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%

Improving I/O performance through an in-kernel disk simulator

This paper presents two mechanisms that can significantly improve the I/O performance of both hard and solid-state drives for read operations: KDSim and REDCAP. KDSim is an in-kernel disk simulator that provides a framework for simultaneously simulating the performance obtained by different I/O system mechanisms and algorithms, and for dynamically turning them on and off, or selecting between different options or policies, to improve the overall system performance. REDCAP is a RAM-based disk cache that effectively enlarges the built-in cache present in disk drives. By using KDSim, this cache is dynamically activated/deactivated according to the throughput achieved. Results show that, by using KDSim and REDCAP together, a system can improve its I/O performance up to 88% for workloads with some spatial locality on both hard and solid-state drives, while it achieves the same performance as a ‘regular system’ for workloads with random or sequential access patterns.Peer ReviewedPostprint (author's final draft

Extensible synthetic file servers? or: Structuring the glue between tester and system under test

We discuss a few simple scenarios of how we can design and develop a compositional synthetic file server that gives access to external processes – in particular, in the context of testing, gives access to the system under test – such that certain parts of said synthethic file server can be prepared as off-the-shelf components to which other specifically written parts can be added in a kind of plug-and-play fashion.\ud \ud The approaches only deal with the problem of accessing the system under test from the point of view of offered functionality, and compositionality, but do not consider efficiency or performance. \ud \ud The study is rather preliminary, and only very limited practical experiments have been performed

Emoji Company GmbH v Schedule A Defendants

Declaration of Dean Eric Goldma

Vulnerability analysis of AIS-based intrusion detection systems using genetic and evolutionary hackers

In this thesis, an overview of current intrusion detection methods, evolutionary computation, and immunity-based intrusion detection systems (IDSs) is presented. An application named Genetic Interactive Teams for Intrusion Detection Design and Analysis (GENERTIA) is introduced which uses genetic algorithm (GA)-based hackers known as a red team in order to find vulnerabilities, or holes, in an artificial immune system (AlS)-based IDS. GENERTIA also uses a GA-based blue team in order to repair the holes it finds. The performance of the GA-based hackers is tested and measured according to the number of distinct holes that it finds. The GA-based red team�s behavior is then compared to that of 12 variations of the particle swarm optimization (PSO)-based red team named SWO, SW0+, SW1, SW2, SW3, SW4, CCSWO, CCSW0+, CCSW1, CCSW2, CCSW3, and CCSW4. Each variant of the PSO-based red team differs in terms of the way that it searches for holes in an IDS. Through this test, it is determined that none of the red teams based on PSO perform as well as the one based on a GA. However, two of the twelve PSO-based red teams, CCSW4 and SW0+, provide hole finding capabilities closest to that of the GA. In addition to the ability of the different red teams to find holes in an AlS-based IDS, the search behaviors of the GA-based hackers, PSO-based hackers that use a variable called a constriction coefficient, and PSO-based hackers that do not use the coefficient are compared. The results of this comparison show that it may be possible to implement a red team based on a hybrid �genetic swarm� that improves upon the performance of both the GA- and PSO-based red teams

Emoji Company GmbH v Schedule A Defendants

Declaration of Dean Eric Goldma

Automatic Code Placement Alternatives for Ad-Hoc And Sensor Networks

Developing applications for ad-hoc and sensor networks poses significant challenges. Many interesting applications in these domains entail collaboration between components distributed throughout an ad-hoc network. Defining these components, optimally placing them on nodes in the ad-hoc network and relocating them in response to changes is a fundamental problem faced by such applications. Manual approaches to code and data migration are not only platform-dependent and error-prone, but also needlessly complicate application development. Further, locally optimal decisions made by applications that share the same network can lead to globally unstable and energy inefficient behavior. In this paper we describe the design and implementation of a distributed operating system for ad-hoc and sensor networks whose goal is to enable power-aware, adaptive, and easy-to-develop ad-hoc networking applications. Our system achieves this goal by providing a single system image of a unified Java virtual machine to applications over an ad-hoc collection of heterogeneous nodes. It automatically and transparently partitions applications into components and dynamically finds a placement of these components on nodes within the ad-hoc network to reduce energy consumption and increase system longevity. This paper outlines the design of our system and evaluates two practical, power-aware, online algorithms for object placement that form the core of our system. We demonstrate that our algorithms can increase system longevity by a factor of four to five by effectively distributing energy consumption, and are suitable for use in an energy efficient operating system in which applications are distributed automatically and transparently

A File System Component Compiler

File System development is a difficult and time consuming task, the results of which are rarely portable across operating systems. Several proposals to improve the vnode interface to allow for more flexible file system design and implementation have been made in recent years, but none is used in practice because they require costly fundamental changes to kernel interfaces, only operating systems vendors can make those changes, are still non-portable, tend to degrade performance, and do not appear to provide immediate return on such an investment. This proposal advocates a language for describing file systems, called FiST. The associated translator can generate portable C code — kernel resident or not — that implements the described file system. No kernel source code is needed and no existing vnode interface must change. The performance of the file systems automatically generated by FiST can be within a few percent of comparable hand-written file systems. The main benefits to automation are that development and maintenance costs are greatly reduced, and that it becomes practical to prototype, implement, test, debug, and compose a vastly larger set of such file systems with different properties. The proposed thesis will describe the language and its translator, use it to implement a few file systems on more than one platform, and evaluate the performance of the automatically generated code