Achieving Reliability in Cloud Computing by a Novel Hybrid Approach
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View abstract on PubMed
Summary
This summary is machine-generated.This study enhances cloud computing fault tolerance by comparing machine learning algorithms and proposes a modified sequential minimal optimization (MSMO) with delta-checkpointing for improved reliability and accuracy.
Area Of Science
- Cloud Computing
- Machine Learning
- Fault Tolerance
Background
- Cloud computing (CC) offers significant benefits but faces challenges in resource allocation, security, and fault tolerance (FT).
- Existing fault tolerance methods struggle with heterogeneity, lack of standards, and the need for automation in cloud environments.
- Accurate fault prediction and system reliability are critical for robust cloud infrastructure.
Purpose Of The Study
- To evaluate the performance of various machine learning (ML) algorithms for fault prediction in cloud computing.
- To identify the most accurate and reliable ML classifier for virtual machine (VM) fault detection.
- To propose an improved fault tolerance method combining ML with delta-checkpointing.
Main Methods
- Comparison of ML algorithms including Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (MLR), K-Nearest Neighbor (KNN), and Random Forest (RF).
- Utilized both secondary data from an HPC system and primary data from virtual machines (VMs).
- Employed data splitting ratios (80/20, 70/30) and 5-fold cross-validation for model evaluation.
- Introduced a modified Sequential Minimal Optimization (MSMO) algorithm coupled with delta-checkpointing (D-CP).
Main Results
- Naïve Bayes excelled in CPU-Mem fault prediction, while Sequential Minimal Optimization (SMO) performed well on HDD fault prediction using secondary data.
- Random Forest showed high accuracy but poor time complexity with primary data; SMO offered a balance of accuracy and time efficiency.
- The proposed Modified Sequential Minimal Optimization (MSMO) with Delta-Checkpointing (D-CP) demonstrated enhanced accuracy, reduced fault prediction error, and improved reliability.
Conclusions
- Machine learning algorithms show promise in enhancing cloud computing fault tolerance.
- The modified MSMO algorithm combined with D-CP offers a superior approach for improving cloud system reliability and fault prediction.
- This research contributes to more robust and dependable cloud computing environments.
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