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On National Teacher Day, meet the 2024-25 Kenan Fellows
An Intelligence-Defined Networking Architecture With Importance-Based Network Resource Control.pdf
1. An Intelligence
Architecture With Importance
Network Resource Control
Abstract
As network complexity increases because of the diversification of applications
and services based on Internet of Autonomous Things (IoAT), it is
humans to design the optimal control rule for software
(SDN) controllers. Intelligence
overcome this limitation through machine learning (ML) algorithms. Since the
existing IDN approaches are mostly designed to optimize only the network
Quality of Services (QoS), including throughput, jitter, and latency, the
controllers do not consider the importance of data in the applications and the
services. This causes the controller to al
crucial data flow, which leads critical problems, such as self
accidents. To prevent this problem, we propose an importance
architecture that enables network controllers to manage network traffic w
an importance levels (ILs) of data flows. First, we devise an importance
estimation scheme to set the IL for the flows. Second, a dynamic resource
allocation model of the controllers is developed by means of deep learning
An Intelligence-Defined Networking
Architecture With Importance-Based
Network Resource Control
As network complexity increases because of the diversification of applications
and services based on Internet of Autonomous Things (IoAT), it is
humans to design the optimal control rule for software-defined networking
(SDN) controllers. Intelligence-defined networking, called IDN, is proposed to
overcome this limitation through machine learning (ML) algorithms. Since the
approaches are mostly designed to optimize only the network
Quality of Services (QoS), including throughput, jitter, and latency, the
controllers do not consider the importance of data in the applications and the
services. This causes the controller to allocate insufficient resources to the
crucial data flow, which leads critical problems, such as self
accidents. To prevent this problem, we propose an importance
architecture that enables network controllers to manage network traffic w
an importance levels (ILs) of data flows. First, we devise an importance
estimation scheme to set the IL for the flows. Second, a dynamic resource
allocation model of the controllers is developed by means of deep learning
Defined Networking
Based
As network complexity increases because of the diversification of applications
and services based on Internet of Autonomous Things (IoAT), it is difficult for
defined networking
defined networking, called IDN, is proposed to
overcome this limitation through machine learning (ML) algorithms. Since the
approaches are mostly designed to optimize only the network
Quality of Services (QoS), including throughput, jitter, and latency, the
controllers do not consider the importance of data in the applications and the
locate insufficient resources to the
crucial data flow, which leads critical problems, such as self-driving car
accidents. To prevent this problem, we propose an importance-based IDN
architecture that enables network controllers to manage network traffic with
an importance levels (ILs) of data flows. First, we devise an importance
estimation scheme to set the IL for the flows. Second, a dynamic resource
allocation model of the controllers is developed by means of deep learning
2. algorithms in order to make the optimal network resource. Additionally, an
online learning mechanism based on weighted auto-labeling is adopted to
continue enhancing the adaptability of the resource allocation model on
runtime as the network conditions change. The evaluation results of the
proposed architecture under various autonomous things scenarios show that
the loss rate for data flows of higher importance is reduced by one-quarter
compared to the case of a network controller without IL and that the
bandwidth waste ratio is reduced by 10% compared to the rule-based model.
