HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems
Data-intensive applications are generating massive amounts of data which is stored on cloud computing platforms where distributed file systems are utilized for storage at the back end. Most users of those applications deployed on cloud computing systems read data more often than they write. Hence, e...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/5/1183 |
| _version_ | 1797615528719679488 |
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| author | Anusha Nalajala T. Ragunathan Ranesh Naha Sudheer Kumar Battula |
| author_facet | Anusha Nalajala T. Ragunathan Ranesh Naha Sudheer Kumar Battula |
| author_sort | Anusha Nalajala |
| collection | DOAJ |
| description | Data-intensive applications are generating massive amounts of data which is stored on cloud computing platforms where distributed file systems are utilized for storage at the back end. Most users of those applications deployed on cloud computing systems read data more often than they write. Hence, enhancing the performance of read operations is an important research issue. Prefetching and caching are used as important techniques in the context of distributed file systems to improve the performance of read operations. In this research, we introduced a novel highly relevant frequent patterns (HRFP)-based algorithm that prefetches content from the distributed file system environment and stores it in the client-side caches that are present in the same environment. We have also introduced a new replacement policy and an efficient migration technique for moving the patterns from the main memory caches to the caches present in the solid-state devices based on a new metric namely the relevancy of the patterns. According to the simulation results, the proposed approach outperformed other algorithms that have been suggested in the literature by a minimum of 15% and a maximum of 53%. |
| first_indexed | 2024-03-11T07:26:41Z |
| format | Article |
| id | doaj.art-a02a1f474d314e94bc0d2e12e5963dd1 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-11T07:26:41Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-a02a1f474d314e94bc0d2e12e5963dd12023-11-17T07:32:49ZengMDPI AGElectronics2079-92922023-03-01125118310.3390/electronics12051183HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File SystemsAnusha Nalajala0T. Ragunathan1Ranesh Naha2Sudheer Kumar Battula3CSE Department, SRM University-AP, Amaravati 522502, IndiaFaculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai 600116, IndiaSchool of Computer Science, The University of Adelaide, Adelaide 5005, AustraliaSchool of Technology, Environments and Design (TED), University of Tasmania, Hobart 7000, AustraliaData-intensive applications are generating massive amounts of data which is stored on cloud computing platforms where distributed file systems are utilized for storage at the back end. Most users of those applications deployed on cloud computing systems read data more often than they write. Hence, enhancing the performance of read operations is an important research issue. Prefetching and caching are used as important techniques in the context of distributed file systems to improve the performance of read operations. In this research, we introduced a novel highly relevant frequent patterns (HRFP)-based algorithm that prefetches content from the distributed file system environment and stores it in the client-side caches that are present in the same environment. We have also introduced a new replacement policy and an efficient migration technique for moving the patterns from the main memory caches to the caches present in the solid-state devices based on a new metric namely the relevancy of the patterns. According to the simulation results, the proposed approach outperformed other algorithms that have been suggested in the literature by a minimum of 15% and a maximum of 53%.https://www.mdpi.com/2079-9292/12/5/1183frequent patternscloud computing systemsprefetchingcaching and replacement methodsdistributed file systems |
| spellingShingle | Anusha Nalajala T. Ragunathan Ranesh Naha Sudheer Kumar Battula HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems Electronics frequent patterns cloud computing systems prefetching caching and replacement methods distributed file systems |
| title | HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems |
| title_full | HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems |
| title_fullStr | HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems |
| title_full_unstemmed | HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems |
| title_short | HRFP: Highly Relevant Frequent Patterns-Based Prefetching and Caching Algorithms for Distributed File Systems |
| title_sort | hrfp highly relevant frequent patterns based prefetching and caching algorithms for distributed file systems |
| topic | frequent patterns cloud computing systems prefetching caching and replacement methods distributed file systems |
| url | https://www.mdpi.com/2079-9292/12/5/1183 |
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