What are the current trends and best practices in QoS research and development?

1 QoS models and architectures

One of the main challenges in QoS research and development is designing and implementing models and architectures that can support diverse, dynamic network requirements and conditions. These models define parameters, metrics, and classes of service to specify and measure QoS levels, while architectures define protocols, mechanisms, and functions to implement QoS management and control. Current trends in QoS models and architectures involve developing cross-layer and cross-domain models that integrate and coordinate QoS information across different network layers and domains, such as application, transport, network, and link. Adaptive and context-aware models can adjust QoS parameters according to changing network state, user preferences, or application requirements. Additionally, modular and extensible models can support new QoS features like security, mobility, or multicasting.

2 QoS mechanisms and algorithms

A key challenge in QoS research and development is to design and implement QoS mechanisms and algorithms that can efficiently and effectively manage network resources, such as bandwidth, buffer, and CPU. These mechanisms and algorithms are responsible for performing QoS functions, including scheduling, queuing, routing, admission control, congestion control, and traffic shaping. Current trends in QoS mechanisms and algorithms focus on developing intelligent and learning-based systems that leverage artificial intelligence, machine learning, and data analytics techniques to improve decision making and optimization. Additionally, distributed and cooperative QoS mechanisms are being developed to enable network nodes to collaborate autonomously. Lastly, robust and resilient QoS mechanisms are being developed to ensure reliability and availability in the face of network uncertainties, failures, and attacks.

3 QoS evaluation and validation

A third key challenge in QoS research and development is to evaluate and validate the performance, effectiveness, and impact of QoS models, architectures, mechanisms, and algorithms. This requires measuring, analyzing, and comparing QoS metrics and outcomes under different network scenarios and conditions. To do this, various methods and tools such as simulation, emulation, experimentation, and testing can be used. Trends in QoS evaluation and validation include developing realistic scenarios that capture the complexity of network environments and applications, comprehensive metrics that reflect the multidimensional aspects of QoS performance, and scalable interoperable methods that support large-scale cross-domain analysis.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?