An Encoder-Decoder Deep Learning Approach for Multistep Service Traffic Prediction

Θεοδωρόπουλος, Θεόδωρος; Maroudis, Angelos-Christos; Violos, John; Tserpes, Konstantinos

doi:10.1109/bigdataservice52369.2021.00010

Cited by 15 publications

(5 citation statements)

References 21 publications

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“…A more comprehensive comparison with other state-of-the-art approaches would provide a better understanding of the performance of the proposed approach. For single-step and multi-step prediction, Theodoropoulos, Theodoros et al [ 16 ] compared the performance of the statistical model and the deep learning model. They used four distinct designs of the deep encoder–decoder model for traffic prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Saha,

Haque,

Sidebottom

2024

Sensors

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The ISP (Internet Service Provider) industry relies heavily on internet traffic forecasting (ITF) for long-term business strategy planning and proactive network management. Effective ITF frameworks are necessary to manage these networks and prevent network congestion and over-provisioning. This study introduces an ITF model designed for proactive network management. It innovatively combines outlier detection and mitigation techniques with advanced gradient descent and boosting algorithms, including Gradient Boosting Regressor (GBR), Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGB), CatBoost Regressor (CBR), and Stochastic Gradient Descent (SGD). In contrast to traditional methods that rely on synthetic datasets, our model addresses the problems caused by real aberrant ISP traffic data. We evaluated our model across varying forecast horizons—six, nine, and twelve steps—demonstrating its adaptability and superior predictive accuracy compared to traditional forecasting models. The integration of the outlier detection and mitigation module significantly enhances the model’s performance, ensuring robust and accurate predictions even in the presence of data volatility and anomalies. To guarantee that our suggested model works in real-world situations, our research is based on an extensive experimental setup that uses real internet traffic monitoring from high-speed ISP networks.

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Section: Literature Reviewmentioning

confidence: 99%

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Saha,

Haque,

Sidebottom

2024

Sensors

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“…Towards that end, we employed the proposed architecture that enabled the out-of-the box integration of a model for proactive autoscaling that considers a richer state-space than mere runtime compute utilization. The model is based on a novel multi-step Deep-Learning prediction mechanism [59] that was originally used to predict network traffic. We conducted an experiment to evaluate this mechanism against a typical reactive autoscaler.…”

Section: B Closed Loop Examplementioning

confidence: 99%

Toward Supporting XR Services: Architecture and Enablers

Taleb

Boudi²,

Rosa

et al. 2023

IEEE Internet Things J.

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Emerging XR applications, including Holography, Augmented, Virtual and Mixed Reality, are characterized by unprecedented requirements for Quality of experience (QoE), largely exceeding those currently attainable. To cope with these requirements, noticeable efforts and a number of initiatives are ongoing to enhance the current communications technologies, especially in the direction of supporting ultra-low latency and increased bandwidth. This work proposes an architecture that puts together the key enablers to support future XR applications, highlighting the shortcomings of existing technologies and leveraging the ongoing innovations. It demonstrates the feasibility of the proposed architecture by describing the processes driving the platform with relevant use case scenarios, and mapping the envisioned functionality to existing tools.

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“…Security-wise, this provides many advantages for bolstering threat detection and prevention [104][105][106]. AI security mechanisms can facilitate the analysis of user behavior, system interactions, and network traffic [107] within applications to identify anomalies or suspicious activities that may indicate potential security breaches. Native AI enhances access control mechanisms by harnessing cutting-edge technologies like biometric recognition, voice authentication, and gaze tracking, ensuring robust verification of user identities.…”

Section: Native Ai and Security As A Service (Secaas)mentioning

confidence: 99%

Security in Cloud-Native Services: A Survey

Theodoropoulos,

Rosa,

Benzaid

et al. 2023

JCP

Self Cite

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Cloud-native services face unique cybersecurity challenges due to their distributed infrastructure. They are susceptible to various threats like malware, DDoS attacks, and Man-in-the-Middle (MITM) attacks. Additionally, these services often process sensitive data that must be protected from unauthorized access. On top of that, the dynamic and scalable nature of cloud-native services makes it difficult to maintain consistent security, as deploying new instances and infrastructure introduces new vulnerabilities. To address these challenges, efficient security solutions are needed to mitigate potential threats while aligning with the characteristics of cloud-native services. Despite the abundance of works focusing on security aspects in the cloud, there has been a notable lack of research that is focused on the security of cloud-native services. To address this gap, this work is the first survey that is dedicated to exploring security in cloud-native services. This work aims to provide a comprehensive investigation of the aspects, features, and solutions that are associated with security in cloud-native services. It serves as a uniquely structured mapping study that maps the key aspects to the corresponding features, and these features to numerous contemporary solutions. Furthermore, it includes the identification of various candidate open-source technologies that are capable of supporting the realization of each explored solution. Finally, it showcases how these solutions can work together in order to establish each corresponding feature. The insights and findings of this work can be used by cybersecurity professionals, such as developers and researchers, to enhance the security of cloud-native services.

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An Encoder-Decoder Deep Learning Approach for Multistep Service Traffic Prediction

Cited by 15 publications

References 21 publications

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Multi-Step Internet Traffic Forecasting Models with Variable Forecast Horizons for Proactive Network Management

Toward Supporting XR Services: Architecture and Enablers

Security in Cloud-Native Services: A Survey

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