and hence, Reo) with quantitative models to capture such non-functional aspects of a system's behaviour as delays, costs, resource needs and consumption, that depend on the internal details of the system. However, the performance of a system can crucially depend not only on its internal details, but also on how it is used in an environment, as determined for instance by the frequencies and distributions of the arrivals of I/O requests. In this paper we propose Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system's environment on its performance. Moreover, we show the translation of QIA into ContinuousTime Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis of QIA and Reo circuits. Abstract. Reo is a channel-based coordination model whose operational semantics is given by Constraint Automata (CA). Quantitative Constraint Automata extend CA (and hence, Reo) with quantitative models to capture such non-functional aspects of a system's behaviour as delays, costs, resource needs and consumption, that depend on the internal details of the system. However, the performance of a system can crucially depend not only on its internal details, but also on how it is used in an environment, as determined for instance by the frequencies and distributions of the arrivals of I/O requests. In this paper we propose Quantitative Intentional Automata (QIA), an extension of CA that allow incorporating the influence of a system's environment on its performance. Moreover, we show the translation of QIA into Continuous-Time Markov Chains (CTMCs), which allows us to apply existing CTMC tools and techniques for performance analysis of QIA and Reo circuits. Probability, Networks and Algorithms (PNA)SoftwareKeywords: Performance evaluation, Coordination language, Reo, MarkovChains. IntroductionService-oriented Computing (SOC) provides the means to design and deploy distributed applications that span organization boundaries and computing platforms by exploiting and composing existing services available over a network. Services are platform-and network-independent applications that support rapid, low-cost, loosely-coupled composition. Services run on the hardware of their own providers, in different containers, separated by fire-walls and other ownership and trust barriers. Their composition requires additional mechanisms (e.g., process work-flow engines, connectors, or glue code) to impos...

Grid computing is an emerging technology by which huge numbers of processors over the world create a global source of processing power. Their collaboration makes it possible to perform computations that are too extensive to perform on a single processor. On a grid processors may connect and disconnect at any time, and the load on the computers can be highly bursty. Those characteristics raise the need for the development of techniques that make grid applications robust against the dynamics of the grid environment. In particular, applications that use significant amounts of processor power for running jobs need effective predictions of the expected computation times of those jobs on remote hosts. Currently, there are no effective prediction methods available that cope with the ever-changing running times of jobs on a grid environment. Motivated by this, we develop the Dynamic Exponential Smoothing (DES) method to predict running times in a grid environment. The main idea behind DES is that it dynamically adapts its prediction strategy to the height of the fluctuations in those running times. We have performed extensive experiments in a real global-scale grid environment to compare the effectiveness of DES. The results demonstrate that DES strongly and consistently outperforms existing prediction methods.

We study a queueing network with a single shared server that serves the queues in a cyclic order. External customers arrive at the queues according to independent Poisson processes. After completing service, a customer either leaves the system or is routed to another queue. This model is very generic and finds many applications in computer systems, communication networks, manufacturing systems, and robotics. Special cases of the introduced network include well-known polling models, tandem queues, systems with a waiting room, multi-stage models with parallel queues, and many others. A complicating factor of this model is that the internally rerouted customers do not arrive at the various queues according to a Poisson process, causing standard techniques to find waiting-time distributions to fail. In this paper we develop a new method to obtain exact expressions for the Laplace-Stieltjes transforms of the steady-state waiting-time distributions. This method can be applied to a wide variety of models which lacked an analysis of the waitingtime distribution until now.

In life-threatening emergency situations, the ability of emergency medical service (EMS) providers to arrive at the emergency scene within a few minutes may make the difference between survival or death. To realize such extremely short response times at affordable cost, efficient planning of EMS systems is crucial. In this article we will discuss the Testing Interface For Ambulance Research (TIFAR) simulation tool that can be used by EMS managers and researchers to evaluate the effectiveness of different dispatch strategies. The accuracy of TIFAR is assessed by comparing the TIFAR-based performance indicators against a real EMS system in the Netherlands. The results show that TIFAR performs extremely well.

Today’s smartphones allow for a wide range of “big data” measurement, for example, ecological momentary assessment (EMA), whereby behaviours are repeatedly assessed within a person’s natural environment. With this type of data, we can better understand – and predict – risk for behavioral and health issues and opportunities for (self-monitoring) interventions. In this mixed-methods feasibility study, through convenience sampling we collected data from 32 participants (aged 16–24) over a period of three months. To gain more insight into the app experiences of youth with mental health problems, we interviewed a subsample of 10 adolescents who received psycthological treatment. The results from this feasibility study indicate that emojis) can be used to identify positive and negative feelings, and individual pattern analyses of emojis may be useful for clinical purposes. While adolescents receiving mental health care are positive about future applications, these findings also highlight some caveats, such as possible drawback of inaccurate representation and incorrect predictions of emotional states. Therefore, at this stage, the app should always be combined with professional counseling. Results from this small pilot study warrant replication with studies of substantially larger sample size.

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We investigate dynamic decision mechanisms for composite web services maximizing the expected revenue for the providers of composite services. A composite web service is represented by a (sequential) workflow, and for each task within this workflow, a number of service alternatives may be available. These alternatives offer the same functionality at different price-quality levels. After executing a sub-service, it is decided which alternative of the next sub-service in the workflow is invoked. The decisions optimizing expected revenue are based on observed response times, costs and responsetime characteristics of the alternatives as well as end-toend response-time objectives and corresponding rewards and penalties. We propose an approach, based on dynamic programming, to determine the optimal, dynamic selection policy.Extensive numerical examples show significant potential gain in expected revenues using the dynamic approach compared to other, non-dynamic approaches.