Jiri Schindler

Workload-aware storage layout for database systems

The performance of a database system depends strongly on the layout of database objects, such as indexes or tables, onto the underlying storage devices. A good layout will both balance the I/O workload generated by the database system and avoid the performance-degrading interference that can occur when concurrently accessed objects are stored on the same volume. In current practice, layout is typically guided by heuristics and rules of thumb, such as separating indexes and tables or striping all objects across all of the available storage devices. However, these guidelines may give poor results. In this paper, we address the problem of generating an optimized layout of a given set of database objects. Our layout optimizer goes beyond generic guidelines by making use of a description of the database systems I/O activity. We formulate the layout problem as a non-linear programming (NLP) problem and use the I/O description as input to an NLP solver. Our layout optimization technique, which is incorporated into a database layout advisor, identifies a layout that both balances load and avoids interference. We evaluate experimentally the efficacy of our approach and demonstrate that it can quickly identify non-trivial optimized layouts.

A deduplication study for host-side caches in virtualized data center environments

Flash memory-based caches inside VM hypervisors can reduce I/O latencies and offload much of the I/O traffic from network-attached storage systems deployed in virtualized data centers. This paper explores the effectiveness of content deduplication in these large (typically 100s of GB) host-side caches. Previous deduplication studies focused on data mostly at rest in backup and archive applications. This study focuses on cached data and dynamic workloads within the shared VM infrastructure. We analyze I/O traces from six virtual desktop infrastructure (VDI) I/O storms and two longterm CIFS studies and show that deduplication can reduce the data footprint inside host-side caches by as much as 67%. This in turn allows for caching a larger portion of the data set and improves the effective cache hit rate. More importantly, such increased caching efficiency can alleviate load from networked storage systems during I/O storms when most VM instances perform the same operation such as virus scans, OS patch installs, and reboots.

I/O characteristics of NoSQL databases

The advent of the so-called NoSQL databases has brought about a new model of using storage systems. While traditional relational database systems took advantage of features offered by centrally-managed, enterprise-class storage arrays, the new generation of database systems with weaker data consistency models is content with using and managing locally attached individual storage devices and providing data reliability and availability through high-level software features and protocols. This work aims to review the architecture of several existing NoSQL DBs with an emphasis on how they organize and access data in the shared-nothing locally-attached storage model. It shows how these systems operate under typical workloads (new inserts and point and range queries), what access characteristics they exhibit to storage systems. Finally, it examines how several recently developed key/value stores, schema-free document storage systems, and extensible column stores organize data on local filesystems on top of directly-attached disks and what system features they must (re)implement in order to provide the expected data reliability.