The 3 A s: Authentication, Authorization, Accounting in .NET

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The 3 A s: Authentication, Authorization, Accounting
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Figure 1.3 An illustration of accounting management architecture
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the data need to be routed to. If the domain portion is absent or corresponds to the local domain, then the session record is treated as an intra-domain accounting event. Intra-domain accounting events are typically routed to the local billing server, while inter-domain accounting events will be routed to accounting servers operating within other administrative domains.
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1.3.2 Models for Collection of Accounting Data
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Several accounting data collection methods are implemented in the industry. Examples are the polling model and the event-driven model. In the following sections we provide a short description for a few of these models.
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accounting systems, do not perform batching and hence transfer only one accounting event per packet. This model is called even-driven model without batching and is rather inefficient. An event-driven model typically stores accounting events that have not yet been delivered only until a timeout interval expires. Once the timeout interval has expired, the accounting event is lost, even if the device has sufficient buffer space to continue to store it. As a result, the event-driven model has the smallest memory requirement, and is the least reliable, since accounting data loss will occur due to device reboots, sustained packet loss, or network failures of duration greater than the timeout interval. The event-driven model is frequently used in networks with roaming applications, since this model sends data to the recipient domains without requiring them to poll a large number of devices. Since the most basic event-driven model does not support batching, it permits accounting records to be sent with low processing delay, enabling application of fraud-prevention techniques. However, because roaming accounting events are frequently of high value, the poor reliability of this model is an issue. As a result, an event-driven polling model may be more appropriate. In the event-driven model with batching, again, a device will contact the accounting server or manager when it is ready to transfer accounting data. However, the device contacts the server when a batch of a given size has been gathered or when data of a certain type are available or after a minimum time period have elapsed. Since, such systems transfer more than one accounting event per packet, they are more efficient. An event-driven system with batching will store accounting events that have not yet been delivered up to the limits of memory. As a result, accounting data loss will occur due to device reboots, but not due to packet loss or network failures of short duration. Note that while transfer efficiency will increase with batch size, without non-volatile storage the potential data loss from a device reboot will also increase. Through implementation of a scheduling algorithm, event-driven systems with batching can deliver appropriate service to accounting events that require low processing delays. For example, high-value inter-domain accounting events could be sent immediately, thus enabling use of fraud-prevention techniques, while all other events would be batched. As a result, this approach can have good scalability and flexibility characteristics. In the event-driven polling model, an accounting manager will poll the device for accounting data only when it receives an event. Events are typically generated by accounting clients. Examples of events include: when a batch of a given size has been gathered, when data of a certain type are available or lapse of a minimum time period. Without non-volatile storage, an event-driven polling model will lose data due to device reboots, but is resistant to packet loss, or network partitions of short duration due to its buffering nature. Unless a minimum delivery interval is set, event-driven polling systems are not useful in monitoring of device health. The event-driven polling model can be suitable for use in roaming, since it permits accounting data to be sent to the roaming partners with low processing delay. At the same time, non-roaming accounting can be handled via more efficient polling techniques, thereby providing the best of both worlds. Where batching can be implemented, the state required in event-driven polling can be reduced to scale with the number of active devices. Note that processing delay in this approach is higher than in event-driven accounting with batching since at least two round-trips are required to deliver data: one for the event notification, and one for the resulting poll.
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