Achieving Reliability in Cloud Computing by a Novel Hybrid Approach
- Publisher:
- MDPI
- Publication Type:
- Journal Article
- Citation:
- Sensors, 2023, 23, (4), pp. 1965
- Issue Date:
- 2023-02-09
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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 na ve 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 na ve 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 optimizatio
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