Saurabh Kadekodi
Saurabh Kadekodi is a research scientist working in the Storage Analytics team. He specializes in reliability anf performance of large-scale storage clusters. Saurabh completed his Ph.D. from Carnegie Mellon University as part of the Parallel Data Laboratory. Prior to that he has done his Masters from Northwestern University and bachelors from Pune Institute of Computer Technology, India.
Authored Publications
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Practical Design Considerations for Wide Locally Recoverable Codes (LRCs)
Shashwat Silas
Dave Clausen
File and Storage Technologies (FAST), USENIX (2023)
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Most of the data in large-scale storage clusters is erasure coded. At exascale, optimizing erasure codes for low storage overhead, efficient reconstruction, and easy deployment is of critical importance. Locally recoverable codes (LRCs) have deservedly gained central importance in this field, because they can balance many of these requirements. In our work we study wide LRCs; LRCs with large number of blocks per stripe and low storage overhead. These codes are a natural next step for practitioners to unlock higher storage savings, but they come with their own challenges. Of particular interest is their reliability, since wider stripes are prone to more simultaneous failures.
We conduct a practically-minded analysis of several popular and novel LRCs. We find that wide LRC reliability is a subtle phenomenon that is sensitive to several design choices, some of which are overlooked by theoreticians, and others by practitioners. Based on these insights, we construct novel LRCs called Uniform Cauchy LRCs, which show excellent performance in simulations, and a 33% improvement in reliability on unavailability events observed by a wide LRC deployed in a Google storage cluster. We also show that these codes are easy to deploy in a manner that improves their robustness to common maintenance events. Along the way, we also give a remarkably simple and novel construction of distance optimal LRCs (other constructions are also known), which may be of interest to theory-minded readers.
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Tiger: disk-adaptive redundancy without placement restrictions
Francisco Maturana
Sanjith Athlur
Rashmi KV
Gregory R. Ganger
Tiger: disk-adaptive redundancy without placement restrictions (2022)
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Large-scale cluster storage systems use redundancy (via erasure coding) to ensure data durability. Disk-adaptive redundancy—dynamically tailoring the redundancy scheme to observed disk failure rates—promises significant space and cost savings. Existing disk-adaptive redundancy systems, however, pose undesirable constraints on data placement, partitioning disks into subclusters with homogeneous failure rates and forcing each erasure-coded stripe to be entirely placed on the disks within one subcluster. This design increases risk, by reducing intra-stripe diversity and being more susceptible to unanticipated changes in a make/model’s failure rate, and only works for very large storage clusters fully committed to
disk-adaptive redundancy.
Tiger is a new disk-adaptive redundancy system that efficiently avoids adoption-blocking placement constraints, while also providing higher space-savings and lower risk relative to prior designs. To do so, Tiger introduces the eclectic stripe, in which disks with different failure rates can be used to store a stripe that has redundancy tailored to the set of failure rates of those disks. With eclectic stripes, pre-existing placement policies can be used while still enjoying the space-savings and robustness benefits of disk-adaptive redundancy. This paper introduces eclectic striping and Tiger’s design, including a new mean time-to-data-loss (MTTDL) approximation technique and new approaches for ensuring safe per-stripe settings given that failure rates of different devices change over time. Evaluation with logs from real-world clusters show that Tiger provides better space-savings, less bursty IO for changing redundancy schemes, and better robustness (due to increased risk-diversity) than prior disk-adaptive redundancy designs.
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