Timothy Bisson

Dynamic integrated scheduling of hard real-time, soft real-time, and non-real-time processes

Real-time systems are growing in complexity and real-time and soft real-time applications are becoming common in general-purpose computing environments. Thus, there is a growing need for scheduling solutions that simultaneously support processes with a variety of different timeliness constraints. Toward this goal we have developed the resource allocation/dispatching (RAD) integrated scheduling model and the rate-based earliest deadline (RBED) integrated multi-class real-time scheduler based on this model. We present RAD and the RBED scheduler and formally prove the correctness of the operations that RBED employs. We then describe our implementation of RBED and present results demonstrating how RBED simultaneously and seamlessly supports hard real-time, soft real-time, and best-effort processes.

A Hybrid Disk-Aware Spin-Down Algorithm with I/O Subsystem Support

To offset the significant power demands of hard disk drives in computer systems, drives are typically powered down during idle periods. This saves power, but accelerates duty cycle consumption, leading to earlier drive failure. Hybrid disks with a small amount of non-volatile flash memory (NVCache) are coming on the market. We present four I/O subsystem enhancements that exploit the characteristics of hybrid disks to improve system performance: 1) artificial idle periods, 2) a read-miss cache, 3) anticipatory spin-up, and 4) NVCache write-throttling. These enhancements reduce power consumption, duty cycling, NVCache block-erase impact, and the observed spinup latency of a hybrid disk, resulting in lower power consumption, greater reliability, and faster I/O.

NVCache: Increasing the Effectiveness of Disk Spin-Down Algorithms with Caching

Being one of the few mechanical components in a typical computer system, hard drives consume a significant amount of the overall power used by a computer. Spinning down a hard drive reduces its power consumption, but only works when no disk accesses occur, limiting overall effectiveness. We have designed and implemented a technique to extend disk spin-down times using a small non-volatile storage cache called NVCache, which contains a combination of caching techniques to service reads and writes while the hard disk is in low-power mode. We show that combining NVCache with an adaptive disk spin-down algorithm, a hard disk’s power consumption can be reduced by up to 90%.

Integrating best-effort scheduling into a real-time system

Demand for real-time capability in general-purpose systems is rising and as systems are retrofitted with scheduling features they become increasingly complex. To counter this trend we present the best-effort bandwidth server (BEBS), an aperiodic server for flexible and efficient support of best-effort applications in a real-time system. Recognizing that the responsiveness of a server depends on its period, and that not every best-effort task requires equal responsiveness, the algorithm adjusts its period based on run-time behavior of tasks. We created a prototype implementation of the system to demonstrate that it performs suitably as a general-purpose scheduler in comparison to Linux, and outperforms a common type of hierarchy used in existing general-purpose systems. The result is a system that integrates real-time scheduling with best-effort support, both simple and powerful enough to be used as the only scheduler in a general-purpose operating system.

High-performance metadata indexing and search in petascale data storage systems

Large-scale storage systems used for scientific applications can store petabytes of data and billions of files, making the organization and management of data in these systems a difficult, time-consuming task. The ability to search file metadata in a storage system can address this problem by allowing scientists to quickly navigate experiment data and code while allowing storage administrators to gather the information they need to properly manage the system. In this paper, we present Spyglass, a file metadata search system that achieves scalability by exploiting storage system properties, providing the scalability that existing file metadata search tools lack. In doing so, Spyglass can achieve search performance up to several thousand times faster than existing database solutions. We show that Spyglass enables important functionality that can aid data management for scientists and storage administrators.

Designing a fast file system crawler with incremental differencing

Search engines for storage systems rely on crawlers to gather the list of files that need to be indexed. The recency of an index is determined by the speed at which this list can be gathered. While there has been a substantial amount of literature on building efficient web crawlers, there is very little literature on file system crawlers. In this paper we discuss the challenges in building a file system crawler. We then present the design of two file system crawlers: the first uses the standard POSIX file system API but carefully controls the amount of memory and CPU that it uses. The second leverages modifications to the file systemss internals, and a new API called SnapDiff, to detect modified files rapidly. For both crawlers we describe the incremental differencing design; the method to produce a list of changes between a previous crawl and the current point in time.