Implementing Distributed Systems Using Linear Naming

Item

Title: en_US Implementing Distributed Systems Using Linear Naming
Creator: en_US Bawden, Alan
Date: 2004-10-20T20:29:19Z
Date Available: 2004-10-20T20:29:19Z
Date Issued: en_US 1993-03-01
Identifier: en_US AITR-1627
uri: http://hdl.handle.net/1721.1/7085
Abstract: en_US Linear graph reduction is a simple computational model in which the cost of naming things is explicitly represented. The key idea is the notion of "linearity". A name is linear if it is only used once, so with linear naming you cannot create more than one outstanding reference to an entity. As a result, linear naming is cheap to support and easy to reason about. Programs can be translated into the linear graph reduction model such that linear names in the program are implemented directly as linear names in the model. Nonlinear names are supported by constructing them out of linear names. The translation thus exposes those places where the program uses names in expensive, nonlinear ways. Two applications demonstrate the utility of using linear graph reduction: First, in the area of distributed computing, linear naming makes it easy to support cheap cross-network references and highly portable data structures, Linear naming also facilitates demand driven migration of tasks and data around the network without requiring explicit guidance from the programmer. Second, linear graph reduction reveals a new characterization of the phenomenon of state. Systems in which state appears are those which depend on certain -global- system properties. State is not a localizable phenomenon, which suggests that our usual object oriented metaphor for state is flawed.
Extent: 1051233 bytes; 1810543 bytes
Format: application/postscript; application/pdf
Language: en_US
Relation: en_US AITR-1627

Item sets: AI Technical Reports (1964 - 2004)