Invaluable and Evolving Network Analytics
Network analytics is the process of gathering and analyzing information about what is occurring in the network to guide better decisions on using and maintaining the network. Main items covered by network analytics in a combined packet plus optical network include:
- Packet services performance. Tracks Ethernet Connectivity Fault Management (CFM) for compliance against SLAs, including service downtime, packet delays, and packet loss. Also monitors the utilization of ports, services, links, available bandwidth, and other resources, on an absolute and per-customer basis.
- Optical services performance. Tracks OTN services for downtime and error count against SLAs, abnormal span losses, and out of range transceiver optical signals.
- Services inventory. Provides insights on service types and usage, including L3VPN services, L2 MPLS services and tunnels, and OTN links and trails.
- Physical inventory. Details the network elements, service cards, transceivers, and links that make up the network, and their utilization.
- Health. Provides trends for failures, alarms and error notifications, including tools to analyze intermittent error events.
Besides providing insights on “how the network is doing”, strong network analytics packages also deliver higher level business benefits.
Bigger Business Opportunities. They pinpoint services with the highest customer utilization and/or growth. This can focus marketing efforts to promote these services, or to create service variations. The result is maximizing revenues where customers are hungriest for connectivity and bandwidth.
Rapid Service Launches. Getting new service offerings to market quickly and efficiently is key to turning a profit. However, launching new services is complex. Network operators need a lot of information, such as: Are network resources available? Is there enough capacity? Are technicians needed in the field?
Putting all this information together can take days or even weeks of research. Good network analytics answers these and similar questions quickly, assembling all the information needed to launch new services rapidly.
Enhanced Customer SLAs. Network analytics digs into the details of packet services performance for throughput, bandwidth utilization, latency, delays, dropped packets, and overall availability. This enables crafting, offering, and tracking appropriate SLAs, improving customer satisfaction, and reducing services churn.
SLA Guarantees for Businesses. It is always a good idea to demonstrate in advance to large business customers or sub-carriers (in the case of a carrier-of-carrier model) that the network is meeting SLA guarantees. Network analytics addresses this through customer-specific SLA performance reports generated automatically or on demand. In addition, by linking to CNM systems, customers can even generate their own reports through customer portals.
Better Network Planning. Network analytics identifies patterns of traffic growth and resource utilization, guiding planners on how to shift resources to accommodate service demands and network expansion. It accomplishes this, for instance, through a clear mapping of current and historical bandwidth against network assets.
Touch-of-a-Button Full Network Audits. Full network inventory audits are one of the greatest challenges facing network operators. Every year, it can take weeks to collect, verify, and organize the information into the necessary reports. With proper network analytics, full network inventory audits are easy, and can be completed quickly, in a matter of hours. Typically, these include user-defined reports, charts, and tables summarizing results clearly and alerting for any abnormalities found.
Networks analytics are also evolving, in terms of sophistication and speed.
For example, network analytics are now starting to include simulations capabilities that assist network planning. “What-if” simulations explore what occurs to services under conditions of traffic surges or failures. By playing around with network configurations and restoration schemes, these simulations can assist in making the network more robust to unanticipated events.
Network analytics are also on the cusp of becoming a real-time optimization tool, particularly as telecommunications moves towards its long-term goal of self-organizing networks. We have one ingredient for that today with streaming telemetry that pushes continuously masses of data on the state of the network. The next stage is real-time AI-guided network analytics that makes continuous optimization decisions based on this data, and controls the networks with minimum human intervention.
Bottom line, strong network analytics are essential for a well-run network, today and into the future.
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