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AppNeta introduces PathView Cloud

AppNeta delivers a new variation on one of the traditional application performance management themes. This variation is based upon the use of microAppliances and a cloud service.
Written by Dan Kusnetzky, Contributor on

Jim Melvin, CEO of AppNeta dropped by to introduce his company; some new features of the company's end user and application performance management tool, PathView Cloud; and describe at some depth, FlowView Intelligent Application Visibility (IAV).

PathView Cloud and PathView microAppliances

PathView is a cleverly designed end user, network and application performance management cloud service that offers organizations the ability to monitor what their staff, partners and customers are experiencing. It offers detailed information about performance segmented by a number of very useful categories.

This service relies on the installation of a "microAppliance," an appliance server, somewhere in each of the targeted network segments. Once this device is installed in each segment, it starts learning about the applications, systems and workload patterns. That data is sent to AppNeta's PathView service offering.

Organizations then have the ability to examine what workloads are running, what network resources they're using and know down to what applications each person is using what is happening. It would be relatively straightforward to quickly find and resolve many performance issues.

Snapshot analysis

In the past, I've been presented three different approaches to network and performance management. AppNeta appears to have come up with a new variation of the third approach. Each of these approaches has its strengths and challenges. Here are the typical approaches to management:

Building management in

Instrument everything so that internal operational data is available when management tools ask. While this approach offers the most fine grain collection and presentation of data, this approach can impose a great deal of management overhead and slow down production systems.

It also may not effectively deal with multi-tier distributed applications or applications that operate in a cloud computing environment. Furthermore, developers and technology suppliers often have chosen not to collect this data.

It is wise, however, for an application performance management tool to know what data is available and use it.

Agents everywhere

Install agents to collect data on every single component and have those agents send data to a central collection point for later analysis is another approach that is highly touted.

The truth is that installing agents on every client (PC, Laptop, Smartphone, Tablet), server (physical, virtual or cloud-based), network device, storage device, database engine, application framework and application component isn't really practical. Persuading staff and customers to install agent software on their client devices is quite problematic. Installing them without consent on customer systems could get the organization into a great deal of hot water.

Furthermore, agents can only see local operational data. Unless the supplier has developed techniques to capture data from networking and storage systems, it is likely that critical pieces of the performance puzzle will not be discovered.

As before, it is wise, however, for application performance management tools to be aware of these agents and use the data they've collected if it is available.

Follow the network traffic

Several suppliers believe that the best approach is the follow the network traffic and learn who is talking to who, what is talking to what, where components are located, how those components are moving throughout the network and then apply sophisticated analysis to that traffic data. Typically this approach revolves around the installation of appliance servers into the network at key points to gather data from the flow of network communications. This approach inserts only a tiny amount of overhead into production systems. Network packets would only have to experience a single additional network hop and a wealth of operational information can be collected.

If the performance management tool was well designed and had built in knowledge of all of the popular operating systems, virtualization tools, database engines, application frameworks, networking and storage components and commercial application components, there would be no need for customers to configure the system. If it were plugged into the network and given a chance to watch the network traffic, it would learn what was needed. AppNeta's approach is to build this intelligence into a Cloud service rather than trying to overload either the appliance servers or the application hosts in the network.

Another great point is that applications or their components could be running on mainframes, midrange systems, industry standard systems, PCs, Macs, Smartphones, Tablets or even instrumented coffee makers, operational data would be collected.

One key challenge to this approach is developing a system that can scan through network traffic fast enough to deal with today's high volume, low latency networking environments. Another challenge is learning enough about every popular application, framework, networking component, etc. to build a complete picture of what's happening.

AppNeta's approach appears similar to that being offered by ExtraHop. The key differences are how and where the data is collected and where it is analyzed. AppNeta relies on microAppliances that are installed in all sub networks and the analysis is conducted in AppNeta's cloud service.  AppNeta's approach means that new product features and new products can be added without changing anything in the customer's environment. Very clever.

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