Network monitoring tools for when cloud monitoring falls short

Cloud monitoring can collect a range of performance data, but a crucial piece of the puzzle, networking, often falls short. A third-party network monitoring tool is essential to keep your cloud running up to par.

Cloud monitoring services collect a range of performance data on servers, storage and other services within a provider's ecosystem. Unfortunately, network monitoring features in these tools are often limited, which means they can miss out on major performance issues in your cloud environment. To keep a close watch on your network and catch potential problems, use an established network monitoring tool to track and report more in-depth...

on performance.

Network monitoring tools fall into three categories: basic monitoring, visualization and reporting, and advanced anomaly detection. Advanced products have features that hit upon more than one category. But, if you're concerned about visualization or advanced anomaly detection and security, look for those features specifically in a single product.

Basic network monitoring tools to get the job done

Nagios is an open-source network monitoring tool with a reputation for being difficult to use but comprehensive. It captures network protocols, monitors network services and generates alerts via SMS, email or custom script execution. A reporting module provides documentation of past outages and responses to those outages.

For those who want the monitoring capabilities of Nagios without the administrative learning curve, third-party utilities based on Nagios may be the right option. With services like Opsview Ltd. offers, you get advanced features, service support and several different plans -- from a free, open-source core option to enterprise-level options. The latter includes unified dashboards, enterprise reports and data aggregation from multiple distributed master servers.

Another open-source option for IT infrastructure, including network monitoring, is from Zabbix ISA. With Zabbix monitoring tools, administrators can collect detailed metrics on servers and network devices. It provides visualizations to render large amounts of data in easy-to-evaluate formats. Proxies enable distributed monitoring, and both agent-based and agentless monitoring options are available.

Visualization tools for digesting large amounts of data

One of the challenges with a network monitoring service is it can generate large volumes of data. Reporting tools can filter and aggregate data. Visualizations can consolidate large volumes of data and render it in ways that display salient relationships.

For visualizations, cloud admins may want to use an open-source network monitoring tool such as PRTG, which is built on RRDtool, used for data logging and graphing applications. Commercial support for PRTG is available from Paessler. Cacti, another visualization tool based on RRDtool, is available from AWS Marketplace Partner JumpBox. Cacti includes an interface with support for graph templating and multiple data acquisition methods.

Anomaly detection for more advanced network monitoring

Visualization techniques alone, however, are not always enough. Some patterns in network traffic are more subtle. For example, variations in average traffic patterns could indicate a problem, but may not appear in typical management reports and visualizations. This requires an anomaly detection technique. For more advanced pre-emptive monitoring, anomaly detection applications can identify network behavior outside of expected ranges.

Anomaly detection is important for services that require consistent application and network performance. A successful marketing campaign can lead to a traffic spike for a website. Even with load balancing in place, there is the risk for increased latency in customer traffic. Tools such as Grok from Numenta can identify spikes and other anomalies.

Ideally, if auto-scaling were enabled, your application would automatically add nodes to your cluster to handle the increased load. Tools such as Grok identify potential performance problems, enabling system administrators to manually add additional servers or ensure that auto-scaling is responding to the increased workload.

About the author:
Dan Sullivan holds a Master of Science degree and is an author, systems architect and consultant with more than 20 years of IT experience. He has had engagements in advanced analytics, systems architecture, database design, enterprise security and business intelligence. He has worked in a broad range of industries, including financial services, manufacturing, pharmaceuticals, software development, government, retail and education. Dan has written extensively about topics that range from data warehousing, cloud computing and advanced analytics to security management, collaboration and text mining.

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This was first published in July 2014

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