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Eden Shochat of Aternity on EUEM in the context of BSM
As a follow up to my recent post titled “End User Experience Monitoring as lynchpin for BSM,” I spoke with Eden Shochat, CTO of Aternity, to learn more about their offerings in this space and discuss unique contributions they could make as part of a larger BSM strategy. For those who aren’t familiar with them, Aternity provides a new class of Application Performance Management software via its Frontline Performance Intelligence Platform.
An excerpt of our very interesting discussion is presented below: Abbas: I’ve described the role that EUEM solutions like Aternity’s Frontline Performance Intelligence play in the context of BSM, but what is unique about your technology in this area? Eden: Aternity’s Platform fuses application, desktop and user performance monitoring with real-time business intelligence. This approach generates the most accurate and comprehensive user experience information from multiple levels of the network and application stack on the end-user machines (physical or virtual). By combining this unique data with correlation, clustering and anomaly detection analysis algorithms, Aternity’s Platform is able to perform preemptive problem detection, usage & usability analysis, fact-based capacity planning and activity-oriented compliance. Abbas: What is the typical deployment architecture for one of your Global 2000 customers? Does the solution require agents, appliances, or a combination?
Eden: There is basically a four-tiered architecture approach we follow during the course of our enterprise deployments:
> The Microsoft Certified Agent(s) collects information and measures the performance of the desktop, applications and user productivity
> Next, Aggregation services communicate with the Agent(s) to aggregate the measurements and further compress the traffic
> Then, Analytics Services perform analysis on any incoming data, such as activity usage metrics, and clusters similar data together, performing anomaly detection and correlating endpoints with similar characteristics to locate probable cause
> Finally, the architecture is supported by a Management Console and a historical database store for enabling the management, configuration and interactive drill-down into specific user experience business intelligence data.
The aggregation, analytics and management tiers can all run on the same server or they can be split into multiple servers, scaling horizontally or vertically to support tens of thousands of monitored users.
Unlike appliances which are located within the data center, the Microsoft Certified Agent(s) resides on the end-point where the service is consumed, providing for an in-depth level of accuracy that is impossible to achieve with sniffer based technologies. Additionally, Agent distribution is performed as a software update versus having to distribute multiple hardware appliances.
And, by having aggregation separated, the architecture can more easily support distributed models. These could for example include those found in the oil and gas industry, where some of the services that are consumed there are behind very high cost vsat links. Bank branches are another good example, where some of the network and application services are local and don’t go to through a general corporate network.
Abbas: When monitoring performance of applications, do you treat them all the same (i.e. agnostic) or are their specialized analyses for common applications such as Exchange, SAP, and Web?
Eden: The Aternity Agent performs both protocol agnostic monitoring, supporting virtually all applications as well as technology specific monitoring. This includes:
> Generic network Cartridge, supporting any request à response type protocol, e.g: Java RMI, CIFS, 3270 or other, unpublished protocols.
> HTTP/s Cartridge supporting any HTTP-based application, web or otherwise, without requiring the secure breaches by appliance-type key management
> Win32 Client/Server Cartridge: Passive monitoring of any win32 user interface application, be it .NET forms, Powerbuilder or plain vanilla win32 programming.
> Oracle EBusiness Cartridge: Generic monitoring of JInitiator and JDK based form applications, including customizations performed by customers.
> Java: Monitor any Swing-based (AWT is supported by the Win32 Cartridge) applications and applets
> Server-based Computing ICA/RDP Support: We monitor both the latency of the screen refreshes as well as the actual applications on the Citrix/Terminal Server for published desktops and applications.
The support for technology-based instrumentation means that most (if not all) of the applications, shrink wrapped or custom, can be monitored by the Aternity Platform.
In addition, the Agent collects environmental information, e.g: network statistics, process information (including crashed and hung processes, user activity) and operating system, service packs, installed applications & patches. The agent is a Windows service monitoring the endpoint providing insight into the network, desktop, server-based computing protocols.
This monitoring can be applied to standard desktop/laptop clients, server-based computing environments like Citrix XenApp and virtual desktop infrastructure (VDI) deployments, all this with incredibly minimal footprint of less than 10MB of physical RAM and under 0.1 percent CPU on average.
Abbas: Analytics seems to play a prominent role in how Aternity positions itself. Can you go into more detail about how it works and the value that provides to your customers?
Eden: Attempting to understand or derive business intelligence from volumes of end user performance metrics is like looking for the proverbial, “needle in the haystack”. Sophisticated, real-time analytics are therefore necessary to truly bring about what we call, Frontline Performance Intelligence.
The issue that plagued the early attempts of monitoring user experience is having the capability to transform huge volumes of data into actionable intelligence. Previously, organizations would try to lessen the flow of data from end users’ desktops by only supporting partial deployments of Application Performance Management (APM) technologies. These deployments would be applied to PCs that were exhibiting performance issues. This mode of operation prohibits on-going introspection of user productivity and experience.
By collecting a comprehensive set of end user performance and productivity metrics at the Frontline, and processing this data with analytics, the Aternity Platform generates Frontline Performance Intelligence from real frontline performance metrics. The analytical components in the Aternity algorithm engine include:
Autonomic Performance Profiling: An Autonomic Performance Profile™ is the mathematical model used for automatic, real-time identification of groups of homogenous users sharing the same behavior at a particular time, and is used to quantify, detect and distinguish between normal and abnormal behavior.
Deviation detection: Autonomic Performance Profiles were designed to provide the earliest possible detection of performance problems that impact multiple users while simultaneously eliminating the need for manual alert configuration and tuning, which many other products in the market require. The Analytic Engine performs continuous correlation between the real-time performance measurements captured by the Agents, and the Baselines of the Autonomic Performance Profiles. In this way, performance deviations of any magnitude can be automatically detected, for groups of users of any size, with no manual configuration and/or intervention.
Problem Minimization: Each of the detected symptoms is analyzed for commonalities to tie multiple symptoms together into a problem. This has been shown to greatly reduce the number of alerts going to the IT operations.
Problem Isolation through Endpoint Classification: End users with like symptoms are first grouped together into an “Effect group”, and an alert is raised. The analytic engine then automatically identifies the end users’ unique commonalities, with two levels of correlation, across the effect group:
1. Positive Correlation: the attributes that the affected group have in common
2. Negative Correlation: the attributes common to the effect group that are also common to the non-affected user groups
The intersection of these two correlations, i.e. the Query Group and the Effect Group is shown as the “Match Group” above. The attributes that produce the strongest Match Group are “surfaced” as a Probable Cause. Any attributes collected by the Aternity Agent (e.g.: the amount of memory, installed application or the subnet where the endpoint resides) may be used for Dynamic Problem Isolation, i.e. Probable Cause Analysis.
Abbas: Given that FPI may well be the early warning system that companies would rely on to get ahead of end user performance issues, what mechanisms do you provide by which another management platform can gain access to the results of the analytics so that they can be presented inside of a BSM view?
Eden: When we designed the Aternity Platform, it was clear that we are generating a new type of a data stream - user experience combined with activity data. As such, we architected the system to be totally open. The system components communicate over a message bus among themselves. And, the complete database schema is open, documented and simple for custom-built reports. The problem detection analytics are exposed through our object-oriented Problem Life Cycle Manager and CLI layers.
Some of the existing integrations at customers include to Ticketing Systems (CA, BMC), Portals (IBM WebSphere, Microsoft Sharepoint), SNMP alert systems (HPOV) and other proprietary systems.
Contact information for Aternity is available here.
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