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Many businesses possess a small infrastructure that they can use for their computing tasks, but also often buy extra computing resources from clouds. Cloud vendors such as Amazon EC2 offer two types of purchase options: on-demand and spot instances. As tenants have limited budgets to satisfy their computing needs, it is crucial for them to determine how to purchase different options and utilize them (in addition to possible self-owned instances) in a cost-effective manner while respecting their response-time targets. In this paper, we propose a framework to design policies to allocate self-owned, on-demand and spot instances to arriving jobs. In particular, we propose a near-optimal policy to determine the number of self-owned instances and an optimal policy to determine the number of on-demand instances to buy and the number of spot instances to bid for at each time unit. Our policies rely on a small number of parameters and we use an online learning technique to infer their optimal values. Through numerical simulations, we show the effectiveness of our proposed policies, in particular that they achieve a cost reduction of up to 64.51 percent when spot and on-demand instances are considered and of up to 43.74 percent when self-owned instances are considered, compared to previously proposed or intuitive policies. [ABSTRACT FROM AUTHOR]

Abstract: In the realm of multiscale signal analysis, multifractal analysis provides a natural and rich framework to measure the roughness of a time series. As such, it has drawn special attention of both mathematicians and practitioners, and led them to characterize relevant physiological factors impacting the heart rate variability. Notwithstanding these considerable progresses, multifractal analysis almost exclusively developed around the concept of Legendre singularity spectrum, for which efficient and elaborate estimators exist, but which are structurally blind to subtle features like non-concavity or, to a certain extent, non scaling of the distributions. Large deviations theory allows bypassing these limitations but it is only very recently that performing estimators were proposed to reliably compute the corresponding large deviations singularity spectrum. In this article, we illustrate the relevance of this approach, on both theoretical objects and on human heart rate signals from the Physionet public database. As conjectured, we verify that large deviations principles reveal significant information that otherwise remains hidden with classical approaches, and which can be reminiscent of some physiological characteristics. In particular we quantify the presence/absence of scale invariance of RR signals. [Copyright &y& Elsevier]

Abstract: We establish large deviation properties valid for almost every sample path of a class of stationary mixing processes . These properties are inherited from those of and describe how the local fluctuations of almost every realization of deviate from the almost sure behavior. These results apply to the fluctuations of Brownian motion, Birkhoff averages on hyperbolic dynamics, as well as branching random walks. Also, they lead to new insights into the “randomness” of the digits of expansions in integer bases of Pi. We formulate a new conjecture, supported by numerical experiments, implying the normality of Pi. [Copyright &y& Elsevier]

For more than a decade, it has been observed that network traffic exhibits long-range dependence and many models have been proposed relating this property to heavy-tailed flow durations. However, none of these models consider correlations at flow scale. Such correlations exist and will become more prominent in the future Internet with the emergence of flow-aware control mechanisms correlating a flow's transmission to its characteristics (size, duration, etc.). In this article, we study the impact of the correlation between flow rates and durations on the long-range dependence of aggregate traffic. Our results extend those of existing models by showing that two possible regimes of long-range dependence exist at different time scales. The long-range dependence in each regime can be stronger or weaker than standard predictions, depending on the conditional statistics between the flow rates and durations. In the independent case, our proposed model consistently reduces to former approaches. The pertinence of our model is validated on real web traffic traces, and its ability to accurately explain the Hurst parameter is validated on both web traces and numerical simulations. [ABSTRACT FROM AUTHOR]

This paper analyzes the motivation and strategies for ensuring cooperative behavior among hosts and customer networks in the Internet and 5G networks. The hypothesis is that better cooperation among the benevolent entities could improve the overall Internet welfare, motivating the need for adoption of cooperative security. However, in state of the art, the prevalent security approach in the Internet is based on self-help, while the adoption of cooperative methods is progressing slowly. At the same time, the ubiquitous reliance on 5G and mission critical nature of some of the new services, for example, ultrareliable (machine-to-machine) communication and Internet of things, requires that 5G will do its best to curb the malicious (noncooperative) behavior from becoming a cause of failure to the legitimate services. In this paper, we relate our analysis of the conditions for sustainable cooperation in the Internet with the famous end-to-end principle, and present the hypothesis that there is no end-to-end solution to the problem of ensuring cooperation among Internet hosts. Game theory allows studying the outcomes of interactions among the players with conflicting interests. We use it to study the hypothesis and show that introducing the reputation of Internet nodes and customer networks can lead to cooperation, which improves the overall Internet welfare and reduces the payoffs of malicious actors. We study the possible response of noncooperative users with advanced defection strategies and the resulting outcomes. We argue that 5G shall make significant progress towards uprooting the selfish behavior and malicious activities using cooperation and relate it with motivation for providing ubiquitous connectivity and ultrareliable services. The paper concludes by summarizing our earlier work on the application of the proposed methods of cooperation to 5G and the Internet; outlining how cooperation in security is not only desirable but also feasible. [ABSTRACT FROM AUTHOR]

