arXiv:2410.19248v2 Announce Type: replace Abstract: Quality of Service (QoS) is an important metric to measure the performance of network services. Nowadays, it is widely used in mobile edge environments to evaluate the quality of service when mobile devices request services from edge servers. QoS usually involves multiple dimensions, such as bandwidth, latency, jitter, and data packet loss rate. However, most existing QoS datasets, such as the common WS-Dream dataset, focus mainly on static QoS metrics of network services and ignore dynamic attributes such as time and geographic location. Thi
The proliferation of mobile edge computing and AI applications necessitates more sophisticated datasets to optimize network performance and user experience in dynamic environments.
A more comprehensive QoS dataset, including dynamic attributes like time and location, is crucial for developing robust AI-driven service management in mobile edge networks, impacting critical applications and user satisfaction.
The availability of CHESTNUT allows for the development of AI models that can adapt to real-time network conditions, moving beyond static performance metrics to enable more intelligent and responsive edge services.
- · Mobile edge computing providers
- · AI/ML developers for network optimization
- · Telecommunication companies
- · Users of edge services
- · Providers relying on static QoS metrics
- · Legacy network management systems
Improved efficiency and reliability of mobile edge services, supporting more complex distributed AI applications.
Reduced operational costs for edge infrastructure as resource allocation becomes more intelligent and data-driven.
Acceleration of new business models and applications that depend on highly responsive and location-aware edge computing infrastructure through better AI agent orchestration.
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