HTTP Application Performance Monitoring

Cílem této bakalářské práce bylo vytvořit řešení pro monitorování a analýzu síťové výkonnosti HTTP serverů s využitím frameworku Nemea a NetFlow záznamů. Pro tento účel jsem vytvořil modul ve frameworku Nemea, který filtruje, rozebírá a ukláda NetFlow záznamy obohacené o informace z HTTP pluginu ve flow exportéru. Následné bylo potřebné vytvořit webové rozhraní založené na frameworku Django, pro zobrazení různych štatistík, které může užívatel využít na zjištení problému s monitorovanými servery. Výsledkom mé práce je produkt, který demonstruje možnost využití systému Nemea na pasívní monitorování HTTP servrů.Goal of this bachelor thesis was to create solution for monitoring and analysis of network performance of HTTP server using Nemea framework and NetFlow data. For this purpose, I've created Nemea module for filtering, parsing and saving NetFlow data enhanced by informations gained from HTTP plugin on exporter. For analysis and user interface, webpage based on Django framework was created, used for displaying statistics that are useful for users in order to reveal problems with monitored servers. Result of my work is product, which is demonstrating possibility of using of Nemea system for passive monitoring of HTTP servers.

A general approach to posterior contraction in nonparametric inverse problems

In this paper we propose a general method to derive an upper bound for the contraction rate of the posterior distribution for nonparametric inverse problems. We present a general theorem that allows us to derive con- traction rates for the parameter of interest from contraction rates of the related direct problem of estimating transformed parameter of interest. An interesting aspect of this approach is that it allows us to derive con- traction rates for priors that are not related to the singular value decomposition of the operator. We apply our result to several examples of linear inverse problems, both in the white noise sequence model and the nonparametric regression model, using priors based on the singular value decomposition of the operator, location-mixture priors and splines prior, and recover minimax adaptive contraction rates

Relational semantics of linear logic and higher-order model-checking

In this article, we develop a new and somewhat unexpected connection between higher-order model-checking and linear logic. Our starting point is the observation that once embedded in the relational semantics of linear logic, the Church encoding of any higher-order recursion scheme (HORS) comes together with a dual Church encoding of an alternating tree automata (ATA) of the same signature. Moreover, the interaction between the relational interpretations of the HORS and of the ATA identifies the set of accepting states of the tree automaton against the infinite tree generated by the recursion scheme. We show how to extend this result to alternating parity automata (APT) by introducing a parametric version of the exponential modality of linear logic, capturing the formal properties of colors (or priorities) in higher-order model-checking. We show in particular how to reunderstand in this way the type-theoretic approach to higher-order model-checking developed by Kobayashi and Ong. We briefly explain in the end of the paper how his analysis driven by linear logic results in a new and purely semantic proof of decidability of the formulas of the monadic second-order logic for higher-order recursion schemes.Comment: 24 pages. Submitte

Bayesian inverse problems with partial observations

We study a nonparametric Bayesian approach to linear inverse problems under discrete observations. We use the discrete Fourier transform to convert our model into a truncated Gaussian sequence model, that is closely related to the classical Gaussian sequence model. Upon placing the truncated series prior on the unknown parameter, we show that as the number of observations $n\rightarrow\infty,$ the corresponding posterior distribution contracts around the true parameter at a rate depending on the smoothness of the true parameter and the prior, and the ill-posedness degree of the problem. Correct combinations of these values lead to optimal posterior contraction rates (up to logarithmic factors). Similarly, the frequentist coverage of Bayesian credible sets is shown to be dependent on a combination of smoothness of the true parameter and the prior, and the ill-posedness of the problem. Oversmoothing priors lead to zero coverage, while undersmoothing priors produce highly conservative results. Finally, we illustrate our theoretical results by numerical examples.Comment: 22 pages, 2 figure