Getting to solid answers around these topics is challenging , however , many companies have been able to determine tangible benefits . For example , a financial services company saw a 10 % productivity gain in their software development after moving to AWS . On a $ 700 million budget , that gain is significant and can help build the business case for a Cloud First commitment .
Finally , it is a best practice to track your financial KPIs as you build your cloud program . Your economic model gets better over time as you add more and more use cases .
# 5 - Discover the Inner-Workings of Your Application Estate
Public cloud environments like AWS , Azure and Google are not fully backward compatible . That means some of your applications are not going to be able to move to the cloud . Depending on the importance of these applications , there will likely be a hybrid cloud network whereby the public cloud provider is connected with a private MPLS circuit . In this mode , cloud-based applications can access legacy on-premise services while still gaining the benefits of a cost efficient and agile infrastructure .
The challenges with hybrid cloud networks include latency issues as well as the volume of data being transmitted through the network . Simply put , you could cripple your cloud program without an understanding of the application mapping and data volume between application dependencies .
The challenge is that it is uncommon for organizations to know the inner-workings of their application estate . Rarely do CMDBs have this level of detail and , more often than not , those team members who did have this information are no longer working in your organization . Companies have built data centers around application centers of gravity . Without a solid understanding of what the connections are and how much data travels between those applications , there is little hope for program success .
Automation , Tools & Heroic Efforts
Application discovery is not easy . The good news is tools and automation make the job far less painful .
Discovery Automation
Using automation to discover virtual machine profiles is nothing new . Most hypervisors will give you this information and there are numerous third-party tools that will sniff out virtual and physical server details ( such as RAM , cores , etc ). However , there are few that will tell you the connections between VMs , the frequency between service calls , and the volume of data moving between the VMs .
There are agentless software tools that discover all the standard VM profile information and build a dependency map based on service calls . Over time , the tools provide a profile of data flow between VMs . The dependency map is the
60 | THE DOPPLER | SUMMER 2016