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Record W380789892

On fault-tolerance and security in MPLS networks

2008· dissertation· en· W380789892 on OpenAlexaff
Sahel Alouneh

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2008
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Optical Network Technologies
Canadian institutionsConcordia University
Fundersnot available
KeywordsMultiprotocol Label SwitchingComputer networkComputer scienceQuality of serviceNetwork packet
DOInot available

Abstract

fetched live from OpenAlex

Multi-Protocol Label Switching (MPLS) is an evolving network technology that is used to provide Traffic Engineering (TE) and high speed networking. Internet service providers, which support MPLS technology, are increasingly required to provide high Quality of Service (QoS) guarantees and security. One of the aspects of QoS is fault tolerance. It is defined as the property of a system to continue operating in the event of failure of some of its parts. Fault tolerance techniques are very useful to maintain the survivability of the network by recovering from failure within acceptable delay and minimum packet-loss while efficiently utilizing network resources. On the other hand, with the increasing deployment of MPLS networks, security concerns have been raised. The basic architecture of MPLS networks does not support security aspects such as data confidentiality, data integrity, and availability. MPLS technology has emerged mainly to provide high speed packet delivery. As a result security considerations have not been discussed thoroughly until recent demands for security have emerged by most providers and researchers. In this thesis, we propose a new method that has a two-fold objective: to provide fault tolerance and to enhance the security in MPLS networks. Our approach uses a modified (k, n) threshold sharing scheme (TSS) combined with multi-path routing. An IP packet entering MPLS network is partitioned into n MPLS packets, which are each assigned to disjoint or maximally disjoint Label Switched Path (LSP) across the MPLS network. Receiving MPLS packets from k out of n LSPs are sufficient to reconstruct the original IP packet. From the security point of view, the modified TSS provides data confidentiality, integrity, availability and IP spoofing. In addition, fault tolerance in MPLS is supported using reasonable resources. The recovery from node/link failure and/or transmission errors is provided with no delay or packet loss. Packet re-ordering may not be required if packets are lost due to failure. However, sequencing is considered in our approach to identify packets with transmission errors. In order to provide fault tolerance, our scheme requires n > k . However, for security purposes, if the target is only to provide data confidentiality, then only a modified (k, k ) TSS algorithm is sufficient and consequently no significant redundant bandwidth is required. To verify that our approach does not require long processing time, we conducted simulations that show the modified TSS processing time does not significantly affect the packet transmission time. RSVP-TE is the MPLS signaling protocol used to establish LSPs. Extensions required to support multi-path routing in RSVP-TE are also studied. The impact of multi-path routing and modified TSS on MPLS security and fault tolerance is investigated and compared with single routing. The connection intrusion probability and connection failure probability have shown lower values when multi-path routing is used. The application of IPSec security protocol in MPLS networks is also investigated. Finally, we applied the modified threshold sharing scheme on MPLS multicast networks, where both the source specific tree approach and the group shared tree approach are considered.

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.

How this classification was reachedexpand

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), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
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
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.003
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 head0.012
GPT teacher head0.248
Teacher spread0.235 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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