Performance Management of Optical Transport Networks through Time Series Forecasting

Cavalcante, Jefferson; Patel, Ahmed; Celestino, Joaquim

doi:10.1109/aina.2017.117

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Time domain methods use historical data to identify patterns and pattern changes. They are commonly applied in diverse fields such as finance [ 96 ], electricity markets, retail and optical transport networks [ 97 ]. Although there exist multiple models for time-series forecasting, we limit this review to the most commonly used ones and exclude more application-specific methods such as ‘robust-trend’ [ 98 ] and ‘theta’ [ 99 ].…”

Section: Quantitative Forecasting Methodsmentioning

confidence: 99%

A survey of human judgement and quantitative forecasting methods

et al. 2021

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This paper's top-level goal is to provide an overview of research conducted in the many academic domains concerned with forecasting. By providing a summary encompassing these domains, this survey connects them, establishing a common ground for future discussions. To this end, we survey literature on human judgement and quantitative forecasting as well as hybrid methods that involve both humans and algorithmic approaches. The survey starts with key search terms that identified more than 280 publications in the fields of computer science, operations research, risk analysis, decision science, psychology and forecasting. Results show an almost 10-fold increase in the application-focused forecasting literature between the 1990s and the current decade, with a clear rise of quantitative, data-driven forecasting models. Comparative studies of quantitative methods and human judgement show that (1) neither method is universally superior, and (2) the better method varies as a function of factors such as availability, quality, extent and format of data, suggesting that (3) the two approaches can complement each other to yield more accurate and resilient models. We also identify four research thrusts in the human/machine-forecasting literature: (i) the choice of the appropriate quantitative model, (ii) the nature of the interaction between quantitative models and human judgement, (iii) the training and incentivization of human forecasters, and (iv) the combination of multiple forecasts (both algorithmic and human) into one. This review surveys current research in all four areas and argues that future research in the field of human/machine forecasting needs to consider all of them when investigating predictive performance. We also address some of the ethical dilemmas that might arise due to the combination of quantitative models with human judgement.

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Section: Quantitative Forecasting Methodsmentioning

confidence: 99%

A survey of human judgement and quantitative forecasting methods

et al. 2021

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OTN-over-WDM optimization in 5G networks: key challenges and innovation opportunities

et al. 2023

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The continued growth of both mobile broadband and fixed broadband subscriptions as well as the added deployment of Internet of Things devices has led to making 5G networks a reality. More specifically, 5G networks are expected to support a diverse set of new applications/services in addition to existing applications/services from previous generations (2G/3G/4G). The COVID-19 pandemic has further increased the demand for such services which has resulted in a further surge in the Internet usage. Thus, 5G networks are expected to have a highly flexible architecture at all levels including at the radio, core, and transport levels. Optical Transport Networks (OTN) have been proposed as one potential and promising supporting technology for 5G networks at the transport level, particularly for next generation transport networks featuring large-granule broadband service transmissions. This is because it allows for more flexible, efficient, and dynamic networks. However, adopting and deploying OTNs in 5G networks comes with its own set of challenges including control, management, and orchestration of such networks as well as their security. Accordingly, this paper overviews 5G networks along with their requirements and provides a brief summary of OTNs and the corresponding optimization mechanisms. Additionally, this work discusses the challenges facing OTNs and their optimization within the context of 5G. Moreover, it outlines some of the key research areas and opportunities for innovation stemming from the data-driven intelligent networking paradigm using Machine Learning techniques.

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Applying Smart Security Model to Protect the Client Services from the Threats of the Optical Network

Rahouma

Ali

2020

2020 2nd Novel Intelligent and Leading Emerging Sciences Conference (NILES)

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Performance Management of Optical Transport Networks through Time Series Forecasting

Cited by 4 publications

References 15 publications

A survey of human judgement and quantitative forecasting methods

A survey of human judgement and quantitative forecasting methods

OTN-over-WDM optimization in 5G networks: key challenges and innovation opportunities

Applying Smart Security Model to Protect the Client Services from the Threats of the Optical Network

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