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Isolate Application and Network Performance Degradations

The first step in problem-solving consists of gathering sufficient information to investigate the case efficiently. The initial information usually available to IT analysts is often incomplete; it typically contains far too little objective data regarding the performance degradation or the application error.

Skylight™ collects and stores objective measurements of 100% of the transactions performed by users with all applications, including Citrix XenApp and XenDesktop sessions, HTTP/S sessions (including public and private SaaS applications), VoIP calls, SQL database transactions, and CIFS/SMB file transfers. This data is then summarized in instant-playback dashboards for analysts to understand each of the five Ws: What happened exactly? Who was involved? Where did it take place? When did it occur? Why did it happen?

Which Applications Are Affected?

Skylight sensors automatically generate an alert when a critical application provides a degraded end-user experience. IT professionals immediately understand when the problem occurred and which components contribute to the degradation: client, network, server processing, or data volumes.

When Was Performance Degraded?

In addition to displaying when the problem occurred, Skylight sensors generate a baseline to understand what the “normal” behavior of the application is, and when its performance was degraded, helping IT analysts to correlate the new behavior with the overall IT change log.

Which Users Were Impacted?

With a single click, IT analysts can view which servers and end-users have experienced degraded performance and where to look for the root cause.

Which Application Transactions Were Involved?

What were users attempting to do when the performance degradation appeared? To provide the answer to this question, Skylight tracks all end-user activities and reports on 100% of network flows as well as 100% of application transactions. As a result, an IT analyst can quickly identify the specific transactions impacted during the performance degradation in order to provide accurate and precise feedback to the application team.