Cisco Cisco Tetration Analytics G1 白書

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White Paper

Behavior-Based Application Insight: Helping You

Understand What’s Running in Your Data Center

What You Will Learn

This document discusses the following topics:

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How the evolution of data center technology has changed the approach to application dependency mapping

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Why gaining insight into applications and their dependencies is cumbersome in a dynamic data center

environment

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Various approaches that customers have tried to gain application insight

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How the Cisco Tetration Analytics

™

platform can help solve the application insight problem

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How application insight works in Cisco Tetration Analytics

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What application insight enables you to do

Current Data Center Environment

The modern data center has evolved in a brief period of time into the complex environments seen today, with

extremely fast, high-density switching pushing large volumes of traffic, and multiple layers of virtualization and

overlays. The result is a highly abstract network that can be difficult to secure, monitor, and troubleshoot.

Data centers typically handle millions of flows a day, and are fast approaching billions of flows. On average, there

are 10,000 active flows per rack at any given second (Benson, Akella, & Maltz, 2010). At the same time,

networking teams are being asked to increase their operational efficiency and secure the ever-expanding attack

surface of their networks.

Although this accelerated rate of change is helping increase the scale and complexity of the applications and

solutions that organizations can deliver, it is also putting additional strain on networking teams to respond to these

changes.

The type of workload deployed is changing in nature as well. The popularization of microservices has caused

development of containerized applications that exhibit entirely different behavior on the network than traditional

services. Their lifecycles often can be measured in milliseconds, making their operations difficult to capture for

analysis. The same effect can be seen with hybrid cloud and virtualized workloads, which move between

hypervisors as needed. Furthermore, highly scaled data centers are challenging TCP to meet their high-

performance needs, including the need to handle multiple unique conversations within the same long-lived TCP

session (Qiu, Zhang, & Keshav, 2001).

To be able to efficiently and confidently respond to changes, you need a complete understanding of the

applications that depend on your network (Xu, Zhang, Mao, & Bahl, 2008). In data centers, it can be almost

impossible to manually catalog and inventory the myriad applications that are running at any one time, and doing

so usually requires extensive and often challenging manual collaboration across a number of separate groups.