Internet Traffic Volumes are Not Gaussian—They are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction

Alasmar, Mohammed; Clegg, R. E. S.; Zakhleniuk, N. A.; Parisis, George

doi:10.1109/tnet.2021.3059542

Cited by 23 publications

(6 citation statements)

References 46 publications

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“…e larger value of H(ε i /U i ) indicates that there is a lager deviation between the actual network state and normal state, and the probability of this phenomenon P(i) is low. Actually, the RTT of links is stable over a longer period of time [36,37]. Hence, for each link, the relative entropy between the measured value and reference value is small, and the probability P(i) is always large.…”

Section: Workflowmentioning

confidence: 99%

On the Modeling of RTT Time Series for Network Anomaly Detection

Kuang

Huang

et al. 2022

Security and Communication Networks

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Network anomalies can seriously influence the performance of networks and cause huge financial losses. Existing studies modeled the round-trip time (RTT) time series of each link and identified their abnormal patterns independently to detect the network anomalies. However, they rarely investigated the correlation among links, and they rarely considered the goodness of fit and complexity in model selection, which led to low timeliness and accuracy of detection. They failed to understand the impact of network anomalies. In this work, we propose the RTS detection approach to address these challenges. Specifically, we, firstly, propose a link clustering method to cluster the links into different classes based on the topological location of pairwise links and the similarity between their RTT time series. Then, for each class of links, we consider the goodness of fit and complexity in model selection and select the suitable model to analyze their RTT time series. Finally, we propose a detection method to detect the network anomalies by observing the deviation between the probability density distribution of the current RTT values and the reference value. We perform experiments with data from public measurement infrastructures like RIPE Atlas to evaluate the performance of our approach. The results show that our approach can not only reduce the detection time and improve the accuracy of detection effectively but also can roughly evaluate the impact of network anomalies.

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Section: Workflowmentioning

confidence: 99%

On the Modeling of RTT Time Series for Network Anomaly Detection

Kuang

Huang

et al. 2022

Security and Communication Networks

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“…We utilized the publicly available MAWI dataset for Internet traffic tracking trajectories (https://mawi.wide.ad.jp/mawi/ditl/ditl2018-G/, accessed on 20 November 2023). These traces have been demonstrated to exhibit a log-normal distribution [26][27][28] and comprise IP-level traffic observed in favorable positions within WIDE from 14:00 to 14:15 each day. The traces include anonymized IP and MAC headers.…”

Section: Simulation Resultsmentioning

confidence: 99%

Risk Assessment Edge Contract for Efficient Resource Allocation

Sheng,

Wang,

et al. 2024

Mathematics

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The rapid growth of edge devices and mobile applications has driven the adoption of edge computing to handle computing tasks closer to end-users. However, the heterogeneity of edge devices and their limited computing resources raise challenges in the efficient allocation of computing resources to complete services with different characteristics and preferences. In this paper, we delve into an edge scenario comprising multiple Edge Computing Servers (ECSs), multiple Device-to-Device (D2D) Edge Nodes (ENs), and multiple edge devices. In order to address the resource allocation challenge among ECSs, ENs, and edge devices in high-workload environments, as well as the pricing of edge resources within the resource market framework, we propose a Risk Assessment Contract Algorithm (RACA) based on risk assessment theory. The RACA enables ECSs to assess risks associated with local users by estimating their future revenue potential and updating the contract autonomously at present and in the future. ENs acquire additional resources from ECSs to efficiently complete local users’ tasks. Simultaneously, ENs can also negotiate reasonable resource requests and pricing with ECSs by a Stackelberg game algorithm. Furthermore, we prove the unique existence of Nash equilibrium in the established game, implying that equilibrium solutions can stably converge through computational methods in heterogeneous environments. Finally, through simulation experiments on the dataset, we demonstrate that risk assessment can better enhance the overall profit capability of the system. Moreover, through multiple experiments, we showcase the stability of the contract’s autonomous update capability. The RACA exhibits better utility in terms of system profit capabilities, stability in high-workload environments, and energy consumption. This work provides a more dynamic and effective solution to the resource allocation problem in edge systems under high-workload environments.

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“…• The nonlinear ARMA with generalized autoregressive conditional heteroskedasticity (ARMA-GARCH) model, widely used for time-series QoS prediction, e.g., [53], [54]. • The heavy tailed log-normal distribution, recently claimed to fit traffic among network links [55]. • The 2-state Markov Gilbert Elliot (GE) approach, that has been efficiently applied to describe the PLR measurements and link quality over wireless networks [56].…”

Section: Performance Evaluation Over Synthetic Datamentioning

confidence: 99%

A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

Skaperas,

Mamatas,

Tsaoussidis

2024

Trans. Mach. Learn. Comm. Netw.

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Software-defined wireless mesh networks are being increasingly deployed in diverse settings, such as smart cities and public Wi-Fi access infrastructures. The signal propagation and interference issues that typically characterize these environments can be handled by employing SDN controller mechanisms, effectively monitoring link quality and triggering appropriate mitigation strategies, such as adjusting link and/or routing protocols. In this paper, we propose an unsupervised machine learning (ML) online -for link quality detection consisting of: (i) an improved preprocessing clustering algorithm, based on elastic similarity measures, to efficiently characterize wireless links in terms of reliability, and, (ii) a novel change point (CP) detector for the real-time identification of anomalies in the quality of selected links, which minimizes the overestimation error through the incorporation of a rank-based test and a recursive max-type procedure. In this sense, considering the communication constraints of such environments, our approach minimizes the detection overhead and the inaccurate decisions caused by overestimation. The proposed detector is validated, both on its individual components and as an overall mechanism, against synthetic but also real data traces; the latter being extracted from real wireless mesh network deployments.

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Internet Traffic Volumes are Not Gaussian—They are Log-Normal: An 18-Year Longitudinal Study With Implications for Modelling and Prediction

Cited by 23 publications

References 46 publications

On the Modeling of RTT Time Series for Network Anomaly Detection

On the Modeling of RTT Time Series for Network Anomaly Detection

Risk Assessment Edge Contract for Efficient Resource Allocation

A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

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