IEEE access, 2020, Vol.8, p.202858-202871
2020
Details
- Autor(en) / Beteiligte
- Titel
- Optimization and Decomposition Methods in Network Traffic Prediction Model: A Review and Discussion
- Ist Teil von
- IEEE access, 2020, Vol.8, p.202858-202871
- Ort / Verlag
- Piscataway: IEEE
- Erscheinungsjahr
- 2020
- Link zum Volltext
- Quelle
- EZB Electronic Journals Library
- Beschreibungen/Notizen
- The 21st century is a high-tech information era in which our lives are closely linked by computer networks. Hence, how to effectively supervise networks and reduce the frequency of network security incidents has now become a research hotspot in cyberspace. Specifically, researchers have shown an increased interest in predicting the network traffic before any untoward incident happens. Optimization and decomposition technologies are the core components of network traffic prediction model which plays an important role in network management. This article discusses past network traffic prediction research and critically examines the optimization and decomposition technologies used in the model, lists the model parameter structure based on the research methodology, the data set used, the evaluation criteria and so on. By comparison, digging out the Particle Swarm Optimization (PSO) algorithm and the Variational Mode Decomposition (VMD) decomposition technique will effectively solve the network traffic model predictive difficulties that have proven to be crucial to improving predictive accuracy and convergence speed strategy.The comprehensive review reveals that PSO and VMD are the most suitable optimization algorithm and decomposition technology for network traffic prediction modeling.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 2169-3536eISSN: 2169-3536DOI: 10.1109/ACCESS.2020.3036421Titel-ID: cdi_crossref_primary_10_1109_ACCESS_2020_3036421
- Format
- –
- Schlagworte
- Algorithms, Communications traffic, Computer networks, Convergence, Data models, Decomposition, Decomposition technology, Internet, network traffic prediction, Optimization, optimization algorithm, Particle swarm optimization, Prediction algorithms, Prediction models, Predictive models, Research methodology, Security, Telecommunication traffic, Traffic models, variational mode decomposition