Saurabh Kadekodi
Saurabh Kadekodi is a Senior 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.
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TCO-driven Storage Provisioning for Exascale Data Centers
Timothy Kim
Prashant Nema
Jai Menon
Gregory R. Ganger
Rashmi Vinayak
2026
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Recent changes in data temperatures and storage device characteristics, both mechanical disk drives (HDDs) and solidstate drives (SSDs), expand the set of deployment options for exascale storage. Until recently, exascale storage systems followed a pattern of placing most data on HDDs with smaller amounts of SSD storage used for caching and performance-critical workloads. Exascale storage provisioning and dataset placement trade-offs have now changed.
This paper describes a total cost of ownership (TCO) model that captures primary aspects of modern deployments and uses it to explore the new trade-off space. Using capacity and performance telemetry information for 43 production datasets+workloads at two large hyperscalers, we show significant changes from prior analyses of workloads and storage placement decisions across a multitude of storage device types. We also introduce a storage cluster TCO optimizer that identifies the lowest-TCO grouping and assignment of datasets to device types, exposing a number of insights that can help guide future deployments. For example, our analysis shows that the highest-density SSDs are particularly favorable for clusters with heavy AI/ML workloads but are only cost-effective at exascale when combined with high-density HDDs. Finally, we use our framework to evaluate how storage provisioning and overall TCO change as a function of key parameters like device write amplification, cluster power bounds, and the maximum number of device types allowed.
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Okapi: Decoupling data striping from grouping in cluster storage systems
Sanjith Athlur
Timothy Kim
Francisco Maturana
Xavier Ramos
Rashmi KV
Gregory R. Ganger
2025
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The Okapi cluster file system decouples how data is spread across disks (data striping) for IO efficiency from how data is erasure coded together (redundancy grouping) for durability. Existing systems couple these two mechanisms’ configurations, inducing significant inefficiencies. Decoupling allows grouping to be configured based on reliability and space efficiency goals, while simultaneously allowing striping to be configured based on performance goals. Decoupling also allows redundancy scheme changes from one EC scheme to another (e.g., to react to data temperature or disk failure rate changes) to occur without having to re-write data. Evaluation of an Okapi prototype shows that decoupling can be accomplished with <1% increase in metadata size and file manager memory, and minimal file creation and degraded read resource increase. Experiments demonstrate that decoupling can improve read throughput by 80% and reduce seeks per second by up to 70%, without yielding any data reliability, and reduce the overhead of redundancy transitions by up to 70%.
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JBD2, the current physical journaling mechanism in Ext4 is bulky and resource-hungry. Specifically, in case of metadataheavy workloads, fsyncs issued by applications cause JBD2 to write copies of changed metadata blocks, incurring high byte and IO overhead. When storing data in Ext4 via NFS (a popular setup), the NFS protocol issues fsyncs for every file metadata update which further exacerbates the problem. In a simple multi-threaded mail-server workload, JBD2 consumed approximately 76% of the disk’s write bandwidth. Higher byte and IO utilization of JBD2 results in reduced application throughput, higher wear-out of flash based media and increased performance provisioning costs in cloud-based storage services.
In this paper, we present hybrid journaling for Ext4 which performs logical journaling for simple and frequent file system modifications, while relying on JBD2 for more complex and rare modifications. Key design ingredients of XCOMMIT are compact logging, selective flushing and inline journaling. The first two techniques work together to ensure that over 80% commits are contained within a single 4KB block and are written to disk without requiring an expensive cache flush operation. Inline journaling minimizes context switching delays. With faster and efficient fsyncs, XCOMMIT reduces throughput interference of JBD2 by over 2× along with throughput improvements of up to 120%. We implemented XCOMMIT in Ext4 and successfully merged our code to the upstream Linux kernel.
