A tantárgyleírás hatályossága
| Subject name (Hungarian, English) |
Tömegkiszolgálás I.
Queueing Theory I.
|
||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subject code | BMEVITTD069 | ||||||||||||
| Subject type | — | ||||||||||||
| Training Level | — | ||||||||||||
| Course types and hours (weekly/semester) |
|
||||||||||||
| Assessment type | vizsga | ||||||||||||
| Credits | 5 | ||||||||||||
| Subject coordinator |
DR. Molnár Sándor
position: egyetemi docens
contact:
molnar.sandor@vik.bme.hu
|
||||||||||||
| Responsible department |
Távközlési és Mesterséges Intelligencia Tanszék
|
||||||||||||
| Faculty | Villamosmérnöki és Informatikai Kar | ||||||||||||
| Subject website | — | ||||||||||||
| Primary curriculum type | — | ||||||||||||
| Direct prerequisites – Strong prerequisite | none | ||||||||||||
| Direct prerequisites – Weak prerequisite | none | ||||||||||||
| Direct prerequisites – Parallel prerequisite | none | ||||||||||||
| Direct prerequisites – Milestone prerequisite | none | ||||||||||||
| Direct prerequisites – Exclusion | none |
Objectives
Traffic characterization and modeling
Traffic descriptors
Applications of stochastic processes in modeling
Study guides for traffic analysis of measured Internet traffic
Fractal traffic theory
Self-similarity and long-range dependence
Heavy-tailed distributions in the Internet
Fractal traffic models
Traffic management and congestion control
Traffic management methods
Open-loop and closed-loop control
TCP performance models
Statistical inequalities, large deviations and their applications to data science
examples in traffic engineering, risk management and insurance
resource estimation based on analyzing rare events
distribution-free estimations of event frequencies
computational models for large
systems
Large and complex networks
structural similarities and differences of large complex networks (Internet, airport networks, road network, brain networks, word networks, metabolic networks)
small-world properties, degree distributions and clustering
routing and navigability in large networks
modularity, rich club phenomenon
and controllability
Case studies, examples, analytical and numerical investigations to be performed
by Wolfram Mathematica
No objectives provided.
Learning outcomes
Ez a tantárgy a KKK rendeletben meghatározott, következő kompetenciák fejlesztését szolgálja:
Knowledge
No learning outcomes recorded.
Skills
No learning outcomes recorded.
Attitudes
No learning outcomes recorded.
Autonomy and responsibility
No learning outcomes recorded.
Oktatási módszertan
Tanulástámogató anyagok
Online források
Recommended preliminary knowledge for completing the subject
General rules
Assessment methods
In-term assessments
No detailed assessments provided.
Weight of in-term assessments
No weights provided.
Exam-period assessments
No detailed assessments provided.
Weight of exam elements
No weights provided.
Grade calculation
No grade thresholds provided.
Attendance requirements
No attendance requirements provided.
Rules for retake and resubmission
Not provided.
Short description
Not provided.
Detailed description
Not provided.
Recommended courses
Not provided.
Workload to complete the subject
No workload breakdown provided.
Validity of subject requirements
Curriculum placement
No curriculum placements recorded for this subject version.