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Record W80243497

Implementation of BGP in a network simulator

2004· dissertation· en· W80243497 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSummit (Simon Fraser University) · 2004
Typedissertation
Languageen
FieldComputer Science
TopicNetwork Traffic and Congestion Control
Canadian institutionsSimon Fraser University
FundersSimon Fraser University
KeywordsBorder Gateway ProtocolThe InternetScalabilityComputer scienceNetwork mappingComputer networkDefault-free zoneRouting protocolInternet ProtocolInternet layerProtocol (science)Network simulationDistributed computingRouting (electronic design automation)World Wide WebOperating systemRouting tableLink-state routing protocol
DOInot available

Abstract

fetched live from OpenAlex

Border Gateway Protocol (BGP) is the inter-domain routing protocol currently employed in the Internet.Internet growth imposes increasing requirements on BGP performance.Recent studies revealed that performance degradations in BGP are due to the highly dynamic nature of the Internet.Undesirable properties of BGP, such as poor integrity, slow convergence, and divergence, have been reported by the research community.Theoretical analysis and empirical measurements have been employed in the past, albeit with certain limitations.Simulations allow more realistic experiments with fewer simplifications than the theoretical approach.They also provide more enhanced flexibility than empirical studies permit.In this thesis, we describe the design and implementation of a BGP-4 model (ns-BGP) in the network simulator ns-2 by porting the BGP-4 implementation from SSFNet.The ns-BGP node is based on the existing ns-2 unicast node and the SSF.OS.BGP4 model from SSFNet.In order to provide socket support and at the same time maintain the structure of SSF.OS.BGP4, we also ported to ns-2 TcpSocket, the socket layer implementation of SSFNet.In order to support the lPv4 addressing and packet forwarding, the basic address classifier in ns-2 was replaced with a new address classifier named IPv4ClassiJier.We also modified FullTcpAgent, the TCP agent used by TcpSocket, to support user data transmission.We performed a suite of validation tests to ensure that the ns-BGP model complies with the BGP-4 specifications, including BGP-4 features such as: basic peer session management (keep and drop peer), route selection, reconnection, internal BGP (iBGP), and route reflection.Finally, in the scalability analysis of ns-BGP, we showed that the model scales with respect to the number of peer sessions and the size of routing tables.We added the following data structures and classes to support TcpSocket capable of user data transmission: SendQueue class that stores the data requested to be sent by sender TCP agent, ReceiveQueue class that stores the received data from the sender, and TcpData class that contains the transmitted user data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.448
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.226
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it