System and method for infrastructure resource optimization

1. A method of altering cloud computer resource allocation, the method comprising:

receiving by a processor a first time-series metric describing a process performed by a first computer system;

receiving by the processor a second time-series resource metric describing a computer resource at a cloud computer system;

analyzing by the processor the first time-series metric and the second time-series resource metric as an independent and a dependent variable to determine a gradient coefficient defining the ratio of the rate of change between the first time-series metric and the second time-series resource metric; and

based on the analyzing, altering the allocation of the computer resource at the cloud computer system.

2. The method of claim 1 , wherein analyzing the first time-series metric and the second time-series resource metric as independent and dependent variables comprises calculating an observed time lag defining the time to observe a change in the dependent variable after a change in the independent variable.

3. The method of claim 1 , wherein the method comprises altering the allocation of a computer resource at the cloud computer system based on the gradient coefficient.

4. The method of claim 2 , wherein the method comprises altering the allocation of a computer resource at the cloud computer system based on the time lag.

5. The method of claim 1 , wherein the gradient coefficient is calculated using linear regression analysis.

6. The method of claim 1 , wherein the independent and dependent variables are validated based on a correlation threshold value.

7. The method of claim 1 , further comprising analyzing a corresponding real-time first time-series metric and a corresponding second time-series resource metric as an independent and a dependent variable to determine a real time gradient coefficient defining the ratio of the rate of change between the corresponding first time-series metric and the corresponding second time-series resource metric.

8. The method of claim 1 , wherein the gradient coefficient is compiled in an exponential moving average.

9. The method of claim 2 , wherein the time lag is compiled in an exponential moving average.

10. The method of claim 2 , wherein the time lag is calculated by a sigma correlation function.

11. The method of claim 1 , wherein altering the allocation of the computer resource at the cloud computer system is performed by a scaling execution module.

12. The method of claim 1 , wherein altering the allocation of the computer resource at the cloud computer system comprises an action selected from the group consisting of: changing the numbers of routers used, changing the number of recorders used, and changing the amount of storage used.

13. A system of altering cloud computer resource allocation, comprising:

a memory; and

a processor configured to:

receive a first time-series metric describing a process performed by a first computer system;

receive a second time-series resource metric describing a computer resource at a cloud computer system;

analyze the first time-series metric and the second time-series resource metric as an independent and a dependent variable to determine a gradient coefficient defining the ratio of the rate of change between the first time-series metric and the second time-series resource metric; and

based on the analyzing, alter the allocation of the computer resource at the cloud computer system.

14. The system of claim 13 , wherein the processor is configured to analyze the first time-series metric and the second time-series resource metric as independent and dependent variables comprises calculating an observed time lag defining the time to observe a change in the dependent variable after a change in the independent variable.

15. The system of claim 13 , wherein the processor is configured to alter the allocation of a computer resource at the cloud computer system based on the gradient coefficient.

16. The system of claim 14 , wherein the processor is configured to alter the allocation of a computer resource at the cloud computer system based on the time lag.

17. The system of claim 13 , wherein the gradient coefficient is calculated using linear regression analysis.

18. The system of claim 13 , wherein the independent and dependent variables are validated based on a correlation threshold value.

19. The system of claim 13 , wherein the processor is further configured to analyze a corresponding real-time first time-series metric and a corresponding second time-series resource metric as an independent and a dependent variable to determine a real time gradient coefficient defining the ratio of the rate of change between the corresponding first time-series metric and the corresponding second time-series resource metric.

20. The system of claim 13 , wherein the gradient coefficient is compiled in an exponential moving average.

21. The system of claim 14 , wherein the time lag is compiled in an exponential moving average.

22. A method of determining a trend in cloud computer resource allocation, the method comprising:

receiving by a processor a series of measurements describing a set of events occurring at a first computer system;

receiving by the processor a series of resource measurements describing resource usage at a cloud computer system;

analyzing by the processor the series of measurements and the series of resource measurements to generate a gradient coefficient defining the ratio of the rate of change between the series of measurements and series of resource measurements; and

based on the analyzing, altering the allocation of the computer resource at the cloud computer system.