Network probe placement optimization

Abstract

Intelligent network probe placement optimization (e.g., using a computerized tool) is enabled. A method can comprise determining, from an inventory database, physical interfaces and service paths for data traffic to be monitored, according to a two-way active management protocol, with respect to network interfaces between cloud compute elements in a leaf-spine cloud fabric and radio access network equipment, based on the cloud compute elements, determining directed acyclic graph information representative of a directed acyclic graph of connections between the cloud compute elements and other cloud compute elements in the leaf-spine cloud fabric other than the cloud compute elements, and based on the directed acyclic graph information, determining a number and a distribution of probes to be employed at at least some of the physical interfaces and the service paths to monitor a parameter of the data traffic according to the two-way active management protocol.

What is claimed is: 
     
         1 . A system, comprising:
 a processor; and   a memory that stores executable instructions that, when executed by the processor, facilitate performance of operations, comprising:
 based on a number and a distribution of probes employable, at at least some physical interfaces and service paths for data traffic, to monitor a parameter of the data traffic according to a two-way active management protocol, generating a Monte Carlo sampling for attribute values for the probes, wherein the attribute values comprise a source-ip value, a destination-ip value, a source-port value, and a destination-port value; and 
 based on the Monte Carlo sampling, determining a lower limit on a quantity of samples of the service paths for use in monitoring by the probes. 
   
     
     
         2 . The system of  claim 1 , wherein the operations further comprise:
 determining a lower limit on a number of paths of data traffic between groups of cloud compute elements in a leaf-spine cloud fabric usable to monitor according to the two-way active management protocol, wherein the number and the distribution of the probes are determined to be able to monitor at least the lower limit on the number of paths of data traffic.   
     
     
         3 . The system of  claim 1 , wherein the probes comprise pairs of sender probes and receiver probes defined according to the two-way active management protocol. 
     
     
         4 . The system of  claim 1 , wherein determining the lower limit on the quantity of samples of the service paths comprises determining the lower limit on the quantity of samples of the service paths based on a specified degree of statistical certainty. 
     
     
         5 . The system of  claim 4 , wherein increasing the specified degree of statistical certainty results correspondingly in a larger lower limit on the quantity of samples that is larger than the lower limit on the quantity of samples, and wherein the larger lower limit comprises more samples than the lower limit on the quantity of samples. 
     
     
         6 . The system of  claim 4 , wherein decreasing the specified degree of statistical certainty results correspondingly in a smaller lower limit on the quantity of samples that is smaller than the lower limit on the quantity of samples, and wherein the smaller lower limit comprises fewer samples than the lower limit on the quantity of samples. 
     
     
         7 . The system of  claim 1 , wherein the number and the distribution of probes to be employed are determined using a depth first search function. 
     
     
         8 . The system of  claim 1 , wherein the probes comprise physical network probes. 
     
     
         9 . The system of  claim 1 , wherein the probes comprise virtual network probes. 
     
     
         10 . A non-transitory machine-readable medium, comprising executable instructions that, when executed by a processor, facilitate performance of operations, comprising:
 based a number and a distribution of probes to be employed at physical interfaces of a group of physical interfaces and service paths of a group of physical interfaces, determining a lower limit on a number of paths of data traffic among cloud compute elements in a leaf-spine cloud fabric and other cloud compute elements in the leaf-spine cloud fabric, other than the cloud compute elements, usable to monitor according to a two-way active management protocol, wherein the number and the distribution of the probes are determined to be able to monitor the lower limit on the number of paths of the data traffic; and   determining a quantity of samples to be obtained, by the probes, that are able to achieve at least a specified degree of statistical certainty applicable to sampling coverage for a parameter of the data traffic, according to the two-way active management protocol, wherein the quantity of samples is determined using a harmonic series function associated with the service paths.   
     
     
         11 . The non-transitory machine-readable medium of  claim 10 , wherein the samples are obtained according to a defined flow duration. 
     
     
         12 . The non-transitory machine-readable medium of  claim 11 , wherein the defined flow duration is approximately six minutes. 
     
     
         13 . The non-transitory machine-readable medium of  claim 11 , wherein the probes determine at least two of a latency, a loss, or a jitter of the data traffic. 
     
     
         14 . A method, comprising:
 based a number and a distribution of probes employable, at a group of physical interfaces and a group of service paths for data traffic, to monitor a parameter of the data traffic according to a two-way active management protocol, generating, by network equipment comprising a processor, a Monte Carlo sampling for attribute values for the probes, wherein the attribute values represent source-ip, destination-ip, source-port, and destination-port; and   based on the Monte Carlo sampling, determining, by the network equipment, a limit applicable to a quantity of samples of the service paths for monitoring of the parameter.   
     
     
         15 . The method of  claim 14 , wherein the samples are obtained during a defined flow duration. 
     
     
         16 . The method of  claim 15 , wherein the defined flow duration is approximately six minutes. 
     
     
         17 . The method of  claim 14 , wherein the probes determine at least two of a latency, a loss, or a jitter of the data traffic. 
     
     
         18 . The method of  claim 14 , further comprising:
 determining, by the network equipment, a limit on a group of paths of data traffic between groups of cloud compute elements in a leaf-spine cloud fabric, wherein the groups of cloud compute elements are employable to monitor the group of physical interfaces and the group of service paths for the data traffic according to the two-way active management protocol.   
     
     
         19 . The method of  claim 18 , wherein the number and the distribution of the probes are determined to monitor the limit on the group of paths of data traffic. 
     
     
         20 . The method of  claim 14 , wherein determining the limit on the quantity of samples of the service paths comprises determining the limit on the quantity of samples of the service paths as a function of a specified degree of statistical certainty.