Network Analytics

Building a Healthy Service Network

By Incognito on September, 23 2014

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You can gather this information using IPDR data, however, normalizing, summarizing, and aggregating the data is a complex task. With accurate metrics on historical congestion and capacity issues, you’ll be able to start creating a plan for future growth. But knowing what percentage of your macro service network is currently being impacted by service congestion is only a start. There is much more to consider when implementing a solution to build your healthy service network:

Multi-tiered Views Once regional CMTS allocations are defined, your measurement solution should have the ability to view any number of congestion-impacted regions, CMTSs per region, and cable modems (CM) per CMTS interfaces. Having the ability to drill down throughout all levels of your service network gives you the most accurate consumption pattern information. By knowing individual subscriber congestion problems and large-area capacity issues, you can make better decisions about where a service level agreement (SLA) upgrade or new hardware installation is needed.

Geographical Views – By grouping CMTSs in a geographical region, you can gain a wider view of your service network, and determine if commercial or residential development is causing potential congestion problems. You can also see exactly where individual subscriber devices are on a map, which will help you understand subscriber growth, plan, and type patterns per region.

Comprehensive Data – Even the smallest details become very important when attempting to accurately measure and forecast a service network. In addition to having visibility about what congestion and capacity issues exist, you should be able to pull related information, like congestion averages against SLAs, and whether the congestion is affecting a business or residential subscriber.

Normalization of Data: When collecting data via an IPDR-based solution, the network is traversed freely, and huge packets of data are returned. Normalization of all this data is crucial to making information relevant. You need a system that translates large amounts of information, cleans up any irrelevant or bad data, and displays the information on an intuitive interface so that users can more easily monitor and manage congestion and capacity issues.

Analytics and Predictions: Trending analysis makes informed network capacity planning possible. Knowing the total number, length, and average time of congestion periods will help you discern what may have created a capacity issue. By determining subscriber bandwidth utilization patterns, you can see where consistent problems are, and create priority lists to make sure you’re spending money efficiently. Your system should also have the ability to use this analytical data, combined with historical subscriber growth trends, to make predictions for future macro, regional, CMTS, and interface requirements.

Automatic Notifications: Because service networks are expanding so rapidly, it’s difficult to keep an eye on the whole picture. You will benefit from using a solution that sends automatic notifications when a CMTS interface is reaching its capacity.

Enforcement Policies: There are two sides to managing network congestion. While utilization measurement and predictive analytics allow providers to take a more proactive approach, it does not change consumer behaviour patterns. A measurement tool should work in collaboration with a policy enforcement tool, which can be used to enforce bandwidth utilization policies to mitigate congestion issues, allowing operators to determine if their current policies are proactively solving congestion and capacity problems.

Optimizing your network with a congestion management and capacity planning tool will ensure that growing bandwidth requirements are adequately provisioned to your subscribers. By taking the guesswork out of identifying problems, and having normalized information readily available, you’ll be able to quickly resolve congestion and capacity issues with effective policies or redistributions of bandwidth. By ensuring that your solution has the ability to collect and store information about bandwidth utilization and capacity trends, you’ll be able to leverage data, and build a healthy service network.

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