Nowadays the traditional file systems cannot handle the new requirements in terms of volume of data, high performance, fault-tolerance, and improved capabilities. So Distributed Storage Systems (DSS) took place to cover the need of a shared storage between separate systems, provide a scalable storage to serve thousands of servers, and improve the fault-tolerance. To this respect, a series of issues need to be properly addressed: scalability, the ability to handle large data, high performance even under heavy access concurrency, versioning, and fault-tolerance. In this work, we propose CoBFS , a framework of a DSS designed to boost the concurrent access to large shared data objects (such as files), while maintaining strong consistency guarantees. CoBFS has two key design factors: data striping and versioning-based concurrency control (through coverability) to enable higher operation performance on large concurrent data objects. To this respect, we introduce the notions of a block as a “bounded” Read/Write register, of a fragmented object as a sequence of blocks, and of fragmented coverable linearizabilty , a strong consistency property suitable for fragmented objects. CoBFS adopts a modular architecture, separating the object fragmentation process from the shared memory service allowing to use different shared memory implementations. At first, we use as storage a static atomic distributed shared memory (ADSM) emulation, the well known ABD , yielding CoABD F, which satisfies fragmented coverable linearizability. Then, we substitute the storage layer of CoBFS with a dynamic (reconfigurable) storage algorithm, called ARES, yielding COARESF; COARESF allows the addition and removal of servers without system interruptions and improves the storage efficiency due to the use of an erasure-coded mechanism. We conduct an extensive experimental evaluation on the Emulab and AWS EC2 testbeds, illustrating the benefits of our approaches, as well as other interesting tradeoffs. We believe that CoBFS ’s features (versioning, high concurrent accesses, handling large objects) has the potential of benefiting any static or dynamic storage algorithm to further extend its functionality for data-intensive applications at large scale.
Trigeorgi et al. (Tue,) studied this question.