Service assurance systems are promising everything from real-time service health information, inferred from low-level resource event data, to digital customer service assistants. This white paper assesses the realism and viability of such approaches. We start by defining service assurance itself and by describing the types of service assurance systems available on the market today, in terms of what questions they can answer and what kinds of data they provide. Next, taking a strictly fact-based and neutral, engineering view of the topic, we look into what may be possible with Big Data and AI and what is not realistic – in terms of cost as well as resources. We therefore discuss what questions should be answered by service assurance systems and what kinds of data we can pull from the network as input to provide these answers. We also summarize the roles of Big Data and AI in the context of service assurance. Finally, we introduce the missing piece in service assurance today: active testing and monitoring. Active testing and monitoring addresses the question: “Are we meeting the level of service quality we promised?” We describe how this activity complements the data landscape derived from the service assurance stack with precisely the kind of pertinent and high-quality data that is missing. We close the white paper with a brief look at some of the relevant results from a recent NFV Service Assurance and Analytics research study and survey completed by Heavy Reading in October 2017. The findings of the survey and the industry’s views on what is needed to satisfy the fundamental goals of service assurance are discussed in relation to the research and findings of this white paper.
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