Aspects relate to a computer implemented method for detecting abnormal contention. The computer implemented method includes collecting resource modeling data for a serially reusable resource, wherein the resource modeling data includes one or more of request count data and contention data and storing, in a computer readable storage medium, the resource modeling data in an in-memory database. The method also includes creating and training a first model and a second model using the resource modeling data and one or more cognitive computing tasks and categorizing a contention event as an abnormal contention event using the first model and the second model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for detecting abnormal contention, the computer implemented method comprising: collecting, using a processor, resource modeling data for a serially reusable resource, wherein the resource modeling data includes one or more of request count data and contention data; storing, in a computer readable storage medium, the resource modeling data in an in-memory database; creating and training, using the processor, a first model and a second model using the resource modeling data and one or more cognitive computing tasks; and categorizing, using the processor, a contention event as an abnormal contention event using the first model and the second model. 2. The computer implemented method of claim 1, wherein the serially reusable resource is selected from a group consisting of: a computer memory, a computer processor, a computer program, a computer data bus, a file, a row in a database table, a piece of code that touches certain memory objects, a database structure in memory, a control block in memory, a shared device, a data set on a shared device, data buffers, and registers. 3. The computer implemented method of claim 1, wherein collecting resource modeling data comprises: collecting request count data during a collection interval, wherein the request count data includes one or more of a first count of requests from jobs to be processed by the serially reusable resource during the collection interval; collecting request count data that includes a second count of requests from jobs to be processed by the serially reusable resource based on a workload, wherein the workload is defined by one or more of CPU usage of the request, memory usage of the request, and time usage of the request; and collecting contention data for the serially reusable resource when the serially reusable resource has at least one request from a job that is waiting. 4. The computer implemented method of claim 3, wherein the contention data includes a first list that includes jobs waiting to be processed by the serially reusable resource and time values of how long each job has been waiting, a second list that includes jobs holding the serially reusable resource and time values for a length of ownership, job identification information for each job on the first list and second list, and a third count of duplicate contention events. 5. The computer implemented method of claim 1, wherein the one or more cognitive computing tasks includes a regression task that categorizes the contention event as an abnormal contention event using the request count data, wherein the regression task includes using statistical analysis to create a curve based on multiple independent variables from the resource modeling data and fitting a dependent variable from the contention data to determine whether the contention event is an abnormal contention event based on fitting of the dependent variable to the curve. 6. The computer implemented method of claim 1, wherein the one or more cognitive computing tasks includes: a classification task that categorizes the contention event as an abnormal contention event based on the contention data, wherein the classification task includes structuring the resource modeling data into a tree structure with nodes and branches and using the structured resource modeling data to determine a group the contention event belongs to, wherein the group is one selected from a group consisting of an abnormal contention event group and a normal contention event group. 7. The computer implemented method of claim 1, wherein the one or more cognitive computing tasks includes: a clustering task that categorizes the contention event as an abnormal contention event based on cluster mapping the resource modeling data and comparing a proximity of the contention event when mapped against the cluster mapping. 8. The computer implemented method of claim 1, wherein the first model and the second model are each selected from a group consisting of: a first regression model of rates of serialization request over time; a second regression model of rates of requests based on workloads run per system; a first clustering model of patterns of serialization requests across multiple resources and resource types; a second clustering model of patterns of contention across multiple resources and resource types; a first classification model of contention based on individual resources; a second classification model of contention based on length of ownership; and a third classification model of contention based on length of waiting. 9. The computer implemented method of claim 1, wherein categorizing the contention event comprises: analyzing the contention event using the first model; analyzing the contention event using the second model; correlating the first model analysis and the second model analysis; and categorizing the contention event based on the correlation. 10. The computer implemented method of claim 1, wherein categorizing the contention event comprises: analyzing the contention event using the first model; analyzing the contention event using the second model; averaging the first model analysis and the second model analysis to give a determination of normal or abnormal, wherein the determination includes a weighted average based on one or more factors including at least one from a group consisting of a confidence level of a determined result, a confidence level of the cognitive computing task used, and a combination of factors; calculating a confidence percentage; and categorizing the contention event based on the determination and the confidence percentage. 11. A system for detecting abnormal contention, the system comprising: a memory having computer readable instructions; and one or more processors for executing the computer readable instructions, the computer readable instructions comprising: collecting resource modeling data for a serially reusable resource, wherein the resource modeling data includes one or more of request count data and contention data; storing, in the memory, the resource modeling data in an in-memory database; creating and training a first model and a second model using the resource modeling data and one or more cognitive computing tasks; and categorizing a contention event as an abnormal contention event using the first model and the second model. 12. The system of claim 11, wherein the serially reusable resource is selected from a group consisting of: a computer memory, a computer processor, a computer program, a computer data bus, a file, a row in a database table, a piece of code that touches certain memory objects, a database structure in memory, a control block in memory, a shared device, a data set on a shared device, data buffers, and registers. 