Wholesale Billing is usually concerned about rating calls/events so that revenues and costs can be identified and cash flow secured. As such, billing systems including BriteSoft’s BriteConnect product usually process successful CDRs as unsuccessful do not yield any type of revenue or cost.
However, there is a whole host of calls that don’t actually complete and are considered unsuccessful, even though the network may have seized such calls and occupied capacity.
BriteMonitor deals with all successful and unsuccessful CDRs so that it can derive the efficiency of the network and present the anomalies to the users. The efficiency is in terms of Quality of Service and the anomalies are in relation to quality metrics or KPI’s
set up by the organisation.
KPI’s or Key Performance Indicators are a measure for an organisation to set Quality of Service targets and compare them against actual traffic. Typically, these are set at Call Direction, Carrier and Destination level, however, the system allows users to set up KPI’s at any level including network elements such as route, trunk and switch.
Once these targets are set, the user is presented with traffic that represents a certain period or interval for the same day and time over the previous days or weeks. This data is superimposed on top of the targets so that the user is a give a clear indication as to how the network is performing against those targets/KPI’s.
Another feature of the KPI’s is that once a Quality of Service metric such as ASR, ACD or NER falls below a certain threshold, alerts can be generated and sent to relevant parties. These alerts can have various thresholds for escalation purposes.
There are two way of presenting data to users showing network information. The first is a map representing the ‘current’ situation in the network and the second is a graph representing the ‘trend’ that has been accumulated over days, weeks or months of data.
The ideal way to present the ‘current’ international traffic is on a map which can give an immediate and colour oriented indication as to what destinations are not performing well. Upon recognizing such a destination, the user can further investigate the problem by identifying the data that culminated in the QoS degradation. A typical map would look like the diagram below:
For ‘trend’ analysis (below), data is typically displayed in the form of a list or graph. An initial graph is displayed in the following manner:
This view provides an immediate indication of the traffic trends over a certain period, typically six to eight weeks. A list of destinations/carriers that each bar represents is also displayed and each of those destinations or carriers can then have a further map showing its own KPI performance to see if there is a specific area at fault. Of course there are many more graphs that can be shown to the user at a more granular level.