Dask parallel implementation is being considered in the toolbox. PGFLibPy’s has 25 Python files and 18 datasets. The toolbox activity is used for binary and multiclassification datasets to classify UCI. PGFLibPy was used to build a model of the UCI dataset that reliably predicts regression values. PGFLibPy is a Python-based machine learning framework for classification and regression problems.
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It is also easily scalable to multi-core CPUs. Four concurrent CPUs are formed to parallelize the GF, and each executes eight threads. The regression and classification problems are solved using PGF. It aids in resolving kernel tricks that are difficult to predict using conventional optimization approaches. For efficient hyper-scale GFs, a hybrid parallel approach based on CPU architecture Parallel GF (PGF) is proposed. Genetic folding (GF) is a robust evolutionary optimization algorithm. The performance is compared with two other heuristic algorithms and with an exhaustive search algorithm, introduced as benchmarks, showing the benefits of the selected solution in terms of performance, flexibility and complexity. We propose to solve the problem through a Genetic Algorithm able to approach the optimal solution but with reduced complexity and execution time. The proposed approach jointly considers Radio Access Network (RAN) and Core Network (CN) functions and, differently from other approaches, introduces an option able to bias the function placement depending on the service requirements, allowing a fast-and-easy operator-side deployment of the network functions. Gaining from the NFV, Network Slicing and Edge-to-Cloud continuum paradigms, we propose a new network function allocation problem for multi-service 5G networks, able to deploy network functions on a distributed computing environment depending on the service requests. In particular, thanks to the introduction of cloud-native technologies, based on the usage of containerization and microservice technologies, the virtual network functions (VNFs) deployment and their orchestration is an easy operation, allowing the on-the-fly network configuration. The advantages of NFV, in the network slicing context, are even more evident in distributed computing environments, such as the edge-to-cloud continuum, recently introduced for enabling a flexible deployment of multiple functions. The idea is that physical communication and computing resources are sliced in multiple end-to-end logical networks, each one tailored to best support a specific service. Boosted by NFV, the concept of network slicing is gaining great attention in 5G networks.
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MATLAB PDF TOOLBOX SOFTWARE
In particular, Network Function Virtualization (NFV) is a recently introduced technology that enables a software implementation of different network functions exploiting virtualization techniques, hence, enabling their flexible deployment upon system requirements. The 5G communication standard is characterized by an increased softwarization, allowing a higher flexibility able to cope with different requirements and services.