Methods for monitoring computer resources using a first and second matrix, and a feature relationship tree

1. A method, comprising the steps of:

a) retrieving a first set of records for a first predetermined time interval from one or more resource-metric records stored at one or more electronic storage devices, wherein said one or more resource-metric records at least contain a resource-metric identifier, a metric's value, and a date-time value when said metric's value was obtained,

b) forming a first mathematical matrix containing one or more metric's values arranged on date-time and resource-metric axes,

c) creating a second mathematical matrix containing one or more features and a third mathematical matrix containing one or more weights, wherein multiplication of said second mathematical matrix and said third mathematical matrix produces said first mathematical matrix,

d) building a feature relationship tree, wherein each feature in said one or more features is assigned a list of date-time values and date-time weights from said third mathematical matrix, and said each feature is assigned a list of resource-metric identifiers and resource-metric weights from said second mathematical matrix,

e) generating a predicted value for said resource-metric identifier and a most recent date-time value in said feature relationship tree,

f) determining a variance between said predicted value and said metric's value for said resource-metric identifier, and

g) if said variance exceeds a predetermined alert threshold, then triggering an alert.

2. The method of claim 1 , further comprising the step of:

h) collecting said one or more resource-metric records from one or more resources on a computer network.

3. The method of claim 1 , further comprising the step of:

h) saving said one or more resource-metric records to said one or more electronic storage devices.

4. The method of claim 1 , wherein said creating said second mathematical matrix and said third mathematical matrix is achieved by applying a non-negative matrix factorization algorithm to said first mathematical matrix.

5. The method of claim 1 , further comprising the step of:

h) sorting said list of date-time values and date-time weights for said each feature by date-time weight in descending order in said feature relationship tree.

6. The method of claim 1 , further comprising the step of:

h) sorting said list of resource-metric identifiers and resource-metric weights for said each feature by resource-metric weight in descending order in said feature relationship tree.

7. The method of claim 1 , further comprising the step of:

h) sorting said features by highest date-time weight of said each feature in descending order in said feature relationship tree.

8. The method of claim 1 , further comprising the step of:

h) pruning nodes in said list of date-time values and date-time weights by leaving top N1 nodes by date-time weights in said each feature and deleting nodes with sequential positions more than N1, wherein N1 is a first predetermined value.

9. The method of claim 1 , further comprising the step of:

h) pruning nodes in said list of resource-metric identifiers and resource-metric weights by leaving top N2 nodes by resource-metric weights in said each feature and deleting nodes with sequential positions more than N2, wherein N2 is a second predetermined value.

10. The method of claim 1 , further comprising the step of:

h) deleting features from said feature relationship tree if said features do not contain a node corresponding to said most recent date-time value in said first set of records retrieved from said one or more electronic storage devices.

11. The method of claim 1 , wherein said generating said predicted value for said resource-metric identifier and said most recent date-time value in said feature relationship tree is achieved by averaging said metric's values.

12. The method of claim 1 , wherein said generating said predicted value for said resource-metric identifier and said most recent date-time value in said feature relationship tree is achieved via a k-Nearest Neighbor algorithm.

13. The method of claim 1 , wherein said variance between said predicted value and said metric's value is normalized on a sliding scale.

14. The method of claim 1 , wherein said variance between said predicted value and said metric's value is normalized on a scale between 0 and 1.

15. The method of claim 1 , wherein said variance between said predicted value and said metric's value is normalized using a Gaussian function.

16. The method of claim 1 , further comprising the step of:

h) verifying that a latest metric's value for said resource-metric identifiers will trigger said alert before triggering said alert.

