SearcharxivSearch

arXiv subjects

Peter Macko

Publications and source records attributed to Peter Macko.

2 recordsLinked to original sources

Trace Validation of Unmodified Concurrent Systems with OmniLink

Concurrent systems are notoriously difficult to validate: subtle bugs may only manifest under rare thread interleavings, and existing tools often require intrusive instrumentation or unrealistic execution models. We present OmniLink, a new methodology for validating concurrent implementations against high-level specifications in TLA+. Unlike prior TLA+ based approaches which use a technique called trace validation, OmniLink treats system events as black boxes with a timebox in which they occurred and a meaning in TLA+, solving for a logical total order of actions. Unlike prior approaches based on linearizability checking, which already solves for total orders of actions with timeboxes, OmniLink uses a flexible specification language, and offers a different linearizability checking method based on off-the-shelf model checking. OmniLink offers different features compared existing linearizability checking tools, and we show that it outperforms the state of the art on large scale validation tasks. Our evaluation validates WiredTiger, a state-of-the-art industrial database storage layer, as well as Balanced Augmented Tree (BAT), a state-of-the art lock-free data structure from the research community, and ConcurrentQueue, a popular lock-free queue featuring aggressive performance optimizations. We use OmniLink to improve WiredTiger's existing TLA+ model, as well as develop new TLA+ models that closely match the behavior of the modeled systems, including non-linearizable behaviors. OmniLink is able to find known bugs injected into the systems under test, as well as help discover two previously unknown bugs (1 in BAT, 1 in ConcurrentQueue), which we have confirmed with the authors of those systems.

cs.SE

SMORE: A Cold Data Object Store for SMR Drives (Extended Version)

Shingled magnetic recording (SMR) increases the capacity of magnetic hard drives, but it requires that each zone of a disk be written sequentially and erased in bulk. This makes SMR a good fit for workloads dominated by large data objects with limited churn. To explore this possibility, we have developed SMORE, an object storage system designed to reliably and efficiently store large, seldom-changing data objects on an array of host-managed or host-aware SMR disks. SMORE uses a log-structured approach to accommodate the constraint that all writes to an SMR drive must be sequential within large shingled zones. It stripes data across zones on separate disks, using erasure coding to protect against drive failure. A separate garbage collection thread reclaims space by migrating live data out of the emptiest zones so that they can be trimmed and reused. An index stored on flash and backed up to the SMR drives maps object identifiers to on-disk locations. SMORE interleaves log records with object data within SMR zones to enable index recovery after a system crash (or failure of the flash device) without any additional logging mechanism. SMORE achieves full disk bandwidth when ingesting data---with a variety of object sizes---and when reading large objects. Read performance declines for smaller object sizes where inter- object seek time dominates. With a worst-case pattern of random deletions, SMORE has a write amplification (not counting RAID parity) of less than 2.0 at 80% occupancy. By taking an index snapshot every two hours, SMORE recovers from crashes in less than a minute. More frequent snapshots allow faster recovery.

cs.OS