Space-Efficient Predictive Block Management

With growing disk and storage capacities, the amount of required metadata for tracking all blocks in a system becomes a daunting task by itself. In previous work, we have demonstrated a system software effort in the area of predictive data grouping for reducing power and latency on hard disks. The structures used, very similar to prior efforts in prefetching and prefetch caching, track access successor information at the block level, keeping a fixed number of immediate successors per block. While providing powerful predictive expansion capabilities and being more space efficient in the amount of required metadata than many previous strategies, there remains a growing concern of how much data is actually required. In this paper, we present a novel method of storing equivalent information, SESH, a Space Efficient Storage of Heredity. This method utilizes the high amount of block-level predictability observed in a number of workload trace sets to reduce the overall metadata storage by up to 99% without any loss of information. As a result, we are able to provide a predictive tool that is adaptive, accurate, and robust in the face of workload noise, for a tiny fraction of the metadata cost previously anticipated; in some cases, reducing the required size from 12 gigabytes to less than 150 megabytes

Two case studies in predictable application scheduling using Rialto/NT

Journal ArticleThis paper analyzes the results of two case studies in applying the Rialto/NT scheduler to real Windows 2000 applications. The first study is of a soft modem-a modem whose signal processing work is performed on the host CPU, rather than on a dedicated signal processing chip. The second is of an audio player application. Both of these are frequently used real-time applications-ones running on systems that were not designed to support predictable real-time execution. To function correctly, both applications require that ongoing computations be performed in a timely manner. In both cases, we first measured an original version designed to run on Windows 2000, and then modified the application to take advantage of ongoing CPU Reservations provided by the Rialto/NT scheduler. We report on the benefits and problems observed when using reservations in these realworld scenarios. In both cases, we found that a real-time scheduler can provide the needed predictability for the application in the presence of competing applications, while also providing other benefits, such as minimizing the soft modem's impact on the scheduling predictability of other computations in the system. We also describe the methodologies we used to analyze the real-time behavior of the operating system and applications during these studies, including the use of instrumented kernels to produce execution traces

Predictable scheduling for digital audio

ManuscriptThis paper presents results from applying the Rialto/ NT scheduler to some real Windows 2000 application scenarios. We report on two aspects of this work. First, we studied the reliability of an audio player application and the middleware and kernel components running beneath it in order to assess its reliability under various concurrent application loads. Then we added CPU Reservations to portions of the workload in order to determine if doing so would increase playback reliability under workloads in which problems were previously seen. We report on the benefits and problems observed when using reservations in these real-world scenarios. We also describe the methodologies we used to analyze the real-time behavior of the operating system and applications, including the use of instrumented kernels to produce execution traces. Finally, we describe several improvements in the Rialto/NT implementation that have been made since the system was originally described

EFFECTIVE GROUPING FOR ENERGY AND PERFORMANCE: CONSTRUCTION OF ADAPTIVE, SUSTAINABLE, AND MAINTAINABLE DATA STORAGE

The performance gap between processors and storage systems has been increasingly critical overthe years. Yet the performance disparity remains, and further, storage energy consumption israpidly becoming a new critical problem. While smarter caching and predictive techniques domuch to alleviate this disparity, the problem persists, and data storage remains a growing contributorto latency and energy consumption.Attempts have been made at data layout maintenance, or intelligent physical placement ofdata, yet in practice, basic heuristics remain predominant. Problems that early studies soughtto solve via layout strategies were proven to be NP-Hard, and data layout maintenance todayremains more art than science. With unknown potential and a domain inherently full of uncertainty,layout maintenance persists as an area largely untapped by modern systems. But uncertainty inworkloads does not imply randomness; access patterns have exhibited repeatable, stable behavior.Predictive information can be gathered, analyzed, and exploited to improve data layouts. Ourgoal is a dynamic, robust, sustainable predictive engine, aimed at improving existing layouts byreplicating data at the storage device level.We present a comprehensive discussion of the design and construction of such a predictive engine,including workload evaluation, where we present and evaluate classical workloads as well asour own highly detailed traces collected over an extended period. We demonstrate significant gainsthrough an initial static grouping mechanism, and compare against an optimal grouping method ofour own construction, and further show significant improvement over competing techniques. We also explore and illustrate the challenges faced when moving from static to dynamic (i.e. online)grouping, and provide motivation and solutions for addressing these challenges. These challengesinclude metadata storage, appropriate predictive collocation, online performance, and physicalplacement. We reduced the metadata needed by several orders of magnitude, reducing the requiredvolume from more than 14% of total storage down to less than 12%. We also demonstrate how ourcollocation strategies outperform competing techniques. Finally, we present our complete modeland evaluate a prototype implementation against real hardware. This model was demonstrated tobe capable of reducing device-level accesses by up to 65%

Economic-based Distributed Resource Management and Scheduling for Grid Computing

Computational Grids, emerging as an infrastructure for next generation computing, enable the sharing, selection, and aggregation of geographically distributed resources for solving large-scale problems in science, engineering, and commerce. As the resources in the Grid are heterogeneous and geographically distributed with varying availability and a variety of usage and cost policies for diverse users at different times and, priorities as well as goals that vary with time. The management of resources and application scheduling in such a large and distributed environment is a complex task. This thesis proposes a distributed computational economy as an effective metaphor for the management of resources and application scheduling. It proposes an architectural framework that supports resource trading and quality of services based scheduling. It enables the regulation of supply and demand for resources and provides an incentive for resource owners for participating in the Grid and motives the users to trade-off between the deadline, budget, and the required level of quality of service. The thesis demonstrates the capability of economic-based systems for peer-to-peer distributed computing by developing users' quality-of-service requirements driven scheduling strategies and algorithms. It demonstrates their effectiveness by performing scheduling experiments on the World-Wide Grid for solving parameter sweep applications

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI

Ballard v. Kerr Clerk\u27s Record Dckt. 42611

https://digitalcommons.law.uidaho.edu/idaho_supreme_court_record_briefs/6640/thumbnail.jp