Vol. 42 · No. 1

One of the biggest challenges while migrating from a monolith architecture to a microservice architecture is to define a proper communication technology. In monolith applications, communication between components is performed using the in-process method or function calls, while different communication methods have to be established to achieve the same functionality in a microservice architecture. A microservices-based application is a distributed system running on multiple processes or services. Therefore, microservices must interact using inter-process communication technologies. This research aims to evaluate synchronous and asynchronous communication technologies and determine particular cases for their application while decomposing monolith into cloud-native applications. Five communication technologies, such as HTTP Rest, RabbitMQ, Kafka, gRPC, and GraphQL, have been evaluated and compared by proposed evaluation criteria. The advantages and disadvantages of each communication technology were identified in the context of microservices architecture.

Cloud computing is a distributed computing model in which access is based on demand. A cloud computing environment includes a wide variety of resource suppliers and consumers. Hence, efficient and effective methods for task scheduling and load balancing are required. This paper presents a new approach to task scheduling and load balancing in the cloud computing environment with an emphasis on the cost-efficiency of task execution through resources. The proposed algorithms are based on the fair distribution of jobs between machines, which will prevent the unconventional increase in the price of a machine and the unemployment of other machines. The two parameters Total Cost and Final Cost are designed to achieve the mentioned goal. Applying these two parameters will create a fair basis for job scheduling and load balancing. To implement the proposed approach, learning automata are used as an effective and efficient technique in reinforcement learning. Finally, to show the effectiveness of the proposed algorithms we conducted simulations using CloudSim toolkit and compared proposed algorithms with other existing algorithms like BCO, PES, CJS, PPO and MCT. The proposed algorithms can balance the Final Cost and Total Cost of machines. Also, the proposed algorithms outperform best existing algorithms in terms of efficiency and imbalance degree.

Smart grid is an advanced electrical grid that enables more efficient distribution of electricity. It counters many of the problems presented by renewable energy sources such as variability in production through techniques like load forecasting and dynamic pricing. Smart grid generates massive amounts of data through smart meters, this data is used to forecast future load to adjust distribution. To process all this data, big data analysis is necessary. Most existing schemes use Apache Hadoop for big data processing and various techniques for load forecasting that include methods based on statistical theory, machine learning and deep learning. This paper proposes using Apache Spark for big data analysis and a modified version of the transformer model for forecasting load profiles of households. The modified transformer model has been tested against several state-of-the-art machine learning models. The proposed scheme was tested against several baseline and state-of-the-art machine learning models and evaluated in terms of the RMSE, MAE, MedAE and R2 scores. The obtained results show that the proposed model has better performance in terms of RMSE and R2 which are the preferred metrics when evaluating a regression model on data with a large number of outliers.

In multi-domain networks, the efficiency of path computation becomes more and more important. The Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) is a recently investigated problem where its objective is to determine the effective routing path between two nodes that traverses every domain at most once. IDPC-NDU is NP-Hard, so the approximation approaches are suitable to deal with this problem for large instances. Multifactorial Evolutionary Algorithm (MFEA) is an emerging research topic in the field of evolutionary computation that can efficiently tackle multiple optimization problems at the same time. This study proposed an approach based on the combination of the Adaptive Multifactorial Evolutionary Algorithm (dMFEA-II) and Dijkstra algorithm for solving IDPC-NDU. The encoding and evaluating methods based on the permutation representation are also introduced, and the new individual representation is always to produce valid solutions. The proposed algorithm is evaluated on two types of instances. Simulation results demonstrate the superior performance of the proposed algorithm in comparison with the existing algorithms in terms of the quality of the solution.

Many of the deep learning solutions for time-series forecasting reported in the literature include complex neural networks that may not be directly employed by the practitioner in the field. In this study, we demonstrate how the standard deep neural network types, convolutional neural network (CNN) and long short-term memory (LSTM) network can be applied in the field of time-series forecasting. This study consists of two parts. The first part is to compare CNN and LSTM models with classical methods like Random Forest (RF) and ARIMA for the univariate electric power consumption task. The second part is to use the best performing model from the first part in the hybrid model and perform data fusion with the newly built hybrid model for the electric power consumption forecasting task. CNN and LSTM models outperform traditional methods when their performances are evaluated on the univariate electric power consumption data of Illinois, USA. We also illustrate the use of hybrid deep learning models composed of standard CNN and LSTM for data fusion with the aim of time-series forecasting. When the hybrid models are applied to the fused data of the electric power consumption data and the multivariate weather data of Illinois, USA, the forecasting performance is improved compared to that when only univariate data is used.

This paper presents the construction of Run Length Limited (RLL) Error Control Codes (ECCs) from four Low Density Parity Check (LDPC) Codes specified by Consultative Committee for Space Data Systems (CCSDS). The obtained RLL-ECCs present a practical alternative to the CCSDS codes with pseudo-randomizers. Their advantage is that the maximal runlengths of equal symbols in their codeword sequences are guaranteed, which is not the case if the common approach with pseudo-randomizers is used. The other advantages are that no additional redundancy is introduced into encoded codewords and that the encoding and decoding procedures of the original error control CCSDS codes do not have to be modified in the following cases. In the first case if hard decoding is used and the transmission channel can be modeled as a Binary Symmetric Channel (BSC) or in the second case if soft decoding and coherent Binary Phase Shift Keying (BPSK) modulation is used and the appropriate transmission channel model is an Additive White Gaussian Noise (AWGN) channel.

In this paper, we present an evolution of a novel approach for evaluating semantic similarity in a taxonomy, based on the well-known notion of information content. Such an approach takes into account not only the generic sense of a concept but also its intended sense in a given context. In this work semantic similarity is evaluated according to a refined relatedness measure between the generic sense and the intended sense of a concept, leading to higher correlation values with human judgment with respect to the original proposal.

Vision-based underwater object detection technology is a hot topic of current research. In order to address the issues of low accuracy and high missed rate of marine life detection, an object detection algorithm called MDM-YOLO (Marine Detection Model with YOLO) for marine organisms based on improved YOLOv4 is proposed. To improve the network's capacity for feature extraction, a multi-branch architecture CSBM is integrated into the backbone. Based on this, the feature fusion structure introduces shuffle attention to reinforce the focus on important information. The experimental results demonstrate that the MDM-YOLO algorithm increases the mean average precision (mAP) by 2.31

The large amount of computing resources required for the simulation of complex natural processes demands a thorough analysis of the efficiency of the calculations and the conditions that influence it. This study investigates computing aspects of fire simulation conducted on a compute cluster. Current fire simulators based on principles of computational fluid dynamics are capable to realistically model majority of complex phenomena related to fire. Fire simulations are highly computationally demanding itself, however, they often lead to extensive parametrical studies requiring high performance computing systems. Smoke stratification and visibility during fire in a road tunnel with two emergency lay-bys are investigated by parametrical study comprising of 24 fire scenarios with the tunnel geometry modifications and various heat release rates and fire locations. Main tendencies of smoke spread in the downstream lay-by are identified and their mutual interactions are analysed. The simulation efficiency of particular simulations is analysed and the reasons of their varied elapsed times are investigated. The analysis indicates that the main reason of this variability are different jet fans velocities influenced by simulation scenario settings.