The SIOS iQ Performance Meta Analysis feature adds Deep Learning to strengthen its Performance Root Cause Analysis. Deep Learning is a Machine Learning approach that helped AlphaGo master the game of Go and Deep Blue to master Chess. Now, the incarnation of Deep Learning in SIOS iQ will help to identify the root causes of the performance problems across very large dataset (behaviors, topologies, anomalies and patterns over time) events in very dynamic virtualization and cloud environments. Meta Analysis drastically reduces problem identification to a very small number of recurring anomalous behavior patterns and their root cause(s). IT admins can now manage even the largest and “noisiest” environments and can gain insights instantaneously, eliminating hours or days of trial-and-error guesswork when trying to understand and mitigate a problem affecting infrastructure operations and application service delivery.

Problem vs Issue

  • An Issue is an incident identified by the Performance Root Cause Analysis feature, powered by patented Topological Behavior Analysis (TBA), that takes place at a particular time in the environment.
  • A Problem is a holistic view of the performance issues (incidents) over time that better reveals their root cause and recommendations to address them.

Following these definitions, Performance Root Cause Analysis performs identification of the Performance issue (incident), while Performance Meta Analysis provides identification and root cause analysis of the related Performance Problems along with resolution(s) for them.

How does Performance Meta Analysis work?

As the issues (incidents) are identified in the environment by the Performance Analysis feature, they are gathered and analyzed by the Meta Analysis feature across behaviors, topologies, anomalies, and patterns over time. As a result of Meta Analysis the provided root cause and recommendations are no longer based on individual issues (incidents) but on a problem overall that repeats itself in the environment across the topologies of the objects. Currently Meta Analysis is performed based on the centers of contention (i.e. hosts and datastores).

How to use Performance Meta Analysis


Performance Meta Analysis workflow is very simple. To access Meta Analysis features, the user may select Meta Analysis on the side navigation bar under the Performance category.

Once there, the user will see the already familiar visualization of the Topology Pie for the Performance Meta Analysis Dashboard Figure 1 (below).

Figure 1

The Performance Meta Analysis Topology Pie provides information on the number of problems (1) for the environment(s) (2) as well as their criticality (3) (Red – Critical, Yellow – Warning, and Blue – Informational). In addition, the user can slice the Topology Pie information by Environment (4) or time frame (6), as well as observe the ongoing or historical view of the problems (5).

To gain access into the details of the Meta Analysis (Figure 2) the user can select one the of the Topology slices ((3) in Figure 1).

Figure 2

Meta Analysis breaks down the problem into two sections; a visual representation on the left and a detailed description along with the root cause and recommendation(s) on the right.
Let’s take a look at each section individually. On the left the Meta Analysis graph displays the problem by breaking down the objects by Root Cause (1), Impacted (2), and Associated (3) (Figure 3).

Figure 3

In addition, Meta Analysis presents the type of problem (4) as well as a button to close the Meta Analysis event once it has been resolved by the user (Set User Resolved) (5) analyzed over the selected time frame (6) (top right corner of the Dashboard). While there could be a number of Root Cause objects (1) identified over time (6) as a result of the analysis, visualizing the relationship edges (7) reveals that only a subset of the Root Cause objects (1) are actually causing most of the “damage” resulting from the Performance problem. In addition to the visualization, the Root Cause objects along with the recommendations are explicitly listed in the Details on the right (Figure 4). Green “healthy” edges (8) complete the picture of topological relationships, indicating that the connected Associated Objects are not involved in the problem.

Figure 4

The details section in Figure 4 is very similar and familiar but with a few differences. Section (1) is used to navigate through the problems identified by Meta Analysis.
In addition to the Details the user has access to the individual issues (2) for the timeframe selected (3) that have been involved in the analysis. The rest of the Details section mimics that of P/E/R/C issues already existing in the SIOS iQ product.


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