We were discussing virtualization and software defined stacks. Servers became virtual machines which allowed automated provisioning, load balancing and management processes. Hypervisors could manage a variety of virtual machines and improve performance and scalability. Network and storage assets were rather slow to catch on.  Software defined technology stack aims to virtualize compute, network, storage and security aspects. The primary benefit of SDN is that it makes provisioning dynamic as opposed to the static configuration from physical resources. It helps to manage complexity. It performs segmentation, workload monitoring, conditional forwarding, and automated switching for dynamic optimization and scaling.
Today we see the example of SDDC as an enabler to build a sustainable analytics platform for an insurer. Analytics require a lot of storage. And the insurer wanted rapid realization of benefits from analytics by folding timelines. In this case, a software defined storage was paired with a platform-as-a-service to help the insurer test and deploy new ideas. This enabled different purpose analytics such as product, pricing and fraud analysis to be implemented and deployed independently.
The enablement of development through virtualized, elastic environments is a significant boost to reducing total cost of ownership as well as reduction in timeline.
The insurance industry was a great example to study SDS because it is driven by information and spends a lot of time and effort on IT on a per employee basis. This money is typically spent on old and new platforms and stacks. Together with a changing landscape of applications involving newer trends such as Big Data, the pace of modernizing the infrastructure and keeping it all consistent becomes difficult. SDS relieves the demand without requiring the supply to change significantly.
#codingexercise

int GetCountSquareSubMatrixSizekCountZeros (int[,] A, int rows, int cols, int k, int zeros)  

{  

int max = INT_MIN;  

for ( int I = 0; I  < rows; i++) {  

    for ( int j = 0; j < cols; j++) {  

            // use this as the start of submatrix  

            int count = 0;  

            for ( int x = i; x < k; x++)  

                for ( int y = j; y < k; y++)  

                       If  ( x < rows && y < cols && A[x,y] == 0) 

                            count += 1;   

              If (count > max) max = count; 

      }  

}  

return max;  

}