Network operators generally aim at providing a good level of satisfaction to their customers. Diverse application demands require the usage of beyond best-effort resource allocation mechanisms, particularly in resource-constrained environments. Such mechanisms introduce additional complexity in the control plane and need to be configured appropriately. Within 5G mobile networks, two new mechanisms for QoS-aware resource allocation are introduced. While QoS Flows enable specifying various QoS profiles on a per flow granularity, slices are dedicated virtual networks, strongly isolated against each other, with aggregated QoS guarantees. It is, however, unclear how QoS Flows and network slicing can optimally be exploited to ensure a high customer QoE while efficiently utilizing the available network resources. We address this research question and evaluate the outlined interplay using the OMNeT++ simulation environment in a multi-application scenario. We show that resource isolation induced by slicing may negatively affect application quality or system utilization, and that this impact can be overcome by finetuning the system parameters.

Today's networks support a great variety of services with different bandwidth and latency requirements. To maintain high user satisfaction and efficient resource utilization, providers employ traffic shaping. One such mechanism is the Hierarchical Token Bucket (HTB), allowing for two-level flow bitrate guarantees and aggregation. In this demo, we present HTBQueue -our OMNeT++ realization of the HTB, and show how the module can be used for mimicking 5G network slicing and analyzing its effect on network services.

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The support of vital societal functions requires a reliable communication network, especially in the presence of crises and disastrous events. Disasters caused by natural factors including earthquakes, fires, floods or hurricanes can disable network elements such as links and nodes and cause widespread disruption in end users connectivity to network services. Effects of disasters can vary over space and time due to disaster escalation and propagation. Network recovery from disasters requires understanding of both the spatial properties of the hazard at hand, and their temporal evolution. While the former has already been addressed in the literature, existing models and measures are unable to capture the temporal aspects of disaster recovery.This paper proposes a framework for spatial and temporal evaluation of network disaster recovery. It allows for modelling random spatial patterns of disasters in a geographical grid. The temporal aspects captured in our framework include changes due to the progression of a potentially shape-changing disaster across the affected area, as well as to the recovery actions of adaptive network reconfiguration and topology reconstruction undertaken by the network operator. The framework applicability is demonstrated on a content delivery network use case example, where we capture the evolving network performance in terms of the average shortest path length between the peers and the content replicas hosted by servers. By providing insights into the spatial and temporal effects of both disaster escalation and remediation measures, our proposed framework lays down the groundwork for flexible disaster modelling and recovery sequence optimization.