Development and training of LSTM models for control of virtual distributed systems using TensorFlow and Keras

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Development and training of LSTM models for control of virtual distributed systems using TensorFlow and Keras

Telezhenko Denys

PhD student of the Computer Science department V.N. Karazin Kharkiv National University, Ukraine

Scientific director: Tolstoluzka Olena

Doctor of Engineering Sciences,

Professor of Theoretical and Applied Systems Engineering Department V.N. Karazin Kharkiv National University, Ukraine

Summary

This paper explores the development and application of a long-short-term memory (LSTM) model for virtual distributed system (VDS) control optimization using the TensorFlow and Keras frameworks. In the context of the rapid development of information technologies, the effective management of VRS resources becomes important for maintaining the scalability and performance of cloud computing and big data. The author focuses on the use of LSTM, a recurrent neural network, which has proven to be effective in detecting dependencies in time series and processing sequential data, which is critical for predicting the behavior of complex systems such as VRS. The process includes collecting and normalizing historical resource usage data, designing an LSTM architecture, training the model using TensorFlow, and implementing it using Keras to simplify the development process. An important stage is the assessment of the model's effectiveness through training and validation losses, as well as the assessment of the generalization ability of the model on test data. The results showed that the LSTM model effectively optimizes the management of HRS resources, reducing costs and increasing productivity. Analyzing the dynamics of training and validation helps identify and resolve overtraining issues, improving the generalization ability of the model. The article also discusses the possibilities of further research and the implementation of the model in real conditions, emphasizing the potential of LSTM in the development of innovative solutions for information technology.

Keywords: Virtual distributed systems (VDS), resource optimization, LSTM (Long Short-Term Memory), TensorFlow, Keras, machine learning, neural networks. virtual distributed system keras

Introduction

In today's world, where technologies are developing rapidly, the need for effective resource management of virtual distributed systems (VDS) is becoming more and more urgent. VRS provides flexibility, scalability, and efficient use of resources, which are key components for the development of cloud computing, big data, and other modern IT infrastructures. The challenge is to ensure optimal allocation and use of these resources, minimizing costs and increasing productivity.

This paper focuses on the application of machine learning algorithms, particularly long-short-term memories (LSTM), to optimize the architecture of VRS. LSTMs are a type of recurrent neural networks (RNNs) that are able to detect dependencies in time series data and efficiently process sequential data, making them ideal for predicting the behavior of complex systems such as VRS.

Using the TensorFlow and Keras libraries, an LSTM model was developed that is capable of analyzing and predicting optimal HRS configurations based on historical resource usage data. TensorFlow provides a powerful deep learning environment with a wide range of tools for developing, training, and validating models, while Keras simplifies the implementation process with its high-level API.

This article details the LSTM model development process, including data preparation and processing, model architecture, training process, and performance evaluation methods. The obtained results are also analyzed, potential problems such as overtraining are identified, and strategies for their elimination are discussed.

The goal of this work is not only the development of an effective model for the management of VRS, but also the demonstration of the capabilities of modern machine learning technologies in solving practical problems in the field of information technology.

To implement the process of training an LSTM model in Python, which optimizes the architecture of virtual distributed systems, we will use the TensorFlow library and Keras to simplify the process of developing and training a neural network. Below is a code example that demonstrates the key steps in the training process: data preparation, building the LSTM model, setting up the training process, and training the model itself. [1]

The initial stage of training involves the collection and preparation of input data that reflect the state and configuration of the VRS. The data covers a wide range of parameters, including hardware specifications, hypervisor settings, virtual machine configurations, and management strategies. A key aspect is the normalization of the data to ensure its homogeneity, which helps to increase the effectiveness of the training. [2]

? import numpy as np

import tensorflow as tf

import pandas as pd

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import LSTM, Dense, Dropout

from tensorflow.keras.optimizers import Adam

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import MinMaxScaler

# Downloading system data from a CSV file

data = pd.read_csv('vds_data.csv')

# The data have features and a target variable to predict

features = data[['cpu_usage', 'memory_usage', 'disc_usage', 'network_usage']] target = data['system_prodactivity']

# Data normalization for effective LSTM training

scaler = MinMaxScaler()

features_scaled = scaler.fit_transform(features)

target_scaled = scaler.fit_transform(target.values.reshape(-1, 1))

# Separation of data into training and test sets

X_train, X_test, Y_train, Y_test = train_test_split(features_scaled, target_scaled, test_size=0.2, random_state=42)

# Data transformation for LSTM (time series generation if needed)

# Adapt the code to create the time series that suits your task

X_train = np.reshape(X_train, (X_train.shape[0], 1, X_train.shape[1]))

X_test = np.reshape(X_test, (X_test.shape[0], 1, X_test.shape[1]))

# X_train and Y_train can now be used to train an LSTM model

# X_test and Y_test are used to test the model

# Defining an LSTM model model = Sequential([

LSTM(64, activation='relu', input_shape=(10, 4), return_sequences=True),

Dropout(0.2),

LSTM(32, activation='relu', return_sequences=False),

Dropout(0.2),

Dense(3)

])

# Compilation of the model

model.compile(optimizer=Adam(learning_rate=0.01), loss='mean_squared_error')

# Model architecture derivation model.summary()

# Model training

history = model.fit(X_train, Y_train, epochs=50, batch_size=16, validation_split=0.2)

# Evaluation of the model on test data test_loss = model.evaluate(X_test, Y_test)

# Save the model

model.save('lstm_vrs_model.h5')

?

