Blurring the Line Between Oses and Storage Devices (CMU-CS-01-166)

This report makes a case for more expressive interfaces between operating systems (OSes) and storage devices. In today’s systems, the storage interface consists mainly of simple read and write commands; as a result, OSes operate with little understanding of device-specific characteristics and devices operate with little understanding of system priorities. More expressive interfaces, together with extended versions of today’s OS and firmware specializations, would allow the two to cooperate to achieve performance and functionality that neither can achieve alone. This report consists of the technical content of an NSF proposal submitted in January 2001 and funded in June 2001 under the Information Technology Research (ITR) program. The only divergence from the original proposal is the removal of non-technical content (e.g., budgets, biographies, and results from prior NSF support)

Authentication Confidences (CMU-CS-01-123)

"Over the Internet, no one knows you're a dog," goes the joke. Yet, in most systems, a password submitted over the Internet gives one the same access rights as one typed at the physical console. We promote an alternate approach to authentication, in which a system fuses observations about a user into a probability (an authentication confidence) that the user is who they claim to be. Relevant observations include password correctness, physical location, activity patterns, and biometric readings. Authentication confidences refine current yes-or-no authentication decisions, allowing systems to cleanly provide partial access rights to authenticated users whose identities are suspect

Co-Scheduling of Disk Head Time in Cluster-Based Storage (CMU-PDL-08-113)

Disk timeslicing is a promising technique for storage performance insulation. To work with cluster-based storage, however, timeslices associated with striped data must be co-scheduled on the corresponding servers. This paper describes algorithms for determining global timeslice schedules and mechanisms for coordinating the independent server activities. Experiments with a prototype show that, combined, they can provide performance insulation for workloads sharing a storage cluster—each workload realizes a configured minimum efficiency within its timeslices regardless of the activities of the other workload

Automated Disk Drive Characterization (CMU-CS-99-176)

DIXtrac is a program that automatically characterizes the performance of modern disk drives. This report describes and validates DIXtrac's algorithms, which extract accurate values for over 100 performance-critical parameters in 2 to 6 minutes without human intervention or special hardware support. The extracted data include detailed layout and geometry information, mechanical timings, cache management policies, and command processing overheads. DIXtrac is validated by configuring a detailed disk simulator with its extracted parameters; in most cases, the resulting accuracies match those of the most accurate disk simulators reported in the literature. To date, DIXtrac has been successfully used on over 20 disk drives, including eight different models from four different manufacturers

Enabling Dynamic Security Management of via Device-Embedded Security (CMU-CS-00-174)

This report contains the technical content of a recent funding proposal. In it, we propose a new approach to network security in which each individual device erects its own security perimeter and defends its own critical resources. Together with conventional border defenses (e.g., firewalls and OS kernels), such {\it self-securing devices} could provide a flexible infrastructure for dynamic prevention, detection, diagnosis, isolation, and repair of successful breaches in borders and device security perimeters. Managing network security is difficult in current systems, because a small number of border protections are used to protect a large number of resources. We plan to explore the fundamental principles and practical costs/benefits of embedding security functionality into infrastructural devices, such as network interface cards (NICs), network-attached storage (NAS) devices, video surveillance equipment, and network switches and routers. The report offers several examples of how different devices might be extended with embedded security functionality and outlines some challenge of designing and managing self-securing devices

A Human Organization Analogy for Self-* Systems (CMU-CS-03-129)

The structure and operation of human organizations, such as corporations, offer useful insights to designers of self-* systems (a.k.a. self-managing or autonomic). Examples include worker/supervisor hierarchies, avoidance of micro-management, and complaint-based tuning. This paper explores the analogy, and describes the design of a self-* storage system that borrows from it

Connections: Using Context to Enhance File Search (CMU-PDL-05-105)

Connections is a file system search tool that combines traditional content-based search with context information gathered from user activity. By tracing file system calls, Connections can identify temporal relationships between files and use them to expand and reorder traditional content search results. Doing so improves both recall (reducing falsepositives) and precision (reducing false-negatives). For example, Connections improves the average recall (from 13% to 22%) and precision (from 23% to 29%) on the first ten results. When averaged across all recall levels, Connections improves precision from 17% to 28%. Connections provides these benefits with only modest increases in average query time (2 seconds), indexing time (23 seconds daily), and index size (under 1% of the user's data set)

MEMS-Based Storage Devices and Standard Disk Interfaces: A Square Peg in a Round Hole? (CMU-PDL-03-102)

MEMS-based storage devices are a new technology that is significantly different from both disk drives and semiconductor memories. These differences motivate the question of whether they need new abstractions to be utilized by systems, or if existing abstractions will work well. This paper addresses this question by examining the fundamental reasons that the abstraction works for existing systems, and by showing that these reasons hold for MEMS-based storage. This result is borne out through several case studies of proposed roles MEMS-based storage devices may take in future systems, and potential policies that may be used to tailor systems’ access to MEMS-based storage. We argue that when considering the use of MEMS-based storage in systems, their performance should be compared to that of a hypothetical disk drive that matches the speed of a MEMS-based storage device. We discuss exceptional workloads that can use specific features of MEMS-based storage devices and that may require extensions to current abstractions. Also, we consider the ramifications of the assumptions that are made in today’s models of MEMS-based storage devices

A human organization analogy for self-* systems

Abstract: "The structure and operation of human organizations, such as corporations, offer useful insights to designers of self-* systems (a.k.a. self-managing or automatic). This paper explores the analogy, and describes the design of a self-* storage system that borrows from it.

Toward Automatic Context-based Attribute Assignment for Semantic File Systems (CMU-PDL-04-105)

Semantic file systems enable users to search for files based on attributes rather than just pre-assigned names. This paper develops and evaluates several new approaches to automatically generating file attributes based on context, complementing existing approaches based on content analysis. Context captures broader system state that can be used to provide new attributes for files, and to propagate attributes among related files; context is also how humans often remember previous items [2], and so should fit the primary role of semantic file systems well. Based on our study of ten systems over four months, the addition of context-based mechanisms, on average, reduces the number of files with zero attributes by 73%. This increases the total number of classifiable files by over 25% in most cases, as is shown in Figure 1. Also, on average, 71% of the content-analyzable files also gain additional valuable attributes