17. A method, comprising the steps of:

a) collecting one or more resource-metric records from one or more resources on a computer network, wherein said one or more resource-metric records at least contain a resource-metric identifier, a metric's value, and a date-time value when said metric's value was obtained,

b) saving said one or more resource-metric records to one or more electronic storage devices,

c) retrieving a first set of records from said one or more resource-metric records for a first predetermined time interval from said one or more electronic storage devices,

d) forming a first mathematical matrix containing one or more metric's values arranged on date-time and resource-metric axes,

e) creating a second mathematical matrix containing one or more features and a third mathematical matrix containing one or more weights by applying a non-negative matrix factorization algorithm to said first mathematical matrix, wherein multiplication of said second mathematical matrix and said third mathematical matrix produces said first mathematical matrix,

f) building a feature relationship tree, wherein each feature in said one or more features is assigned a list of date-time values and date-time weights from said third mathematical matrix, and said feature is assigned a list of resource-metric identifiers and resource-metric weights from said second mathematical matrix,

g) sorting said list of date-time values and date-time weights for said each feature by date-time weight in descending order in said feature relationship tree,

h) sorting said list of resource-metric identifiers and resource-metric weights for said each feature by resource-metric weight in descending order in said feature relationship tree,

i) sorting said features by highest date-time weight of said each feature in descending order in said feature relationship tree,

j) pruning nodes in said list of date-time values and date-time weights by leaving top N1 nodes by date-time weights in said each feature and deleting nodes with sequential positions more than N1, wherein N1 is a first predetermined value,

k) pruning nodes in said list of resource-metric identifiers and resource-metric weights by leaving top N2 nodes by resource-metric weights in said each feature and deleting nodes with sequential positions more than N2, wherein N2 is a second predetermined value,

l) deleting features from said feature relationship tree if said features do not contain a node corresponding to a most recent date-time value in said first set of records retrieved from said one or more electronic storage devices,

m) generating a predicted value for each resource-metric identifier and said most recent date-time value in said feature relationship tree via a k-Nearest Neighbor algorithm,

n) determining a variance between said predicted value and said metric's value for said each resource-metric identifier as a normalized value between 0 and 1 using a Gaussian function for said each resource-metric identifier, and

o) if said variance exceeds a predetermined alert threshold, then triggering an alert.

18. A method, comprising the steps of:

a) retrieving a first set of records for a single resource and a first predetermined time interval from one or more metric records stored at one or more electronic storage devices, wherein said one or more metric records at least contain a metric identifier, a metric's value, and a date-time value when said metric's value was obtained,

b) forming a first mathematical matrix containing one or more metric's values arranged on date-time and metric identifier axes,

c) creating a second mathematical matrix containing one or more features and a third mathematical matrix containing one or more weights, wherein multiplication of said second mathematical matrix and said third mathematical matrix produces said first mathematical matrix,

d) building a feature relationship tree, wherein each feature in said one or more features is assigned a list of date-time values and date-time weights from said third mathematical matrix, and said each feature is assigned a list of metric identifiers and metric identifier weights from said second mathematical matrix,

e) generating a predicted value for said metric identifier and a most recent date-time value in said feature relationship tree,

f) determining a variance between said predicted value and said metric's value for said metric identifier, and

g) if said variance exceeds a predetermined alert threshold, then triggering an alert.

19. A method, comprising the steps of:

a) retrieving a first set of records for a single metric and a first predetermined time interval from one or more resource records stored at one or more electronic storage devices, wherein said one or more resource records at least contain a resource identifier, a metric's value, and a date-time value when said metric's value was obtained,

b) forming a first mathematical matrix containing one or more metric's values arranged on date-time and resource identifier axes,

c) creating a second mathematical matrix containing one or more features and a third mathematical matrix containing one or more weights, wherein multiplication of said second mathematical matrix and said third mathematical matrix produces said first mathematical matrix,

d) building a feature relationship tree, wherein each feature in said one or more features is assigned a list of date-time values and date-time weights from said third mathematical matrix, and said each feature is assigned a list of resource identifiers and resource identifier weights from said second mathematical matrix,

e) generating a predicted value for said resource identifier and a most recent date-time value in said feature relationship tree,

f) determining a variance between said predicted value and said metric's value for said resource identifier, and

g) if said variance exceeds a predetermined alert threshold, then triggering an alert.