Building reliable storage systems becomes increasingly challenging as the complexity of modern storage systems continues to grow. Understanding storage failure characteristics is crucially important for designing and building a reliable storage system. While several recent studies have been conducted on understanding storage failures, almost all of them focus on the failure characteristics of one component—disks—and do not study other storage component failures. This article analyzes the failure characteristics of storage subsystems. More specifically, we analyzed the storage logs collected from about 39,000 storage systems commercially deployed at various customer sites. The dataset covers a period of 44 months and includes about 1,800,000 disks hosted in about 155,000 storage-shelf enclosures. Our study reveals many interesting findings, providing useful guidelines for designing reliable storage systems. Some of our major findings include: (1) In addition to disk failures that contribute to 20--55% of storage subsystem failures, other components such as physical interconnects and protocol stacks also account for a significant percentage of storage subsystem failures. (2) Each individual storage subsystem failure type, and storage subsystem failure as a whole, exhibits strong self-correlations. In addition, these failures exhibit “bursty” patterns. (3) Storage subsystems configured with redundant interconnects experience 30--40% lower failure rates than those with a single interconnect. (4) Spanning disks of a RAID group across multiple shelves provides a more resilient solution for storage subsystems than within a single shelf.

Software defects significantly reduce system dependability. Among various types of software bugs, semantic and concurrency bugs are two of the most difficult to detect. This paper proposes a novel method, called MUVI, that detects an important class of semantic and concurrency bugs. MUVI automatically infers commonly existing multi-variable access correlations through code analysis and then detects two types of related bugs: (1) inconsistent updates--correlated variables are not updated in a consistent way, and (2) multi-variable concurrency bugs--correlated accesses are not protected in the same atomic sections in concurrent programs.We evaluate MUVI on four large applications: Linux, Mozilla,MySQL, and PostgreSQL. MUVI automatically infers more than 6000 variable access correlations with high accuracy (83%).Based on the inferred correlations, MUVI detects 39 new inconsistent update semantic bugs from the latest versions of these applications, with 17 of them recently confirmed by the developers based on our reports.We also implemented MUVI multi-variable extensions to tworepresentative data race bug detection methods (lock-set and happens-before). Our evaluation on five real-world multi-variable concurrency bugs from Mozilla and MySQL shows that the MUVI-extension correctly identifies the root causes of four out of the five multi-variable concurrency bugs with 14% additional overhead on average. Interestingly, MUVI also helps detect four new multi-variable concurrency bugs in Mozilla that have never been reported before. None of the nine bugs can be identified correctly by the original race detectors without our MUVI extensions.