Authors
Keywords
Abstract
Abstract: This study investigates the performance of container migration processes using a set of input parameters, including memory usage, network bandwidth, and validation score. By analyzing their impact on migration time, the study aims to optimize migration strategies and improve system efficiency.
Research Significance: Efficient container migration is critical for maintaining operational continuity and minimizing downtime in cloud and virtualized environments. Understanding how memory usage, network bandwidth, and validation accuracy affect migration time can guide better resource allocation and process optimization.
Methodology: Algorithm Analysis: The study employs regression-based analysis to evaluate the relationships between input parameters and migration time. Linear Regression and Support Vector Regression (SVR) are applied to model the impact of memory, bandwidth, and validation score on the efficiency of migration, allowing performance prediction and optimization. Alternative Input Parameters: Memory Usage (MB): Measures RAM consumed during migration.
Network Bandwidth (Mbps): Captures the rate of data transfer between source and target. Validation Score or Success Rate (%): Evaluates the accuracy and reliability of the migration.
Evaluation Parameter (Output Parameter) : Migration Time (seconds): Represents the total duration of the migration process, used as the primary metric to assess efficiency.
Result: The analysis demonstrates that higher memory consumption and network bandwidth generally correlate with reduced migration time, while the validation score influences the reliability of outcomes. Regression models can predict migration time effectively, providing insights for optimizing migration strategies.
Keywords: Container Migration, Migration Time, Memory Usage, Network Bandwidth, Validation Score, Regression Analysis, Performance Optimization
