Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems
Cloud data centers have started utilizing erasure coding in large-scale storage systems to ensure high reliability with limited overhead compared to replication. However, data recovery in erasure coding incurs high network bandwidth consumption compared to replication. Cloud storage systems also pla...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10102461/ |
| _version_ | 1827960456860401664 |
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| author | Rekha Nachiappan Rodrigo N. Calheiros Kenan M. Matawie Bahman Javadi |
| author_facet | Rekha Nachiappan Rodrigo N. Calheiros Kenan M. Matawie Bahman Javadi |
| author_sort | Rekha Nachiappan |
| collection | DOAJ |
| description | Cloud data centers have started utilizing erasure coding in large-scale storage systems to ensure high reliability with limited overhead compared to replication. However, data recovery in erasure coding incurs high network bandwidth consumption compared to replication. Cloud storage systems also play an important role in the energy consumption of data centers. Heuristic proactive recovery algorithms select all data blocks from failure-predicted disk/machine and perform proactive replication that contributes to huge recovery bandwidth savings. However, they fail to optimize the selection. Optimization can further improve resource savings. To address this issue, we propose a recovery algorithm that applies minimization on data blocks selected for proactive replication by considering the necessary and appropriate constraints that are constructed based on the system’s current network traffic and data duplication information. We evaluate the proposed algorithm using extensive simulations. Experiments show that the recovery algorithm reduces network traffic by 60% and storage overhead by 46% compared to the heuristic proactive recovery approach. Also, the proposed proactive recovery methods reduce the storage system’s energy consumption by up to 52% compared to replication. |
| first_indexed | 2024-04-09T16:08:49Z |
| format | Article |
| id | doaj.art-91cdb1662ce6472a80531dbcc1b8c6b6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-09T16:08:49Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-91cdb1662ce6472a80531dbcc1b8c6b62023-04-24T23:00:52ZengIEEEIEEE Access2169-35362023-01-0111382263823910.1109/ACCESS.2023.326710610102461Optimized Proactive Recovery in Erasure-Coded Cloud Storage SystemsRekha Nachiappan0https://orcid.org/0000-0003-0300-4093Rodrigo N. Calheiros1https://orcid.org/0000-0001-7435-2445Kenan M. Matawie2Bahman Javadi3School of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW, AustraliaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW, AustraliaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW, AustraliaSchool of Computer, Data and Mathematical Sciences, Western Sydney University, Penrith, NSW, AustraliaCloud data centers have started utilizing erasure coding in large-scale storage systems to ensure high reliability with limited overhead compared to replication. However, data recovery in erasure coding incurs high network bandwidth consumption compared to replication. Cloud storage systems also play an important role in the energy consumption of data centers. Heuristic proactive recovery algorithms select all data blocks from failure-predicted disk/machine and perform proactive replication that contributes to huge recovery bandwidth savings. However, they fail to optimize the selection. Optimization can further improve resource savings. To address this issue, we propose a recovery algorithm that applies minimization on data blocks selected for proactive replication by considering the necessary and appropriate constraints that are constructed based on the system’s current network traffic and data duplication information. We evaluate the proposed algorithm using extensive simulations. Experiments show that the recovery algorithm reduces network traffic by 60% and storage overhead by 46% compared to the heuristic proactive recovery approach. Also, the proposed proactive recovery methods reduce the storage system’s energy consumption by up to 52% compared to replication.https://ieeexplore.ieee.org/document/10102461/Cloud storage systemsdata reliabilityerasure codesreplicationenergy consumption |
| spellingShingle | Rekha Nachiappan Rodrigo N. Calheiros Kenan M. Matawie Bahman Javadi Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems IEEE Access Cloud storage systems data reliability erasure codes replication energy consumption |
| title | Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems |
| title_full | Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems |
| title_fullStr | Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems |
| title_full_unstemmed | Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems |
| title_short | Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems |
| title_sort | optimized proactive recovery in erasure coded cloud storage systems |
| topic | Cloud storage systems data reliability erasure codes replication energy consumption |
| url | https://ieeexplore.ieee.org/document/10102461/ |
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