ENHANCING SLA RELIABILITY IN TELCO NETWORKS THROUGH LSTM-BASED TIME SERIES PREDICTIONS

• Main Author: Farros Alfarobby, Daffa
• Format: Final Project
• Language: Indonesia
• Online Access: https://digilib.itb.ac.id/gdl/view/87820
• Institution: Institut Teknologi Bandung

This study delves into the integration of Long Short-Term Memory (LSTM) networks and Genetic Algorithms (GA) to enhance Service Level Agreements (SLAs) in telecommunication infrastructure, with a particular focus on Indonesia’s 3T regions (Tertinggal, Terdepan, dan Terluar). These regions, characterized by their remote and underserved nature, face numerous challenges in achieving reliable and efficient telecommunications services. The proposed framework seeks to address these challenges by leveraging advanced computational techniques to optimize SLA performance and ensure equitable digital access. The research highlights the complementary roles of LSTMs and GAs in solving complex telecommunication issues. Neural Networks, specifically Long Short-Term Memory (LSTM) models, are employed for their ability to process and analyze vast amounts of time-series data. By identifying patterns and anomalies in network performance, LSTMs provide actionable insights that can guide decision-making processes. Genetic Algorithms, on the other hand, are utilized for their optimization capabilities. By simulating natural selection and evolution, GAs identify the most efficient configurations and parameters for maintaining SLA standards, even under challenging conditions. A key innovation of this study is the integration of these technologies within a cloud-based platform. Cloud computing offers scalability, flexibility, and accessibility, making it an ideal environment for deploying the proposed framework. By utilizing cloud resources, the system can handle the extensive computational demands of LSTM training and GA optimization while ensuring real-time monitoring and analysis. This approach not only enhances the efficiency of SLA management but also reduces the dependency on manual intervention, which is often time-consuming and prone to errors. v The framework’s implementation involves several critical steps. First, historical network performance data from Indonesia’s 3T regions are collected and preprocessed. This data is then used to train the LSTM model, which learns to predict future network conditions based on past trends. The predictions generated by the LSTM model serve as inputs for the Genetic Algorithm, which evaluates various SLA configurations and selects the optimal solutions. This iterative process ensures continuous improvement in SLA performance, adapting to changing conditions and emerging challenges. The results of this study underscore the potential of combining LSTMs and GAs to transform SLA management in telecommunications. In preliminary testing, the framework demonstrated significant improvements in identifying performance bottlenecks, reducing troubleshooting times, and ensuring compliance with SLA standards. Moreover, the system’s ability to operate autonomously and adaptively makes it particularly valuable for remote and underserved areas, where technical expertise and resources may be limited. Beyond its technical merits, this research has broader implications for digital equity and inclusion. By enhancing the reliability and efficiency of telecommunications infrastructure in the 3T regions, the proposed framework contributes to bridging the digital divide and fostering socioeconomic development. Improved SLA monitoring and management enable better access to essential services such as education, healthcare, and e-commerce, empowering communities to participate fully in the digital economy. Future work will focus on further refining the framework to accommodate a wider range of telecommunication scenarios and exploring its scalability for nationwide implementation. Additional research will also investigate the integration of other machine learning models and optimization techniques to enhance system performance further. Furthermore, collaborative efforts with policymakers and industry stakeholders will be essential to ensure the practical applicability and sustainability of the proposed solution. In conclusion, this study demonstrates the transformative potential of integrating Neural Networks and Genetic Algorithms for SLA enhancement in telecommunication infrastructure. By addressing the unique challenges of Indonesia’s 3T regions, the proposed framework offers a scalable and adaptive solution that not only improves technical performance but also promotes digital inclusivity and equity. This work represents a significant step toward leveraging advanced technologies to create a more connected and equitable world.