Erasure Coding Techniques for Distributed Storage Systems
Summary
Erasure coding has emerged as a cornerstone of modern distributed storage, replacing simple replication with more space‐efficient schemes that fragment data into redundant pieces. By encoding an original object into a greater number of fragments than are strictly required for reconstruction, erasure codes enable a system to tolerate node failures while minimising storage overhead. Early implementations relied on classical Reed–Solomon codes, valued for their optimal storage‐versus‐reliability trade‐off but challenged by high decoding complexity and network load during repair. To address these drawbacks, new families of codes have been devised. Locally recoverable codes (LRCs) reduce the number of fragments accessed during a single node repair, thus lowering repair bandwidth and accelerating recovery. Regenerating codes shift some of the decoding burden to the network, achieving lower total repair traffic at the cost of more complex subpacketisation. Fractional repetition and hybrid schemes combine replication and erasure coding for simplified repair management. Recent attention has focused on balancing stripe width, field‐size requirements and update efficiency, with practitioners tuning code parameters to the failure patterns and performance profiles of large‐scale cloud and edge environments. Practical deployments now leverage code constructions that offer configurable locality, multiple recovery sets and flexible trade‐offs between storage savings and repair speed.
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Erasure Coding Techniques for Distributed Storage Systems publication trend
The graph below shows the total number of articles in erasure coding techniques for distributed storage systems across all publications each year (not limited to Nature Index journals).
Technical terms
Erasure code: A method that transforms data into multiple redundant fragments to tolerate losses without full replication.
Reed–Solomon code: A class of maximum‐distance-separable codes offering optimal data reconstruction from a minimal set of fragments.
Locally recoverable code (LRC): A code designed so that any single fragment can be rebuilt by accessing only a small subset of other fragments.
Regenerating code: An erasure code that minimises repair bandwidth by allowing new fragments to be generated from sub-symbols transmitted by surviving nodes.
Stripe: A grouping of data and parity fragments across storage nodes over which erasure coding is applied.
Repair bandwidth: The total volume of data transferred during the repair of a lost fragment.
References
- Practical Design Considerations for Wide Locally Recoverable Codes (LRCs). ACM Transactions on Storage (2023).
- Erasure-Coding-Based Storage and Recovery for Distributed Exascale Storage Systems. Applied Sciences (2021).
- Performance Analysis of Distributed File System Based on RAID Storage for Tapeless Storage. IEEE Access (2023).
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