Achieving Reliability in Cloud Computing by a Novel Hybrid Approach

Abstract

Cloud computing (CC) benefits and opportunities are among the fastest growing technologies in the computer industry. Cloud computing’s challenges include resource allocation, security, quality of service, availability, privacy, data management, performance compatibility, and fault tolerance. Fault tolerance (FT) refers to a system’s ability to continue performing its intended task in the presence of defects. Fault-tolerance challenges include heterogeneity and a lack of standards, the need for automation, cloud downtime reliability, consideration for recovery point objects, recovery time objects, and cloud workload. The proposed research includes machine learning (ML) algorithms such as naïve Bayes (NB), library support vector machine (LibSVM), multinomial logistic regression (MLR), sequential minimal optimization (SMO), K-nearest neighbor (KNN), and random forest (RF) as well as a fault-tolerance method known as delta-checkpointing to achieve higher accuracy, lesser fault prediction error, and reliability. Furthermore, the secondary data were collected from the homonymous, experimental high-performance computing (HPC) system at the Swiss Federal Institute of Technology (ETH), Zurich, and the primary data were generated using virtual machines (VMs) to select the best machine learning classifier. In this article, the secondary and primary data were divided into two split ratios of 80/20 and 70/30, respectively, and cross-validation (5-fold) was used to identify more accuracy and less prediction of faults in terms of true, false, repair, and failure of virtual machines. Secondary data results show that naïve Bayes performed exceptionally well on CPU-Mem mono and multi blocks, and sequential minimal optimization performed very well on HDD mono and multi blocks in terms of accuracy and fault prediction. In the case of greater accuracy and less fault prediction, primary data results revealed that random forest performed very well in terms of accuracy and fault prediction but not with good time complexity. Sequential minimal optimization has good time complexity with minor differences in random forest accuracy and fault prediction. We decided to modify sequential minimal optimization. Finally, the modified sequential minimal optimization (MSMO) algorithm with the fault-tolerance delta-checkpointing (D-CP) method is proposed to improve accuracy, fault prediction error, and reliability in cloud computing.

Fault‐tolerance approaches for distributed and cloud computing environments: A systematic review, taxonomy and future directions

“…A replication strategy is applied to make copies of fault‐prone tasks to provide recovery. Shahid et al 153 integrates machine learning with checkpointing to enhance the accuracy, reliability and reduces false prediction. Authors in this paper introduced machine learning classifiers, that is, naive bayes, library support vector machine, multinomial logistic regression, sequential minimal optimization, K‐nearest neighbor, and random forest for fault classification and prediction and delta‐checkpointing for fault tolerance.…”

End-to-End Service Availability in Heterogeneous Multi-Tier Cloud–Fog–Edge Networks

“…Assessing availability and reliability of multi-tier applications using probabilistic models to combine individual tier reliabilities [11][12][13][14].…”

“…Most existing analytical models focus on reliability within isolated tiers rather than endto-end availability across systems. For example, the authors of [2][3][4][12][13][14] assessed the reliability of individual cloud, edge, or fog tiers but did not study overall system availability. This fails to account for cascading failures between interdependent layers.…”

“…There is limited guidance for setting availability requirements during the design process based on user needs, as seen in previous works like [12][13][14].…”

“…Compared to purely analytical models like those in [12][13][14], the proposed approach better handles cascading failures between interdependent tiers. The Markov chain captures state transitions across layers.…”