File-system snapshots have been a key component of enterprise storage management since their inception. Creating and managing them efficiently, while maintaining flexibility and low overhead, has been a constant struggle. Although the current state-of-the-art mechanism—hierarchical reference counting—performs reasonably well for traditional small-file workloads, these workloads are increasingly vanishing from the enterprise data center, replaced instead with virtual machine and database workloads. These workloads center around a few very large files, violating the assumptions that allow hierarchical reference counting to operate efficiently. To better cope with these workloads, we introduce Generational Chain Trees (GCTrees), a novel method of space management that uses concepts of block lineage across snapshots rather than explicit reference counting. As a proof of concept, we create a prototype file system—gcext4, a modified version of ext4 that uses GCTrees as a basis for snapshots and copy-on-write. In evaluating this prototype empirically, we find that although they have a somewhat higher overhead for traditional workloads, GCTrees have dramatically lower overhead than hierarchical reference counting for large-file workloads, improving by a factor of 34 or more in some cases. Furthermore, gcext4 performs comparably to ext4 across all workloads, showing that GCTrees impose minor cost for their benefits.

File-system snapshots have been a key component of enterprise storage management since their inception. Creating and managing them efficiently, while maintaining flexibility and low overhead, has been a constant struggle. Although the current state-of-the-art mechanism, hierarchical reference counting, performs reasonably well for traditional small-file workloads, these workloads are increasingly vanishing from the enterprise data center, replaced instead with virtual machine and database workloads. These workloads center around a few very large files, violating the assumptions that allow hierarchical reference counting to operate efficiently. To better cope with these workloads, we introduce GCTrees, a novel method of space management that uses concepts of block lineage across snapshots, rather than explicit reference counting. As a proof of concept, we create a prototype file system, gcext4, a modified version of ext4 that uses GCTrees as a basis for snapshots and copy-on-write. In evaluating this prototype analytically, we find that, though they have a somewhat higher overhead for traditional workloads, GCTrees have dramatically lower overhead than hierarchical reference counting for large-file workloads, improving by a factor of 34 or more in some cases. Furthermore, gcext4 performs comparably to ext4 across all workloads, showing that GCTrees impose minor cost for their benefits.

Failures, errors, and bugs can corrupt file systems and cause data loss, despite the presence of journals and similar preventive techniques. While consistency checkers such as fsck can detect corruption and repair a damaged image, they are generally created as an afterthought, to be run only at rare intervals. Thus, checkers operate slowly, causing significant downtime for large scale storage systems. We address this dilemma by treating the checker as a key component of the overall file system, rather than a peripheral add-on. To this end, we present a modified ext3 file system, rext 3, to directly support the fast file-system checker, ffsck . Rext3 colocates and self-identifies its metadata blocks, removing the need for costly seeks and tree traversals during checking. These modifications allow ffsck to scan and repair the file system at rates approaching the full sequential bandwidth of the underlying device. In addition, we demonstrate that rext3 generally performs competitively with ext3 and exceeds it in handling random reads and large writes. Finally, we apply our principles to FreeBSD’s FFS file system and its checker, doing so in a lightweight fashion that preserves the file-system layout while still providing some of the performance gains from ffsck.

Offloading has emerged as a promising idea to allow resource-constrained mobile devices to access intensive applications, without performance or energy costs, by leveraging external computing resources. This could be particularly useful in enterprise contexts where running line-of-business applications on mobile devices can enhance enterprise operations. However, we must address three practical roadblocks to make offloading amenable to adoption by enterprises: (i) ensuring privacy and trustworthiness of offload, (ii) decoupling offloading systems from their reliance on the availability of dedicated resources and (iii) accommodating offload at scale. We present the design and implementation of ECOS, an enterprise-centric offloading framework that leverages Software-Defined Networking to augment prior offloading proposals and address these limitations. ECOS functions as an application running at an enterprise-wide controller to allocate resources to mobile applications based on privacy and performance requirements, to ensure fairness, and to enforce security constraints. Experiments using a prototype based on Android and OpenFlow establish the effectiveness of our approach.

We analyze the I/O behavior of iBench , a new collection of productivity and multimedia application workloads. Our analysis reveals a number of differences between iBench and typical file-system workload studies, including the complex organization of modern files, the lack of pure sequential access, the influence of underlying frameworks on I/O patterns, the widespread use of file synchronization and atomic operations, and the prevalence of threads. Our results have strong ramifications for the design of next generation local and cloud-based storage systems.

We analyze the I/O behavior of iBench, a new collection of productivity and multimedia application workloads. Our analysis reveals a number of differences between iBench and typical file-system workload studies, including the complex organization of modern files, the lack of pure sequential access, the influence of underlying frameworks on I/O patterns, the widespread use of file synchronization and atomic operations, and the prevalence of threads. Our results have strong ramifications for the design of next generation local and cloud-based storage systems.