In this paper we show that application-aware adaptation, a collaborative partnership between the operating system and applications, offers the most general and effective approach to mobile information access. We describe the design of Odyssey, a prototype implementing this approach, and show how it supports concurrent execution of diverse mobile ap-plications. We identify agility as a key attribute of adap-tive systems, and describe how to quantify and measure it. We present the results of our evaluation of Odyssey, indi-cating performance improvements up to a factor of 5 on a benchmark of three applications concurrently using remote services over a network with highly variable bandwidth. 1

Over the past several years, a number of information discovery and access tools have been introduced in the Internet, including Archie, Gopher, Netfind, and WAIS. These tools have become quite popular, and are helping to redefine how people think about wide-area network applications. Yet, they are not well suited to supporting the future information infrastructure, which will be characterized by enormous data volume, rapid growth in the user base, and burgeoning data diversity. In this paper we indicate trends in these three dimensions and survey problems these trends will create for current approaches. We then suggest several promising directions of future resource discovery research, along with some initial results from projects carried out by members of the Internet Research Task Force Research Group on Resource Discovery and Directory Service.

This paper presents an overview of resource discovery services currently available on the Internet. First, we survey a number of existing Internet discovery services. Then, we present a taxonomy of design decisions and characteristics of tools for the Internet resource discovery problem [30]. The Wide Area Information Server

Distributed file systems have come into widespread use in recent years. Many allow files to be accessed over large geographic areas and across organizational boundaries. However, few systems to date have given much thought to how information should be organized in such a global environment. This paper describes the Prospero File System, a file system based on the Virtual System Model, a model for building large systems within which users construct their own virtual systems by selecting and organizing the objects and services of interest. This customized view of a globaJ file system makes it easier for users to keep track of files that they have identified as being of interest. The use of multiple name spaces can cause confusion. Such confusion is eliminated by support for closure: ever) ' object has an associated name space, and names specified by the object are resolved in that name space. Tools are provided to allow views to be kept up-to-date, and to allow views to be defined as functions of other (possibly changing) views. These tools promote sharing and enable the organization of files in ways that make it easier to identify information of interest than it is in existing systems. The prototype implementation has been used to organize information available from Internet archive sites; its directory service has been used from more than 7,500 systems in 29 countries. This paper discusses the goals of the Prospero File System, describes the prototype implementation, and discusses experience with the use of the system to date.

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).

To tune and manage themselves, file and storage systems must understand key properties (e.g., access pattern, lifetime, size) of their various files. This paper describes how systems can automatically learn to classify the properties of files (e.g., read-only access pattern, short-lived, small in size) and predict the properties of new files, as they are created, by exploiting the strong associations between a file's properties and the names and attributes assigned to it. These associations exist, strongly but differently, in each of four real NFS environments studied. Decision tree classifiers can automatically identify and model such associations, providing prediction accuracies that often exceed 90%. Such predictions can be used to select storage policies (e.g., disk allocation schemes and replication factors) for individual files. Further, changes in associations can expose information about applications, helping autonomic system components distinguish growth from fundamental change.

Attribute-based naming enables powerful search and organization tools for ever-increasing user data sets. However, such tools are only useful in combination with accurate attribute assignment. Existing systems rely on user input and content analysis, but they have enjoyed minimal success. This paper discusses new approaches to automatically assigning attributes to files, including several forms of context analysis, which has been highly successful in the Google web search engine. With extensions like application hints (e.g., web links for downloaded files) and inter-file relationships, it should be possible to infer useful attributes for many files, making attribute-based search tools more effective.

Perspective uses a new semantic filesystem construct, the view, to simplify management of distributed storage in the home. A view is a semantic description of a set of files, specified as a a query on file attributes. In Perspective, users can identify and control the files stored on a given device by examining and modifying the views associated with it. This approach allows them to reason about what is where in the same way (semantic naming) as they navigate their digital content. Thus, in serving as their own administrators, users do not have to deal with a second data organization scheme (hierarchical naming) to perform replica management tasks, such as specifying redundancy to provide reliability and data partitioning to address device capacity exhaustion. A set of extensive user studies confirm the difficulties created by current approaches and the efficacy of view-based data management.

Over the past several years, a number of information discovery and access tools have been introduced in the Internet, including Archie, Gopher, Netfind, and WAIS. These tools have become quite popular, and are helping to redefine how people think about wide area network applications. Yet, they are not well suited to supporting the future information infrastructure, which will be characterized by enormous data volume, rapid growth in the user base, and burgeoning data diversity. In this paper we indicate trends in these three dimensions, and survey problems these trends will create for current approaches. We then suggest several promising directions of future resource discovery research, along with some initial results from projects carried out by members of the Internet Research Task Force Research Group on Resource Discovery and Directory Service.

Personal data is growing at ever increasing rates, fueled by a growing market for personal computing solutions and dramatic growth of available storage space on these platforms. Users, no longer limited in what they can store, are now faced with the problem of organizing their data such that they can find it again later. Unfortunately, as data sets grow the complexity of organizing these sets also grows. This problem has driven a sudden growth in search tools aimed at the personal computing space, designed to assist users in locating data within their disorganized file space.