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Nathan Orr

dblp:93/267 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2002
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Computer networks · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Distributed systems · 100%
Theoretical computer science
1 paper
Automata and formal languages · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems
code migration
0.012002
A Model for Mobile Code Using Interacting Automata · IEEE Trans. Mob. Comput. 2002
Distributed systems › distributed mobile computing
code mobility
0.012002
A Model for Mobile Code Using Interacting Automata · IEEE Trans. Mob. Comput. 2002
Distributed systems › distributed mobile computing
mobile agents
0.012002
A Model for Mobile Code Using Interacting Automata · IEEE Trans. Mob. Comput. 2002
Automata and formal languages
cellular automata
0.012002
A Model for Mobile Code Using Interacting Automata · IEEE Trans. Mob. Comput. 2002

Methods — techniques the papers use, named apart from their topics

simulation · 0.1
YearPublicationVenuePosition
2002 A Model for Mobile Code Using Interacting Automata
abstract
The Internet supports migration of code from node to node. A number of paradigms exist for distributed computing and mobile code, including client/server, remote evaluation, code-on-demand, and mobile agents. We find them overly-restrictive views of reality. We propose a model that can express previous paradigms as special cases. We derive a model using cellular automata (CA) abstractions to study relations between local node behavior and global system behavior. Example mobile code systems are provided and existing paradigms are expressed in terms of the model. These examples include network attacks such as viruses, worms, and distributed denial of service (DDoS). A distributed system simulation tool based on the model is described. Simulation results provide insights gained from this work.
Richard R. Brooks, Nathan Orr
IEEE Trans. Mob. Comput.2