Big data storing and processing require specialized distributed services and significant amounts of computing resources. Cloud environments allow one to deploy virtual clusters with the requested characteristics and i...
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ISBN:
(数字)9781665412919
ISBN:
(纸本)9781665448215
Big data storing and processing require specialized distributed services and significant amounts of computing resources. Cloud environments allow one to deploy virtual clusters with the requested characteristics and install the necessary services, but manual configuration is too laborious and inefficient. To use IaaS clouds one should be a highly knowledgeable in the field of system administration. This article presents a Michman tool, which allows on-demand deployment of clusters with a set of services in the cloud IaaS, stores information about deployed systems and provides management of their life cycle. The tool is implemented as a service with a system of user groups, REST API carries access to the system.
There are many research papers devoted to the state identification problem of finite state machines (FSMs) which are widely used for analysis of discrete event systems. A deterministic complete reduced FSM always has ...
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The Problem of the Unknown Component: Theory and Applications addresses the issue of designing a component that, combined with a known part of a system, conforms to an overall specification. The authors tackle this pr...
ISBN:
(纸本)9781489973948
The Problem of the Unknown Component: Theory and Applications addresses the issue of designing a component that, combined with a known part of a system, conforms to an overall specification. The authors tackle this problem by solving abstract equations over a language. The most general solutions are studied when both synchronous and parallel composition operators are used. The abstract equations are specialized to languages associated with important classes of automata used for modeling systems. The book is a blend of theory and practice, which includes a description of a software package with applications to sequential synthesis of finite state machines. Specific topologies interconnecting the components, exact and heuristic techniques, and optimization scenarios are studied. Finally the scope is enlarged to domains like testing, supervisory control, game theory and synthesis for special omega languages. The authors present original results of the authors along with an overview of existing ones.
The numerical method based on the discontinuous Galerkin (DG) approach for perfect inviscid compressible gas dynamics modelling is developed. The approximate Riemann solvers in combination with gas dynamics equations ...
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Analysis of social network user profiles plays an important role in various applications such as recommender systems and marketing services. In this paper we consider the task of profile prediction given a sample of a...
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Analysis of social network user profiles plays an important role in various applications such as recommender systems and marketing services. In this paper we consider the task of profile prediction given a sample of a social graph. We introduce a new node property named context specificity, which allows to enhance node attribute prediction methods by emphasizing more useful nodes and edges. Particularly, we show how to apply it to several existing methods: label propagation, classification based on node embeddings, graph convolutional neural network. Our experiments prove that context specificity feature improves methods quality in most considered tasks. Additionally, we suggest a new supervised method based on attribute values distributions that is competitive with state of the art approaches. We also describe and share two new datasets, representing a sample of VK social network.
A method for establishing a self-similar dependence of the bottom surface on time and spatial coordinate is proposed. The mean bottom tangential stresses are determined for a series of self-similar shapes of the botto...
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Recently, Fuzzing is one of the most successful techniques to expose bugs in software. For testing large programs or large codebase with many features and entry-points, the creation of fuzz-targets remains a big chall...
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ISBN:
(纸本)9781665423281
Recently, Fuzzing is one of the most successful techniques to expose bugs in software. For testing large programs or large codebase with many features and entry-points, the creation of fuzz-targets remains a big challenge. In this paper, we introduce Futag – an automated fuzz target generator for testing software libraries. This approach uses static analysis to collect information about source code: data type definitions, dependencies of types, definitions of functions, etc. Futag has found many vulnerabilities in latest version of popular libraries such as: libopenssl, libpng, libjson-c, liblxml2.
Nowadays specialized distributed services and sig-nificant amounts of computing resources are used for solving tasks in Big Data processing and analysis. Cloud environments allow people to create virtual clusters with...
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ISBN:
(纸本)9781665423311
Nowadays specialized distributed services and sig-nificant amounts of computing resources are used for solving tasks in Big Data processing and analysis. Cloud environments allow people to create virtual clusters with necessary characteris-tics and tune specialized software for planning jobs and resource allocation. Slurm belongs to such systems class. Unfortunately, lots of problems appear during manual setup and Slurm usage in cloud infrastructure. The article offers the solution to the problem of automating deployment of the Slurm cluster in cloud infrastructure for development and testing purposes. User acquires access using REST API request to get ready for use virtual cluster with preinstalled and preconfigured Slurm cluster. This work also includes analysis for Slurm/HPC clusters effi-ciency running in Openstack environment using virtual machines interconnected with an overlay network compared to classical bare-metal HPC clusters.
In this work, we develop software based on porousMultiphaseFoam library in OpenFOAM package to study enhanced oil recovery methods, and to study the influence of these methods on the oil recovery factor. New models of...
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In this work, we develop software based on porousMultiphaseFoam library in OpenFOAM package to study enhanced oil recovery methods, and to study the influence of these methods on the oil recovery factor. New models of relative phase permeability and capillary pressure were implemented, and their comparative analysis was performed. The code was verified using the analytical approach. A new solver for simulating compressible flow was developed.
Reputation and competitiveness of both mobile applications and mobile operating systems depend on their quality. Developers are using various techniques to ensure high quality. Recently, exploratory testing approaches...
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Reputation and competitiveness of both mobile applications and mobile operating systems depend on their quality. Developers are using various techniques to ensure high quality. Recently, exploratory testing approaches have been gaining significant attention in this context. However, these approaches mostly do not consider one major non-functional requirement that affects quality – performance issues. Performance issues, such as sluggish UI or extensive battery consumption, are tightly connected to inefficient use of device resources. Detecting these issues is a non-trivial task. In this paper we present a novel approach to detect anomalous device resource usage and hence potential performance issues. Our approach is integrated with exploratory testing framework and uses information about previously executed test runs to build the expected resource usage model. The underlying model represents resource usage data as a multidimensional time series and is able to detect anomalous time intervals. We integrate our approach with exploratory testing tool for Android and empirically evaluate it on a set of real-world applications with injected performance issues. Our results show that suggested approach can be successfully applied to detect anomalous device resource usage and potential performance regressions.
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