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Thesios: Synthesizing Accurate Counterfactual I/O Traces from I/O Samples
Mangpo Phothilimthana
Soroush Ghodrati
Selene Moon
ASPLOS 2024, Association for Computing Machinery
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Representative modeling of I/O activity is crucial when designing large-scale distributed storage systems. Particularly important use cases are counterfactual “what-if” analyses that assess the impact of anticipated or hypothetical new storage policies or hardware prior to deployment. We propose Thesios, a methodology to accurately synthesize such hypothetical full-resolution I/O traces by carefully combining down-sampled I/O traces collected from multiple disks attached to multiple storage servers. Applying this approach to real-world traces that a real ready routinely sampled at Google, we show that our synthesized traces achieve 95–99.5% accuracy in read/write request numbers, 90–97% accuracy in utilization, and 80–99.8% accuracy in read latency compared to metrics collected from actual disks. We demonstrate how The-sios enables diverse counterfactual I/O trace synthesis and analyses of hypothetical policy, hardware, and server changes through four case studies: (1) studying the effects of changing disk’s utilization, fullness, and capacity, (2) evaluating new data placement policy, (3) analyzing the impact on power and performance of deploying disks with reduced rotations-per-minute (RPM), and (4) understanding the impact of increased buffer cache size on a storage server. Without Thesios, such counterfactual analyses would require costly and potentially risky A/B experiments in production.
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Morph: Efficient File-Lifetime Redundancy Management for Cluster File Systems
Timothy Kim
Sanjith Athlur
Francisco Maturana
Alessandra Vandevoorde
Rashmi KV
Gregory R. Ganger
SOSP (2024)
Preview abstract
Many data services tune and change redundancy configurations of files over their lifetimes to address changes in data temperature and latency requirements. Unfortunately, changing redundancy configs (transcode) is IO-intensive. The Morph cluster file system introduces new transcode-efficient redundancy schemes to minimize overheads as files progress through lifetime phases. For newly ingested data, commonly stored via 3-way replication, Morph introduces a hybrid redundancy scheme that combines a replica with an erasure-coded (EC) stripe, reducing both ingest IO and capacity overheads while enabling free transcode to EC by deleting replicas. For subsequent transcodes to wider, more space-efficient EC configs, Morph exploits Convertible Codes, which minimize data read for EC transcode, and introduces new block placement policies to maximize their effectiveness.
Analysis of data ingest and transcode activity in Google storage clusters shows the current massive IO load and the potential savings from Morph's approach---transcode IO can be reduced by over 95%, and total ingest+transcode IO can be reduced by 50--60% while also reducing capacity overheads for newly ingested data by 20%. Experiments evaluating a Morph implementation in HDFS show that these benefits can be realized in a real system without hidden increases in complexity, tail latency, or degraded-mode latency.
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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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Small (kilobyte-sized) objects are the bane of highly scalable cloud object stores. Larger (at least megabytesized) objects not only improve performance, but also result in orders of magnitude lower cost, due to the current operation-based pricing model of commodity cloud object stores. For example, in Amazon S3’s current pricing scheme, uploading 1GiB data by issuing 4KiB PUT requests (at 0.0005¢ each) is approximately 57x more expensive than storing that same 1GiB for a month. To address this problem, we propose client-side packing of small immutable files into gigabyte-sized blobs with embedded indices to identify each file’s location. Experiments with a packing implementation in Alluxio (an open-source distributed file system) illustrate the potential benefits, such as simultaneously increasing file creation throughput by up to 60x and decreasing cost to 1/25000 of the original.
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With the ever increasing filesystem sizes, there is a constant need for faster filesystem access. A vital requirement to achieve this is efficient filesystem metadata management. The bitmap technique currently used to manage free space in Ext4 is faced by scalability challenges owing to this exponential increase. This has led us to re-examine the available choices and explore a radically different design of managing free space called Space Maps. This paper describes the design and implementation of space maps in Ext4. The paper also highlights the limitations of bitmaps and does a comparative study of howspace maps fare against them. In space maps, free space is represented by extent based red-black trees and logs. The design of space maps makes the free space information of the filesystem extremely compact allowing it to be stored in main memory at all times. This significantly reduces the long, random seeks on the disk that were required for updating the metadata. Likewise, analogous on-disk structures and their interaction with the in-memory space maps ensure that filesystem integrity is maintained. Since seeks are the bottleneck as far as filesystem performance is concerned, their extensive reduction leads to faster filesystem operations. Apart from the allocation/deallocation improvements, the log based design of Space Maps helps reduce fragmentation at the filesystem level itself. Space Maps uplift the performance of the filesystem and keep the metadata management in tune with the highly scalable Ext4.
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