By their nature, networks are graphs.
Today, graph databases are being successfully employed in the areas of telecommunications, network management, impact analysis, cloud platform management and data center and IT asset management.
In all of these domains, graph databases are used to store configuration information which is used in real time to alert operators to potential design flaws and shared failure modes in the infrastructure and to reduce problem analysis and resolution times from hours to seconds.
In this “Graph Databases in the Enterprise” series, we’ll explore the most impactful and profitable use cases of graph database technologies at the world’s leading organizations. In past weeks, we’ve examined fraud detection, real-time recommendation engines and master data management.
This week, we’ll take a closer look at network and IT operations.
The Key Challenges in Network & IT Operations:
Network analysts and data center professionals face greater challenges than ever before as the volume of data and size of networks continues to grow. Here are just a few of their most difficult challenges:
- Troubleshooting a network Whether you’re managing a major network change, bolstering your security-related access or optimizing an application infrastructure – the physical and human interdependencies are extremely complex making it difficult to troubleshoot.
- Impact analysis Relationships among the various nodes in your network are neither purely linear nor hierarchical, making it difficult to determine the interdependencies of network elements on each other. In addition, when two or more systems are brought together, these relationships become even more complex.
- Growing physical and virtual nodes With rapid growth in network sizes and a rapid increase in elements to support new network services and devices, your IT organization must develop systems that accommodate both current and future requirements.
Why Use a Graph Database for Network and IT Operations?
A graph database is used to bring together information from disparate inventory systems, providing a single view of the network and its consumers – from the smallest network element all the way to the applications, services and customers who use them.
A graph representation of a network enables IT managers to catalog assets, visualize their deployment and identify the dependencies between the two. The graph’s connected structure enables network managers to conduct sophisticated impact analyses, answering questions like:
- Which parts of the network – which applications, services, virtual machines, physical machines, data centers, routers, switches and fiber – do particular customers depend on? (Top-down analysis)
- Conversely, which applications and services, and ultimately, customers in the network will be affected if a particular network element – such as a router or switch – fails? (Bottom-up analysis)
- Is there redundancy throughout the network for the most important customers?
A graph database representation of the network can also be used to enrich operational intelligence based on event correlations. Whenever an event correlation engine (such as a Complex Event Processor) infers a complex event from a stream of low-level network events, it assesses the impact of that event against the graph model and triggers any necessary compensating or mitigating actions.
Example: A Large European Telecom Provider
To showcase the use of a graph database in the IT and network operations sector, here is an excerpt from an interview with a software consultant who helped implement a graph database solution for one of Europe’s largest telecommunication providers.
Q: Can you tell me a little more about the requirements of the deployment you did for a large telecommunications provider?
“This telecom provider had a very large complex network with many silos and processes – including network management information spread across more than thirty systems. The large number of data sources was in part due to network complexity, and in part due to different business units, as well as organic growth through mergers and acquisitions. These different sources also created a very non-linear fabric that had to be modeled and understood from various dimensions.
“Prior to using a graph database, they had different network layers stored in different systems – for instance, one system might be dedicated to cell towers, another to fiber cables and another devoted to information about consumers or enterprise customers.
“One of their business challenges was around maintenance and ensuring redundancy – they needed to know if they took a device down for maintenance, exactly who might be impacted and what the penalties might be, as well as what alternate routes might better mitigate the impact.”
Q: How did the graph database deployment progress?
“[Implementing a graph database solution] was almost a dream business case because you could measure the benefit of the project as the telecommunications provider began to manage production-level changes that impacted its many actual customers.”
Q: What were some of the other benefits that the Neo4j deployment achieved there?
“After implementation of the graph database model and the impact analysis queries, it was easy to extend the application to support single point of failure (SPOF) detection thanks to the flexibility of the graph model. Also, by providing an effectively unified cross-domain view, experts from different silos could work together for the first time and agree on a common domain terminology.”
Discovering, capturing and making sense of complex interdependencies is central to effectively running your network and IT operations, which in turn are a critical part of running an enterprise.
Whether you are optimizing a network or application infrastructure or providing more efficient security-related access – these problems involve a complex set of physical and human interdependencies that are a challenge to manage.
Graph databases are designed to store that interconnected data, making it easy to translate network and IT data into actionable insights.
Download your copy of this white paper, The Top 5 Use Cases of Graph Databases, and discover how to tap into the power of connected data at your enterprise.
Catch up with the rest of the “Graph Databases in the Enterprise” series:
About the Author
Jim Webber & Ian Robinson, Chief Scientist & Senior Engineer
Jim Webber is Chief Scientist at Neo Technology working on next-generation solutions for massively scaling graph data. Prior to joining Neo Technology, Jim was a Professional Services Director with ThoughtWorks where he worked on large-scale computing systems in finance and telecoms. Jim has a Ph.D. in Computing Science from the Newcastle University, UK.
Ian Robinson is an Senior Engineer at Neo Technology. He is a co-author of ‘REST in Practice’ (O’Reilly) and a contributor to the forthcoming books ‘REST: From Research to Practice’ (Springer) and ‘Service Design Patterns’ (Addison-Wesley). He presents at conferences worldwide on the big Web graph of REST, and the awesome graph capabilities of Neo4j.