End-to-End Latency Bottleneck Analysis for Multi-class Traffic in Data Center Networks

He, Yonghong; Sun, Yinbo; Yang, Yongdan; Li, Hanruo; Wu, Xiaoqing

doi:10.1109/isdea.2015.98

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…They focus on the best routing path (addressed in our previous research using JosNet [3] while our research takes a different approach by using live data packets to make predictions. Other research work has explored the use of different prediction approaches and algorithms to improve QoS, for example, using the Graph Neural Network (GNN) model to estimate key performance indicators in a network [26,27], combining analytical and simulated estimation for load distribution [28], analyses of bottleneck prediction, and management for multi-class traffic in Data Centre Networks [29].…”

Section: Existing Network Prediction Modelsmentioning

confidence: 99%

Multi-Network Latency Prediction for IoT and WSNs

Balota,

Kor,

Shobande

2023

Computers

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The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.

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