For a decade, the Ceph distributed file system followed the conventional wisdom of building its storage backend on top of local file systems. This is a preferred choice for most distributed file systems today, because it allows them to benefit from the convenience and maturity of battle-tested code. Ceph’s experience, however, shows that this comes at a high price. First, developing a zero-overhead transaction mechanism is challenging. Second, metadata performance at the local level can significantly affect performance at the distributed level. Third, supporting emerging storage hardware is painstakingly slow. Ceph addressed these issues with BlueStore, a new backend designed to run directly on raw storage devices. In only two years since its inception, BlueStore outperformed previous established backends and is adopted by 70% of users in production. By running in user space and fully controlling the I/O stack, it has enabled space-efficient metadata and data checksums, fast overwrites of erasure-coded data, inline compression, decreased performance variability, and avoided a series of performance pitfalls of local file systems. Finally, it makes the adoption of backward-incompatible storage hardware possible, an important trait in a changing storage landscape that is learning to embrace hardware diversity.

For a decade, the Ceph distributed file system followed the conventional wisdom of building its storage backend on top of local file systems. This is a preferred choice for most distributed file systems today because it allows them to benefit from the convenience and maturity of battle-tested code. Ceph's experience, however, shows that this comes at a high price. First, developing a zero-overhead transaction mechanism is challenging. Second, metadata performance at the local level can significantly affect performance at the distributed level. Third, supporting emerging storage hardware is painstakingly slow.

Scale-out distributed storage systems can uphold balanced data growth in terms of capacity and performance on an on-demand basis. However, it is a challenge to store and manage large sets of contents being generated by the explosion of data. One of the promising solutions to mitigate big data issues is data deduplication, which removes redundant data across many nodes of the storage system. Nevertheless, it is non-trivial to apply a conventional deduplication design to the scale-out storage due to the following root causes. First, chunk-lookup for deduplication is not as scalable and extendable as the underlying storage system supports. Second, managing the metadata associated to deduplication requires a huge amount of design and implementation modifications of the existing distributed storage system. Lastly, the data processing and additional I/O traffic imposed by deduplication can significantly degrade performance of the scale-out storage. To address these challenges, we propose a new deduplication method, which is highly scalable and compatible with the existing scale-out storage. Specifically, our deduplication method employs a double hashing algorithm that leverages hashes used by the underlying scale-out storage, which addresses the limits of current fingerprint hashing. In addition, our design integrates the meta-information of file system and deduplication into a single object, and it controls the deduplication ratio at online by being aware of system demands based on post-processing. We implemented the proposed deduplication method on an open source scale-out storage. The experimental results show that our design can save more than 90% of the total amount of storage space, under the execution of diverse standard storage workloads, while offering the same or similar performance, compared to the conventional scale-out storage.

Migrating resources is a useful tool for balancing load in a distributed system, but it is difficult to determine when to move resources, where to move resources, and how much of them to move. We look at resource migration for file system metadata and show how CephFS's dynamic subtree partitioning approach can exploit varying degrees of locality and balance because it can partition the namespace into variable sized units. Unfortunately, the current metadata balancer is complicated and difficult to control because it struggles to address many of the general resource migration challenges inherent to the metadata management problem. To help decouple policy from mechanism, we introduce a programmable storage system that lets the designer inject custom balancing logic. We show the flexibility and transparency of this approach by replicating the strategy of a state-of-the-art metadata balancer and conclude by comparing this strategy to other custom balancers on the same system.

Emerging large-scale distributed storage systems are faced with the task of distributing petabytes of data among tens or hundreds of thousands of storage devices. Such systems must evenly distribute data and workload to efficiently utilize available resources and maximize system performance, while facilitating system growth and managing hardware failures. We have developed CRUSH, a scalable pseudorandom data distribution function designed for distributed object-based storage systems that efficiently maps data objects to storage devices without relying on a central directory. Because large systems are inherently dynamic, CRUSH is designed to facilitate the addition and removal of storage while minimizing unnecessary data movement. The algorithm accommodates a wide variety of data replication and reliability mechanisms and distributes data in terms of user-defined policies that enforce separation of replicas across failure domains.

In petabyte-scale distributed file systems that decouple read and write from metadata operations, behavior of the metadata server cluster will be critical to overall system performance and scalability. We present a dynamic subtree partitioning and adaptive metadata management system designed to efficiently manage hierarchical metadata workloads that evolve over time. We examine the relative merits of our approach in the context of traditional workload partitioning strategies, and demonstrate the performance, scalability and adaptability advantages in a simulation environment.