This code demonstrates an approach to training an LSTM model for a problem that can be analogous to optimizing the architecture of virtual distributed systems.

The performance of the trained LSTM model was evaluated on the basis of a separate test data set that did not participate in the training process. This allows you to objectively assess the model's ability to generalize learning to new data, minimizing the impact of overtraining. Using the evaluate function of the TensorFlow library, a quantitative indicator of the model error was obtained, which in this case is represented by the mean squared error (MSE). [3]

The analysis of LSTM model training and validation results in the context of virtual distributed systems includes several key aspects that are discussed in detail to evaluate the performance and generalization ability of the model:

Visualization of training history: plotting changes in loss values and accuracy metrics on training and validation data over epochs. This will help detect overtraining or undertraining of the model.

? import matplotlib.pyplot as plt

# Construction of a schedule of losses

# Create a new window for graphs

plt.figure(figsize=(12, 6))

# is used to plot several graphs in one window

plt.subplot(1, 2, 1)

plt.plot(history.history['loss'], label='Train Loss')

plt.plot(history.history['val_loss'], label='Validation Loss')

plt.title('Model Loss')

plt.xlabel('Epochs')

plt.ylabel('Loss')

plt.legend()

# If the story also contains an accuracy metric, you can add a graph for it

if 'accuracy' in history.history:

plt.subplot(1,2, 2)

plt.plot(history.history['accuracy'], label='Train Accuracy')

plt.plot(history.history['val_accuracy'], label='Validation Accuracy')

plt.title('Model Accuracy')

plt.xlabel('Epochs')

plt.ylabel('Accuracy')

plt.legend()

plt.tight_layout()

plt.show()

Result of code compilation is described on picture 1.

Pic. 1. Training and validation results

The graph is a visualization of the process of neural network training, where the changes in the amount of losses (loss) for the training and validation data sets during 50 epochs are displayed.

An epoch is one complete pass of all the training data through the neural network, which is used to update the model weights. During each epoch, the training algorithm presents the model's training data in a specific order, making predictions and adjusting weights based on prediction errors.

Complete data pass. An epoch covers one complete pass through all training data. This means that each sample in the training data set was presented to the model once per epoch.

Weight update. After each epoch, the model updates its weights in order to reduce the forecast error. The update of the weights depends on the loss function and the optimization algorithm.

Iterative process. The model is trained iteratively, where each epoch attempts to improve the model's predictive ability by reducing the discrepancy between the actual and predicted values.

Progress monitoring. In the graph, each point on the X-axis, representing an epoch, represents the state of the model after a full pass through the training data. This makes it possible to evaluate how the performance of the model changes with each epoch. [4]

On the graph, the length of the X-axis (number of epochs) allows you to visually assess how quickly the model learns and when signs of stabilization or overtraining begin to appear, as can be seen from how the loss values (on the Y-axis) change over time (epochs).

The horizontal axis (X-axis) shows training epochs from 1 to 50. The vertical axis (Y-axis) shows the loss values, which show how big the difference is between the model's predictions and the actual data.

The blue line represents the loss on the training dataset. It shows how over time the model learns better and better on the training data set, that is, the amount of losses decreases.

The orange line shows the loss on the validation set. It allows you to evaluate how well the model is able to generalize learning to new data that was not used during training.

The graph shows that both curves are decreasing, indicating the model's ability to reduce loss on both training and validation data. However, by analyzing the dynamics of the changes in the curves, it is possible to detect whether retraining is taking place, or whether the model reaches stabilization of losses on the validation data set.

If validation losses start to increase while training losses continue to decrease, this may indicate overtraining of the model. Ideally, both curves should show decreasing losses, while the validation losses should maintain a steady or very slow decreasing trend, indicating good generalization ability of the model.

Conclusions

The research results confirm that LSTM models are effective in forecasting and optimization of virtual distributed systems. The model demonstrated the ability to effectively analyze time dependencies and identify optimal ways of resource management. Using TensorFlow and Keras simplified the process of developing, training and validating an LSTM model. Keras' high-level APIs enabled rapid prototyping and testing of different architectures, while TensorFlow provided powerful tools for deep learning.

Analysis of training and validation processes revealed the importance of monitoring the dynamics of losses to prevent overtraining. The use of early stopping and cross-validation strategies helped to increase the generalizability of the model.

The study demonstrates the practical applicability of LSTM models in resource management of virtual distributed systems, offering an automated and optimized solution for managing complex IT infrastructures.

References:

[1 ] Dogacan Yilmaz, i. Esra Buyuktahtakin. (2023). Learning Optimal Solutions via an LSTM - Optimization Framework. Operations Research Forum, (4).

https://doi.org/10.48550/arXiv.2207.02937

[2] Zhu, Y., Zhang, W., Chen, Y. et al. (2019). A novel approach to workload prediction using attention-based LSTM encoder-decoder network in cloud environment. EURASIP Journal on Wireless Communications and networking, (274). https://doi.org/10.1186/s13638-019- 1605-z

[3] Ashawa, M., Douglas, O., Osamor, J. et al. (2023). Retraction Note: Improving cloud efficiency through optimized resource allocation technique for load balancing using LSTM machine learning algorithm. Journal of Cloud Computing, (12). https://doi.org/10.1186/s13677-023-00562-z

[4] Sayed, S.A., Abdel-Hamid, Y. & Hefny, H.A. (2023). Artificial intelligence-based traffic flow prediction: a comprehensive review. Journal of Electrical Systems and Information Technology, (10). https://doi.org/10.1186/s43067-023-00081-6

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