Computing and Informatics (E-Journal - Institute of Informatics, SAS, Bratislava)
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Knowledge Acquisition Approaches for Virtual Machine Migration in Cloud Computing
Get PDFIn recent years, the utilization of Cloud computing services has significantly increased, placing a heightened demand on the workload, computational infrastructure, storage, and communication networks managed by datacenters. This surge has prompted researchers to enhance Cloud performance, focusing on minimizing execution time and computational costs. To address this challenge, efficient scheduling strategies involve the allocation of Cloud workload across different datacenters through the migration of virtual machines. This article examines and compares three methodologies to tackle this issue: one based on an Adaptive Neuro-Fuzzy Inference System, another utilizing a Swarm Fuzzy System, and a third one employing a Genetic Algorithm. The study evaluates their effectiveness for workflow scheduling using a CloudSim-based simulator in terms of makespan and computational costs. Results reveal that the neuro-fuzzy system outperforms the fuzzy and genetic systems regarding makespan in Montage and CyberShake environments. It demonstrates a computational cost advantage, achieving reductions of 7.01 % and 6.33 % for KASIA and 10.74 % and 8.86 % for Pittsburgh in Montage and CyberShake, respectively. Furthermore, it surpasses the KASIA system by 50 % and Pittsburgh by 37.5 % in terms of the number of rule base evaluations
Power Consumption Forecasting by Hybrid Deep Architectures with Data Fusion
Get PDFMany 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
Integration of a Contextual Observation System in a Multi-Process Architecture for Autonomous Vehicles
Get PDFWe propose a software layered architecture for autonomous vehicles whose efficiency is driven by pull-based acquisition of sensor data. This multiprocess software architecture, to be embedded into the control loop of these vehicles, includes a Belief-Desire-Intention agent that can consistently assist the achievement of intentions. Since driving on roads implies huge dynamic considerations, we tackle both reactivity and context awareness considerations on the execution loop of the vehicle. While the proposed architecture gradually offers 4 levels of reactivity, from arch-reflex to the deep modification of the previously built execution plan, the observation module concurrently exploits noise filtering and introduces frequency control to allow symbolic feature extraction while both fuzzy and first order logic management are used to enforce consistency and certainty over the context information properties. The presented use-case, the daily delivery of a network of pharmacy offices by an autonomous vehicle taking into account contextual (spatio-temporal) traffic features, shows the efficiency and the modularity of the architecture, as well as the scalability of the reaction levels
Evolution-by-Coevolution of Neural Networks for Audio Classification
Get PDFNeural networks are increasingly used in recognition problems, including static and moving images, sounds, etc. Unfortunately, the selection of optimal neural network architecture for a specific recognition problem is a difficult task, which often has an experimental nature. In this paper we present the use of evolutionary algorithms to obtain optimal architectures of neural networks used for audio sample classification. We extend the Pytorch DNN Evolution tool implementing co-evolutionary algorithms which create groups of neural networks that solve a given problem with a certain accuracy, with the support for problems in which training data consists of audio samples. In this paper we use the co-evolutionary approach to solve a sample sound classification problem. We describe how the sound data was prepared for processing with the use of the Mel Frequency Cepstral Coefficients (MFCC). Next we present the results of experiments conducted with the AudioMnist dataset. The obtained neural network architectures, whose classification accuracy is comparable to the classification accuracy attained by the AlexNet neural network, and their implications are discussed
The Evaluation of Microservice Communication While Decomposing Monoliths
No full textOne 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
PointVotes: A Deep Learing Point Cloud Model for Tire Bubble Defect Detection
Get PDFIn order to eliminate the hidden dangers caused by tire bubble defects, considering that the two-dimensional technology is sensitive to light, the 3D point cloud technology is used to obtain the tire surface morphology. This paper proposes a 3D point cloud network model named PointVotes, a point based target detection method. The designed structural framework includes: the fusion sampling layer, the voting layer and the proposal refinement layer. By observing the spatial characteristics of the detected target, a new point sampling method named C-farthest point sampling (C-FPS) is proposed. Combining with the fusion sampling strategy, the FPS and the C-FPS are sampled in a certain proportion. It solves the problem that the proposal box cannot be generated due to less available prospect information when generating suggestions for small targets. The network model uses Set Abstraction layers in multiple PointNet++ to extract features, arranges and combines features of different scales, forms high-dimensional features of points and votes, judges whether there are bubble defects through classification, and then generates proposals and regression to the prediction frame. Experiment results show that the mean average precision of the model can reach 82.8 % with a detection time of 0.12 s
Modeling and Analyzing User Behavior Risks in Online Shopping Processes Based on Data-Driven and Petri-Net Methods
Get PDFWith the rapid spread of e-commerce and e-payment, the increasing number of people choose online shopping instead of traditional buying way. However, the malicious user behaviors have a significant influence on the security of users' accounts and property. In order to guarantee the security of shopping environment, a method based on Complex Event Process (CEP) and Colored Petri nets (CPN) is proposed in this paper. CEP is a data-driven technology that can correlate and process a large amount of data according to Event Patterns, and CPN is a formal model that can simulate and verify the specifications of the online shopping processes. In this work, we first define the modeling scheme to depict the user behaviors and Event Patterns of online shopping processes based on CPN. The Event Patterns can be constructed and verified by formal methods, which guarantees the correctness of Event Patterns. After that, the Event Patterns are translated into Event Pattern Language (EPL) according to the corresponding algorithms. Finally, the EPLs can be inserted into the complex event processing engine to analyze the users' behavior flows in real-time. In this paper, we validate the effectiveness of the proposed method through case studies
Dynamic Matching Algorithm of Human Resource Allocation Based on Big Data Mining
Get PDFIn order to ensure the dynamic matching effect of human resources allocation and improve the accuracy and efficiency of dynamic matching of human resources allocation, a dynamic matching algorithm of human resources allocation based on big data mining is studied. Analyze the meaning and function of big data mining, and explain the common analysis principles of big data mining. The information entropy is selected as the basis for measuring human resource allocation, the human resource allocation is extracted, and the similarity of human resource allocation is calculated using the Huasdorff similarity method based on time interpolation. According to the Apriori algorithm and FP-Growth classification algorithm, the human resource allocation is classified and mined, and the K-Means clustering algorithm is used to realize the dynamic matching of human resource allocation. The experimental results show that the proposed algorithm has better dynamic matching effect of human resources allocation, and can effectively improve the accuracy and efficiency of dynamic matching of human resources allocation
MOOA-CSF: A Multi-Objective Optimization Approach for Cloud Services Finding
Get PDFCloud computing performance optimization is the process of increasing the performance of cloud services at minimum cost, based on various features. In this paper, we present a new approach called MOOA-CSF (Multi-Objective Optimization Approach for Cloud Services Finding), which uses supervised learning and multi-criteria decision techniques to optimize price and performance in cloud computing. Our system uses an artificial neural network (ANN) to classify a set of cloud services. The inputs of the ANN are service features, and the classification results are three classes of cloud services: one that is favorable to the client, one that is favorable to the system, and one that is common between the client and system classes. The ELECTRE (ÉLimination Et Choix Traduisant la REalité) method is used to order the services of the three classes. We modified the genetic algorithm (GA) to make it adaptive to our system. Thus, the result of the GA is a hybrid cloud service that theoretically exists, but practically does not. To this end, we use similarity tests to calculate the level of similarity between the hybrid service and the other benefits in both classes. MOOA-CSF performance is evaluated using different scenarios. Simulation results prove the efficiency of our approach.