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In part one of this article, we learned about real-time big data analytics, what it is and what it means in the telecom environment. Here we will explore the tactical implications of RTBDA.
Applying RTBDA in telecom
For true real-time decision-making, it is necessary to continuously collect, store and analyze network information. In order to fully understand what is happening, all the pertinent Ethernet frames and IP packets must to be surveyed in real time. By capturing and storing network data in this way, we can provide a source of reliable information on data analytic activities.
The real-time data collection layer can provide a constant influx of actionable data for decision-making. Both the TM Forum and the IP Network Monitoring for Quality of Service Intelligent Support project, part of the European CELTIC-Plus program, have studied this need as part of their individual work on customer experience management. The two projects arrived at the conclusion that probes and appliances are critical to providing dependable, real-time insight into what is happening in the network.
Probes are typically data collectors that deliver information to other management systems. On the other hand, appliances use the same technology but additionally analyze and store the data locally. Appliances are typically focused on a specific task, such as performance monitoring, test and measurement, or security and are often perceived as fulfilling that very specific role. Probes and appliances can also be used more strategically as sources of real-time data for big data analytics and as implementations of RTBDA strategies. The following outlines a three-step outlook of how such an infrastructure could be executed.
Implementation
The initial step is the implementation of appliances for data collection. The main constraint here is that all the Ethernet frames and IP packets need to be captured, in real time, at line speed with zero packet loss no matter the conditions. This visibility guarantees that a dependable flow of data is being collected.
It is essential that each and every frame be given a unique time stamp, so that a precise timeline can be established not only native to the appliance but also across multiple appliances. The precision and accuracy of these time stamps must be in the range of nanoseconds. For example, with only 67 nanoseconds between Ethernet frames in a 10-gigabit-per-second network, the time stamp resolution must be better than 67 nanoseconds. If this is not executed properly, two Ethernet frames would receive the same time stamp, making it difficult to differentiate which came first.
The combination of zero packet loss capture with nanosecond precision time stamping ensures that we have a reliable, accurate stream of data analysis information.
Storage
The next step is storing this data in real time. Many appliances deliver capture to disk, which allows real-time data to be placed directly to a local hard disk on the appliance. Otherwise, this data can be redirected to a storage area network or alternative location. This data can be utilized to establish a historical timeline of what has happened in the network with specific details, thereby making it possible to recreate precisely what happened, as it happened.
This history is a source of rich information for data analytics. This data can offer insight into usage and behavior trends. If the appliance has deep-packet inspection capabilities, then usage of services, including over-the-top services, can be tracked and analyzed to detect patterns in usage with respect to time, location and type of device.
Standing alone, this information is a valuable resource for network and service optimization. New, attractive services can be created to match users’ preferences. However, even more significantly, OTT content service providers can use this information as a source insight into their customers’ preferences, allowing carriers to offer compelling services to potential customers.
Decisions in real time
Finally, there is the potential to use real-time and stored data to enable real-time decision-making. A profile of expected behavior can be developed using the historical information captured to the disk. When data is juxtaposed with the real-time information on network activity, it is possible to identify unexpected events or anomalies. These can be a security threat, performance degradation or an opportunity to offer a customer a package extension or a complementary service.
From a RTBDA perspective, this capacity is similar to the types of capabilities that OTT content and service providers have employed. The ability to react in real time, based on an understanding of what is currently happening and relating it to what has occurred in the past.
The future of RTBDA in telecom
Modern telecom networks need to re-think the meaning of “real-time” in their environments. It is also time to reevaluate what sources are used for big data analytics. Telecom carriers should consider the use of probe and appliance technology already in the network in a more strategic way to provision RTBDA. This will not only act as a better source of information for planning decisions, but they will also generate new opportunities to offer enhanced services, not only to end users, but also to OTT service providers. Ultimately this could address the issue of monetizing OTT traffic in telecom networks.