13. The system of claim 11, wherein collecting resource modeling data comprises: collecting request count data during a collection interval, wherein the request count data includes one or more of a first count of requests from jobs to be processed by the serially reusable resource during the collection interval; collecting request count data that includes a second count of requests from jobs to be processed by the serially reusable resource based on a workload, wherein the workload is defined by one or more of CPU usage of the request, memory usage of the request, and time usage of the request; and collecting contention data for the serially reusable resource when the serially reusable resource has at least one request from a job that is waiting, wherein the contention data includes a first list that includes jobs waiting to be processed by the serially reusable resource and time values of how long each job has been waiting, a second list that includes jobs holding the serially reusable resource and time values for a length of ownership, job identification information for each job on the first list and second list, and a third count of duplicate contention events. 14. The system of claim 11, wherein the one or more cognitive computing tasks include one or more from a group consisting of: a regression task that categorizes the contention event as an abnormal contention event using the request count data, wherein the regression task includes using statistical analysis to create a curve based on multiple independent variables from the resource modeling data and fitting a dependent variable from the contention data to determine whether the contention event is an abnormal contention event based on fitting of the dependent variable to the curve; a classification task that categorizes the contention event as an abnormal contention event based on the contention data, wherein the classification task includes structuring the resource modeling data into a tree structure with nodes and branches and using the structured resource modeling data to determine a group the contention event belongs to, wherein the group is one selected from a group consisting of an abnormal contention event group and a normal contention event group; and a clustering task that categorizes the contention event as an abnormal contention event based on cluster mapping the resource modeling data and comparing a proximity of the contention event when mapped against the cluster mapping. 15. The system of claim 11, wherein the first model and the second model are each selected from a group consisting of: a first regression model of rates of serialization request over time; a second regression model of rates of requests based on workloads run per system; a first clustering model of patterns of serialization requests across multiple resources and resource types; a second clustering model of patterns of contention across multiple resources and resource types; a first classification model of contention based on individual resources; a second classification model of contention based on length of ownership; and a third classification model of contention based on length of waiting. 16. The system of claim 11, wherein categorizing the contention event comprises: analyzing the contention event using the first model; analyzing the contention event using the second model; and correlating the first model analysis and the second model analysis. 17. The system of claim 11, wherein categorizing the contention event comprises: analyzing the contention event using the first model; analyzing the contention event using the second model; averaging the first model analysis and the second model analysis to give a determination of normal or abnormal, wherein the determination includes a weighted average based on one or more factors including at least one from a group consisting of a confidence level of a determined result, a confidence level of the cognitive computing task used, and a combination of factors; calculating a confidence percentage; and categorizing the contention event based on the determination and the confidence percentage. 18. A computer program product for detecting abnormal contention, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: collect resource modeling data for a serially reusable resource, wherein the resource modeling data includes one or more of request count data and contention data; store the resource modeling data in an in-memory database; create and train a first model and a second model using the resource modeling data and one or more cognitive computing tasks; and categorize a contention event as an abnormal contention event using the first model and the second model. 19. The computer program product for detecting abnormal contention of claim 18, wherein categorizing the contention event comprises program instructions executable by the processor to cause the processor to: analyze the contention event using the first model; analyze the contention event using the second model; average the first model analysis and the second model analysis to give a determination of normal or abnormal, wherein the determination includes a weighted average based on one or more factors including at least one from a group consisting of a confidence level of a determined result, a confidence level of the cognitive computing task used, and a combination of factors; calculate a confidence percentage; and categorize the contention event based on the determination and the confidence percentage. 20. The computer program product for detecting abnormal contention of claim 18, wherein the one or more cognitive computing tasks include one or more from a group consisting of: a regression task that categorizes the contention event as an abnormal contention event using the request count data, wherein the regression task includes using statistical analysis to create a curve based on multiple independent variables from the resource modeling data and fitting a dependent variable from the contention data to determine whether the contention event is an abnormal contention event based on fitting of the dependent variable to the curve; a classification task that categorizes the contention event as an abnormal contention event based on the contention data, wherein the classification task includes structuring the resource modeling data into a tree structure with nodes and branches and using the structured resource modeling data to determine a group the contention event belongs to, wherein the group is one selected from a group consisting of an abnormal contention event group and a normal contention event group; and a clustering task that categorizes the contention event as an abnormal contention event based on cluster mapping the resource modeling data and comparing a proximity of the contention event when mapped against the cluster mapping, and wherein the first model and the second model are each selected from a group consisting of: a first regression model of rates of serialization request over time; a second regression model of rates of requests based on workloads run per system; a first clustering model of patterns of serialization requests across multiple resources and resource types; a second clustering model of patterns of contention across multiple resources and resource types; a first classification model of contention based on individual resources; a second classification model of contention based on length of ownership; and a third classification model of contention based on length of waiting.