Capacity Analysis Using Closed-System Modules

Abstract

Disclosed are various embodiments of a capacity analysis tool using system modules. In one example, a system generates a target model for a targeted computing system that includes a first virtual machine and a second virtual machine. The target model can include system models for the first virtual machine and the second virtual machine, and each of the system models can represent one of multiple parameters of the first virtual machine and the second virtual machine. A function of time for each of the plurality of parameters can be generated based at least in part a time series of datapoints for the parameters. An estimated point in time of contention between a first parameter for the first virtual machine and a second parameter for the second virtual machine can be identified. A usable capacity for the first parameter and the second parameter can be determined.

Therefore, the following is claimed: 
     
         1 . A system, comprising:
 a computing device comprising a processor and a memory; and   program instructions executable in the computing device, wherein the instructions, when executed, cause the computing device to at least:
 generate a target model for a targeted computing system that includes a first virtual machine and a second virtual machine, the target model comprises a plurality of system models for the first virtual machine and the second virtual machine, each of the plurality of system models representing one of a plurality of parameters of the first virtual machine and the second virtual machine, and each parameter has a capacity constraint; 
 generate a function of time for each of the plurality of parameters based at least in part a time series of datapoints for each of the plurality of parameters; 
 identify an estimated point in time of contention between a first parameter for the first virtual machine and a second parameter for the second virtual machine based at least in part on the function of time and the capacity constraint; and 
 determine a usable capacity for the first parameter and the second parameter based at least in part on the estimated point in time of contention and the function of time. 
   
     
     
         2 . The system of  claim 1 , wherein the estimated point in time of contention represents a respective point in time in which computing resource demand for the first parameter and the second parameter exceeds computing resource capacity for the first parameter and the second parameter. 
     
     
         3 . The system of  claim 1 , wherein the usable capacity comprises a first usable capacity for the first parameter and a second usable capacity for the second parameter, and the instructions, when executed, cause the computing device to at least:
 determine the second usable capacity for the second parameter of the second virtual machine based at least in part on the first usable capacity and a relationship between the first virtual machine and the second virtual machine.   
     
     
         4 . The system of  claim 1 , wherein determining the usable capacity based at least in part on a third parameter for a third virtual machine that has been cloned from the first virtual machine. 
     
     
         5 . The system of  claim 1 , wherein the instructions, when executed, cause the computing device to at least:
 detect a configuration change for the first virtual machine or the second virtual machine; and   determine an updated function of time for each of the plurality of parameters based at least in part the configuration change and the time series of datapoints for each of the plurality of parameters.   
     
     
         6 . The system of  claim 1 , wherein the first parameter represents a first memory parameter for the first virtual machine and the second parameter represents a second memory parameter for the second virtual machine, and the function of time represents a first demand function of time and a second demand function of time. 
     
     
         7 . The system of  claim 6 , wherein identifying the estimated point in time of contention is based at least in part on a summation of the first demand function of time for the first memory parameter and the second demand function of time for the second memory parameter. 
     
     
         8 . A non-transitory computer-readable medium embodying program instructions executable in a computing device that, when executed by the computing device, cause the computing device to at least:
 generate a target model for a targeted computing system that includes a first virtual machine and a second virtual machine, the target model comprises a plurality of system models for the first virtual machine and the second virtual machine, each of the plurality of system models representing one of a plurality of parameters of the first virtual machine and the second virtual machine, and each parameter has a capacity constraint;   generate a function of time for each of the plurality of parameters based at least in part a time series of datapoints for each of the plurality of parameters;   identifying an estimated point in time of contention between a first parameter for the first virtual machine and a second parameter for the second virtual machine based at least in part on the function of time and the capacity constraint; and   determine a usable capacity for the first parameter and the second parameter based at least in part on the estimated point in time of content and the function of time.   
     
     
         9 . The non-transitory computer-readable medium of  claim 8 , wherein the estimated point in time of contention represents a respective point in time in which computing resource demand for the first parameter and the second parameter exceeds computing resource capacity for the first parameter and the second parameter. 
     
     
         10 . The non-transitory computer-readable medium of  claim 8 , wherein the usable capacity comprises a first usable capacity for the first parameter and a second usable capacity for the second parameter, and the program instructions, when executed by the computing device, cause the computing device to at least:
 determine the second usable capacity for the second parameter of the second virtual machine based at least in part on the first usable capacity and a relationship between the first virtual machine and the second virtual machine.   
     
     
         11 . The non-transitory computer-readable medium of  claim 8 , wherein determining the usable capacity based at least in part on a third parameter for a third virtual machine that has been cloned from the first virtual machine. 
     
     
         12 . The non-transitory computer-readable medium of  claim 8 , wherein the program instructions, when executed by the computing device, cause the computing device to at least:
 detect a configuration change for the first virtual machine or the second virtual machine; and   determine an updated function of time for each of the plurality of parameters based at least in part the configuration change and the time series of datapoints for each of the plurality of parameters.   
     
     
         13 . The non-transitory computer-readable medium of  claim 8 , wherein the first parameter represents a first memory parameter for the first virtual machine and the second parameter represents a second memory parameter for the second virtual machine, and the function of time represents a first demand function of time and a second demand function of time. 
     
     
         14 . A method, comprising:
 generating, by a computing device, a target model for a targeted computing system that includes a first virtual machine and a second virtual machine, the target model comprises a plurality of system models for the first virtual machine and the second virtual machine, each of the plurality of system models representing one of a plurality of parameters of the first virtual machine and the second virtual machine, and each parameter has a capacity constraint;   generating, by the computing device, a function of time for each of the plurality of parameters based at least in part a time series of datapoints for each of the plurality of parameters;   identifying, by the computing device, an estimated point in time of contention between a first parameter for the first virtual machine and a second parameter for the second virtual machine based at least in part on the function of time and the capacity constraint; and   determining, by the computing device, a usable capacity for the first parameter and the second parameter based at least in part on the estimated point in time of content and the function of time.   
     
     
         15 . The method of  claim 14 , wherein the estimated point in time of contention represents a respective point in time in which computing resource demand for the first parameter and the second parameter exceeds computing resource capacity for the first parameter and the second parameter. 
     
     
         16 . The method of  claim 14 , wherein the usable capacity comprises a first usable capacity for the first parameter and a second usable capacity for the second parameter, and the further comprising:
 determining, by the computing device, the second usable capacity for the second parameter of the second virtual machine based at least in part on the first usable capacity and a relationship between the first virtual machine and the second virtual machine.   
     
     
         17 . The method of  claim 14 , wherein determining the usable capacity based at least in part on a third parameter for a third virtual machine that has been cloned from the first virtual machine. 
     
     
         18 . The method of  claim 14 , further comprising:
 detecting, by the computing device, a configuration change for the first virtual machine or the second virtual machine; and   determining, by the computing device, an updated function of time for each of the plurality of parameters based at least in part the configuration change and the time series of datapoints for each of the plurality of parameters.   
     
     
         19 . The method of  claim 14 , wherein the first parameter represents a first memory parameter for the first virtual machine and the second parameter represents a second memory parameter for the second virtual machine, and the function of time represents a first demand function of time and a second demand function of time. 
     
     
         20 . The method of  claim 19 , wherein identifying the estimated point in time of contention is based at least in part on a summation of the first demand function of time for the first memory parameter and the second demand function of time for the second memory parameter.