Communication networks are complex systems whose operation relies on a large number of components that work together to provide services to end users. As the quality of these services depends on different parameters, understanding how each of them impacts the final performance of a service is a challenging but important problem. However, intervening on individual factors to evaluate the impact of the different parameters is often impractical due to the high cost of intervention in a network. It is, therefore, desirable to adopt a formal approach to understand the role of the different parameters and to predict how a change in any of these parameters will impact performance. The approach of causality pioneered by J. Pearl provides a powerful framework to investigate these questions. Most of the existing theory is non-parametric and does not make any assumption on the nature of the system under study. However, most of the implementations of causal model inference algorithms and most of the examples of usage of a causal model to predict intervention rely on assumptions such linearity, normality, or discrete data. In this article, we present a methodology to overcome the challenges of working with real-world data and extend the application of causality to complex systems in the area of telecommunication networks, for which assumptions of normality, linearity and discrete data do no hold. Specifically, we study the performance of TCP, which is the prevalent protocol for reliable end-to-end transfer in the Internet. Analytical models of the performance of TCP exist, but they take into account the state of network only and disregard the impact of the application at the sender and the receiver, which often influences TCP performance. To address this point, we take as application the file transfer protocol (FTP), which uses TCP for reliable transfer. Studying a well-understood protocol such as TCP allows us to validate our approach and compare its results to previous studies. We first present and evaluate our methodology using TCP traffic obtained via network emulation, which allows us to experimentally validate the prediction of an intervention. We then apply the methodology to real-world TCP traffic sent over the Internet. Throughout the article, we compare the causal approach for studying TCP performance to other approaches such as analytical modeling or simulation and and show how they can complement each other. [ABSTRACT FROM AUTHOR]

Heart beat rate and blood pressure, together with baroreflex sensitivity, have become important tools in assessing cardiac autonomic system control and in studying sympathovagal balance. These analyses are usually performed thanks to spectral indices computed from standard spectral analysis techniques. However, standard spectral analysis and its corresponding rigid band-pass filter formulation suffer from two major drawbacks. It can be significantly distorted by non-stationarity issues and it proves unable to adjust to natural intra- and inter-individual variability. Empirical mode decomposition (EMD), a tool recently introduced in the literature, provides us with a signal-adaptive decomposition that proves useful for the analysis of non-stationary data and shows a strong capability to precisely adjust to the spectral content of the analyzed data. It is based on the concept that any complicated set of data can be decomposed into a finite number of components, called intrinsic mode functions, associated with different spectral contributions. The aims of this study were twofold. First, we studied the changes in the sympathovagal balance induced by various pharmacological blockades (phentolamine, atropine and atenolol) of the autonomic nervous system in normotensive rats. Secondly, we assessed the use of EMD for the analysis of the cardiac sympathovagal balance after pharmacological injections. For this, we developed a new (EMD-based) low frequency vs. high frequency spectral decomposition of heart beat variability and systolic blood pressure, we define the corresponding EMD spectral indices and study their relevance to detect and analyze changes accurately in the sympathovagal balance without having recourse to any a priori fixed high-pass/low-pass filters. [ABSTRACT FROM AUTHOR]

Discrimination in selection problems such as hiring or college admission is often explained by implicit bias from the decision maker against disadvantaged demographic groups. In this paper, we consider a model where the decision maker receives a noisy estimate of each candidate's quality, whose variance depends on the candidate's group—we argue that such differential variance is a key feature of many selection problems. We analyze two notable settings: in the first, the noise variances are unknown to the decision maker who simply picks the candidates with the highest estimated quality independently of their group; in the second, the variances are known and the decision maker picks candidates having the highest expected quality given the noisy estimate. We show that both baseline decision makers yield discrimination, although in opposite directions: the first leads to underrepresentation of the low-variance group while the second leads to underrepresentation of the high-variance group. We study the effect on the selection utility of imposing a fairness mechanism that we term the γ -rule (it is an extension of the classical four-fifths rule and it also includes demographic parity). In the first setting (with unknown variances), we prove that under mild conditions, imposing the γ -rule increases the selection utility—here there is no trade-off between fairness and utility. In the second setting (with known variances), imposing the γ -rule decreases the utility but we prove a bound on the utility loss due to the fairness mechanism. [ABSTRACT FROM AUTHOR]

A research area of increasing interest consists of studying the benefits of using spectral analysis to screen neurogenic erectile dysfunctions. Our hypothesis is that spectral analysis consists of a non-invasive and simple procedure to investigate such patients. Subjects were allocated into two groups: control, no erectile dysfunction (n = 17), and patients with erectile dysfunction (n = 15). RR intervals (RRI), systolic blood pressure, diastolic blood pressure and baroreflex sensitivity recordings were performed continuously in the supine position, followed by the seated position, and finally standing position. In the supine position, the control group had a higher RRI and a lower diastolic blood pressure. For frequency domain analyses of RRI in the supine position, the erectile dysfunction group had a higher normalized low-frequency (LF) index and low-frequency/high-frequency (LF/HF) ratio while showing a lower normalized HF index. In the seated position, the erectile dysfunction group presented a higher LF/HF ratio. Regarding systolic blood pressure, the erectile dysfunction group showed lower normalized LF and higher normalized HF indices only in the supine position. The α index in HF was lower in the erectile dysfunction group in the three positions. Spectral analyses of cardiac sympathovagal drive constitute a fruitful non-invasive approach to evaluate alterations in cardiovascular autonomic modulation in neurogenic erectile dysfunction patients. Copyright © 2010 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]