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Achieving Reliability in Cloud Computing by a Novel Hybrid Approach.

Muhammad Asim Shahid1, Muhammad Mansoor Alam1,2,3,4, Mazliham Mohd Su'ud4

  • 1Malaysian Institute of Information Technology, Universiti Kuala Lumpur, Kuala Lumpur 50250, Malaysia.

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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.

Keywords:
Weibull distributioncloud computingdelta-checkpointingfault classification and predictionfault tolerancemachine learningreliability

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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.