(Georgia Institute of Technology, 2006)
Cooper, Brian F.; Isbell, Charles L.; Pierce, Jeffrey S.; Roberts, David L.; Bhat, Sooraj
People increasingly use a diverse array of computational devices,
including desktop PCs, one or more laptops, a cell phone, a PDA,
tablet PCs, digital music players, automobile computers, and so on. We
present Accord, a middleware system we have implemented to manage
user data across all of these devices. Accord emulates an ideal abstraction
we call a user data-space: a virtual space in which user files exist
independent of any particular physical device. Users put files into the
space with whatever device is convenient, and later access those files
using any of their devices. This abstraction is difficult to implement,
and requires Accord to predict when a file will be needed and on which
device. We describe two mechanisms the middleware uses to support
such predictions: an object graph, which records contextual and statistical
information about file objects, and a file transfer planner, which uses
predictions to determine how to efficiently move files between devices despite
connectivity, bandwidth and storage constraints. Predictions can be
constructed using simple usage statistics, or from more complex machinelearned
models of user activities. We also present experimental results
demonstrating the effectiveness of our system.
(Georgia Institute of Technology, 2006)
Roberts, David L.; Bhat, Sooraj; Isbell, Charles L.; Cooper, Brian F.; Pierce, Jeffrey S.
As users interact with an increasing array of personal computing devices, maintaining consistency of data across those devices becomes significantly more difficult. Typical solutions assume either access to centralized servers, continual connectivity, or unbounded storage and CPU capacity. In practice, users own devices with widely varying processing and storage capabilities that use intermittent or sparsely-connected networks and incur (often asymmetric) transfer costs. We identify the conditions that enable the seamless management of a user's data across devices and present a multi-agent system built upon a decision-theoretic approach to constructing and executing multiple plans to achieve consistency in a peer-to-peer, partially observable, non-deterministic environment. We analyze the performance of these plans in comparison to a standard epidemic replication algorithm used in many database consistency applications.