Content for  TS 28.104  Word version:  18.3.0

This MDA capability is for the service experience analysis.

Service experience of end user is key indicator that directly reflects the user satisfaction degree. In 5G system, the diversity of network services is expanding all the time and the requirements of different services especially from vertical users are being standardized. Considering these diverse requirements and expectation from end user perspective (e.g. priorities of SLA related attributes such as latency, throughput, maximum number of users or different required values of these attributes), the service experience as a comprehensive indicator need to be extensively analysed.

This MDA capability is for the network slice throughput analysis.

Throughput is of great importance which represents the end users' experiences and also reflects the network problems, e.g. low UE throughput may be caused by resource shortage. In order to satisfy the requirements of dL/ulThptPerSlice in the ServiceProfile, MDAS may be utilized for throughput related analysis/predictions for network slice instance. MDAS producer allows the consumer to request analytics of network slice throughput related issues and identify the corresponding root cause(s) to assist throughput assurance. Network slice throughput analysis can be for a specific domain and/or for cross-domain. Domain-specific MDAS producer analyses the network slice subnet throughput, while the cross-domain MDAS producer analyses the network slice throughput. The two level MDAS producers, i.e. domain-specific and cross-domain may work in coordination to assure the optimum throughput performance.

This MDA capability is for the prediction of network slice traffic patterns.

It is desirable to use MDAS to get the network slice traffic predictions including individual traffic load predictions on each of the constituent network function instance present in the network slice. The traffic load predictions per constituent network function instances can be used for better resource provisioning of the network slice. For example, resources can be pre-configured considering the predicted traffic on the network slice.

This MDA capability is for E2E latency related issue analysis.

E2E latency is an important parameter for URLLC services. User data packets should be successfully delivered within certain time constraints to satisfy the end users requirements. Latency could be impacted by the network capability and network configurations. These factors may be the root cause if the latency requirements cannot be achieved. Packet transmission latency may dynamically change if these factors change. The latency requirement should be assured even if some of the network conditions may degrade. It is important for the MDAS producer to analyze the latency related issues to support SLS assurance.

This MDA capability is for network slice load analysis.

Network slice load may vary during different time periods. Therefore, network resources allocated initially could not always satisfy the traffic requirements, for example, the network slice may be overloaded or underutilized. Overload of signalling in control plane and/or user data congestion in user plane will lead to underperforming network. Besides, allocating excessive resources for network slice with light load will decrease resource efficiency. The analysis of network slice load should consider the load of services with different characteristics (e.g. QoS information, service priority), load distribution to derive the corresponding resource requirements. Load distribution analytic result may be provided, e.g. load distribution for network slices, different locations and/or time periods etc. Traffics and resources related performance measurements and UE measurements can be utilized by MDAS producer to identify degradation of the performance measurements and KPI documented in an SLS due to load issues, e.g. radio resource utilization. MDAS producer may further provide recommendations to the network slice load issue. This analytics results can be considered as an input to support SLA assurance to perform further evaluation.