Optimizing Software Failure Prediction in Cloud Systems through Hybrid Feature Selection and Tuned Random Forest

Problem Definition

The domain of cloud-based systems faces several limitations and challenges in terms of accurately and efficiently predicting failures. Existing models have made progress in this area but still fall short in various aspects. The complexity and scale of cloud infrastructures pose difficulties, alongside the variability and intricacy of cloud workloads. Timely and reliable fault detection is a key issue that needs to be addressed. Another major problem with existing models is the presence of high false-positive or false-negative rates, slow convergence rates, and the inability to effectively handle diverse and dynamic cloud environments.

Given these constraints, there is a critical need to develop enhanced software failure prediction models that can overcome these challenges and ultimately improve the reliability, availability, and performance of cloud-based services.

Objective

The objective of this project is to develop enhanced software failure prediction models for cloud-based systems by integrating the Yellow Saddle Goat Fish algorithm and Grasshopper Optimization algorithm. This hybrid approach aims to improve classification purity values, enhance the performance of artificial neural networks, and optimize the prediction of software failures. By reducing system complexity, improving feature selection, and optimizing hyperparameters, the project seeks to address the challenges faced in accurately predicting failures in cloud environments and ultimately enhance the reliability and performance of cloud-based services.

Proposed Work

In this project, the focus is on developing more accurate and efficient techniques for identifying and predicting failures in cloud-based systems. The existing models have shown progress in this area, but there are still challenges related to the complexity and scale of cloud infrastructures, variability of workloads, and the need for timely fault detection. The objective of this project is to propose a hybrid integration of the Yellow Saddle Goat Fish algorithm and Grasshopper Optimization algorithm to select features for an artificial neural network. By enhancing the classification purity values using a random forest classifier and a modified Grasshopper Optimization algorithm for parameter tuning, the aim is to improve software failure prediction models and enhance the performance of cloud-based services. The proposed work involves implementing the Hybrid YSGA-GOANet technique on processed data to extract only the most relevant attributes, thereby reducing system complexity and improving overall performance.

To enhance the classification rate, the addition of the Random Forest algorithm is considered, with its hyperparameters optimized using the hybrid GOA-HBA optimization algorithms. By combining the strengths of two optimization techniques, the GOA-HBA approach efficiently searches the hyperparameter space to find optimal or near-optimal configurations. This hybrid approach improves the model's ability to capture complex relationships within the data, increase its purity value, and optimize its performance for specific tasks. Through this novel methodology, the project aims to address the existing challenges in software failure prediction models and contribute towards enhancing the reliability and performance of cloud-based services.

Application Area for Industry

This project's proposed solutions can be applied across various industrial sectors such as banking and finance, healthcare, e-commerce, and telecommunications, among others. Industries in these sectors face the common challenge of ensuring the reliability, availability, and performance of their cloud-based systems. By implementing the Hybrid YSGA-GOANet technique and incorporating the Random Forest algorithm with optimized hyperparameters, organizations can proactively identify and predict failures in their cloud infrastructures. This approach reduces the complexity of systems, improves classification rates, and enhances the model's ability to capture complex relationships within the data. The benefits of implementing these solutions include improved fault detection, reduced false-positive or false-negative rates, faster convergence rates, and better adaptability to diverse and dynamic cloud environments.

Overall, the project's solutions can significantly enhance the operational efficiency and reliability of cloud-based services in various industrial domains.

Application Area for Academics

The proposed project has the potential to greatly enrich academic research, education, and training in the field of cloud-based systems and software failure prediction. By addressing the existing challenges in this area, such as the complexity and scale of cloud infrastructures, variability of cloud workloads, and the need for timely fault detection, the project can contribute to the advancement of knowledge and understanding in this important domain. The use of the Hybrid YSGA-GOANet technique to extract important attributes from data, along with the incorporation of the Random Forest algorithm optimized by hybrid GOA-HBA optimization algorithms, presents an innovative approach to improving classification rates and model performance. Researchers, MTech students, and PHD scholars in the field can benefit from the code and literature of this project to explore new research methods, simulations, and data analysis techniques within educational settings. The particular technology and research domain covered by this project focus on software failure prediction in cloud-based systems, highlighting the relevance of improving the reliability, availability, and performance of such services.

By utilizing advanced algorithms like MGOA and Hybrid Levy Flights-HBA Tuned RF, researchers can gain insights into complex relationships within data and enhance the purity value of their models. In the future, the project can be further expanded to explore additional optimization techniques, integrate more sophisticated machine learning algorithms, and incorporate real-world case studies to validate the effectiveness of the proposed approach. This ongoing research can open up new avenues for collaboration, experimentation, and innovation in academia, leading to valuable contributions to the field of cloud computing and software engineering.

Algorithms Used

In the proposed work, Hybrid YSGA-GOANet technique has been implemented on processed data to extract only important and meaningful attributes from it. This reduces the complexity of system and enhances its overall performance. To add the concept of novelty in proposed work, we aimed to improve the classification rate by incorporating Random Forest (RF) algorithm, whose hyperparameters are tuned or optimized by hybrid GOA-HBA optimization algorithms. The GOA-HBA combines the strengths of two optimization techniques, to efficiently search the hyperparameter space and find optimal or near-optimal configurations. This in turn enhances the model's ability to capture complex relationships within the data, improve its purity value and optimize its performance for specific tasks.

Keywords

SEO-optimized keywords: software failures detection, cloud computing systems, MGOA, feature selection, Random Forest (RF), machine learning, data preprocessing, anomaly detection, fault prediction, performance monitoring, cloud infrastructure, virtual machines, fault tolerance, cloud reliability, system resilience, software testing, software quality, artificial intelligence, Hybrid YSGA-GOANet technique, Hybrid GOA-HBA optimization algorithms, software failure prediction models, efficient techniques, improved classification rate, optimization techniques, hyperparameter space, complex relationships, purity value, optimal configurations.

SEO Tags

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