Guide in Designing an Asynchronous Performance-Centric Framework for Heterogeneous Microservices in Time-Critical Cybersecurity Applications. The BIECO Use Case

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Guide in Designing an Asynchronous Performance-Centric Framework for Heterogeneous Microservices in Time-Critical Cybersecurity Applications. The BIECO Use Case

Guide in Designing an Asynchronous Performance-Centric Framework for Heterogeneous Microservices in Time-Critical Cybersecurity Applications. The BIECO Use Case
Daniela Delinschi, Rudolf Erdei, Emil Pasca, Iulia Baraian, Oliviu Matei

Abstract. This article presents the architecture, design and validation of a microservice orchestration approach that improves the flexibility of heterogeneous microservice-based platforms. Improving user experience and interaction for time-critical applications are aspects that were primary objectives for the design of the architecture. Each microservice can provide its own embedded user interface component, also decentralising it and, in consequence, improving the loosely coupled approach to the architecture. Obtained results are promising, with high throughput and low response times. Also, a key finding was the introduction of benchmarking as a new step in the development lifecycle of performance-critical software components, with an example of how it can be applied within an Agile methodology. Further research is proposed to improve the results and raise the final technology readiness level of the system. Obtained results already make the approach a candidate and viable alternative to classical service composers.

Keywords: asynchronous communication, cybersecurity, distributed systems, edge computing, heterogeneous communication, heterogeneous services, loose coupling, message-based communication, service orchestration

Trend-Enabled Recommender System with Diversity Enhancer for Crop Recommendation

Trend-Enabled Recommender System with Diversity Enhancer for Crop Recommendation

The generalized traveling salesman problem (GTSP) is an extension of the classical traveling salesman
problem (TSP), and it is among the most researched combinatorial optimization problems due to its theoretical properties, complexity aspects, and real-life applications in various areas: location-routing problems, material flow design problem, distribution of medical supplies, urban waste collection management, airport selection and routing the courier airplanes, image retrieval and ranking, digital garment manufacturing, etc.

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Evaluation of Feature Selection Methods in Estimation  of Precipitation Based on Deep Learning Artificial  Neural Networks

Evaluation of Feature Selection Methods in Estimation of Precipitation Based on Deep Learning Artificial Neural Networks

Precipitation is the most important element of the water cycle and an indispensable element of water resources management. This paper aims to model the monthly precipitation in 8 precipitation observation stations. The effects and role of different feature weights pre-processing methods (Weight by deviation, Weight by PCA, Weight by correlation, and Weight by Support Vector Machine) on artificial intelligence modeling were investigated.

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A Comparison of different crossover operators in genetic algorithms for clusters shortest-path tree problem

A Comparison of different crossover operators in genetic algorithms for clusters shortest-path tree problem

The clustered shortest-path tree (CluSPT) problem is an extension of the classical shortest path problem, given a graph with the nodes partitioned into several mutually exclusive and collectively exhaustive clusters looks for a shortest-path spanning tree from a predefined source node to all the other nodes of the graph, with the property that every cluster should generate a connected subgraph.

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A comprehensive survey on the generalized traveling salesman problem

A comprehensive survey on the generalized traveling salesman problem

The generalized traveling salesman problem (GTSP) is an extension of the classical traveling salesman
problem (TSP), and it is among the most researched combinatorial optimization problems due to its theoretical properties, complexity aspects, and real-life applications in various areas: location-routing problems, material flow design problem, distribution of medical supplies, urban waste collection management, airport selection and routing the courier airplanes, image retrieval and ranking, digital garment manufacturing, etc.

read more

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