METHOD, SYSTEM AND PROGRAM PRODUCT FOR ALLOCATION AND/OR PRIORITIZATION OF ELECTRONIC RESOURCES

• US 20130080641A1
• Filed: 09/25/2012
• Published: 03/28/2013
• Est. Priority Date: 09/26/2011
• Status: Active Grant

• Current Assignee: KNOA Software Incorporated
• Original Assignee: KNOA Software Incorporated
• Inventors: Lui, Philip, Rayna, David, Sypniewski, Anthony
• Granted Patent: US 9,225,772 B2
• Time in Patent Office: Days
• US Class Current: 709/226
• CPC Class Codes: G06F 9/505 considering the load
• CPC Class Codes: H04L 41/046 comprising network management agents or mobile agents therefor
• CPC Class Codes: H04L 41/0886 Fully automatic configuration
• CPC Class Codes: H04L 41/147 for predicting network behaviour
• CPC Class Codes: H04L 41/20 Network management software packages
• CPC Class Codes: H04L 41/22 comprising specially adapted graphical user interfaces [GUI]
• CPC Class Codes: H04L 41/40 using virtualisation of network functions or resources, e.g. SDN or NFV entities
• CPC Class Codes: H04L 43/045 for graphical visualisation of monitoring data
• CPC Class Codes: H04L 43/065 related to network devices
• CPC Class Codes: H04L 43/0817 by checking functioning
• CPC Class Codes: H04L 43/14 using software, i.e. software packages network security related monitoring H04L63/1408
• CPC Class Codes: H04L 47/70 Admission control; Resource allocation
• CPC Class Codes: H04L 47/83 based on usage prediction
• CPC Class Codes: H04L 67/10 in which an application is distributed across nodes in the network software deployment G06F8/60; multiprogramming arrangements G06F9/46

Abstract

A method, system and program product, the method comprising determining a first rule set comprising a plurality of patterns of run-time data; obtaining a second rule set comprising a respective priority assigned to respective of the application context IDs and/or user IDs and/or business priorities or combinations of two or more thereof; receiving run-time data for a first plurality of the user IDs; determining application context IDs running on desktops; generating allocation data and/or prioritization data for allocation of electronic resources for user IDs, based at least in part on the second rule set; and sending signals, based on the allocation data and/or the prioritization data.

Claims

1. A method comprising:

determining or obtaining or receiving, by one or more computers, a first rule set comprising a plurality of patterns of run-time data used to distinguish among a plurality of applications and/or locations within applications running on monitored computers associated with respective user IDs and to provide respective application context IDs that designate respective applications incorporating application locations within applications;

obtaining or receiving, by the one or more computers, a second rule set comprising a respective priority assigned to respective of the application context IDs and/or user IDs and/or business priorities or combinations of two or more thereof;

receiving or obtaining or being presented, by the one or more computers, with run-time data for a first plurality of the user IDs, wherein the runtime data for a respective one of the user IDs comprises and/or is derived from run-time instantiations of objects occurring on the monitored computer for the respective user ID, with the objects comprising one or more selected from the group of methods, system objects, object events, dynamic GUI objects, network component objects, wherein the object events comprise at least one selected from the group of operating system events, application events, and web browser events and network events generated or affecting directly or indirectly the respective monitored computer;

determining, by the one or more computers, respective application context IDs for respective applications and/or respective locations within applications running on the respective user IDs of a second plurality of the user IDs, based at least in part on the first rule set;

generating allocation data and/or prioritization data by the one or more computers, for a respective allocation of electronic resources for the first plurality of the respective user IDs, based at least in part on the second rule set; and

sending signals, by one or more computers, based at least in part on the allocation data and/or the prioritization data.

2. The method as defined in claim 1,

wherein the first rule set and the second rule set are comprised in a single comprehensive rule set and the determining or obtaining or receiving a first rule set step and the obtaining or receiving a second rule set step comprise a single step, and

wherein the determining respective application context IDs step and the generating allocation data and/or prioritization data step may be performed in a single step.

3. The method as defined in claim 1, further comprising:

obtaining a level of the electronic resources currently provided by respective network infrastructure components to a third plurality of the user IDs;

obtaining a level of the electronic resources currently being used directly or indirectly by the third plurality of the user IDs;

determining, by the one or more computers, difference data based at least in part on a difference between the electronic resources currently available and the electronic resources currently being used; and

providing or making accessible, by the one or more computers, this difference data.

4. The method as defined in claim 1, wherein the sending signals step comprises

sending signals to automatically distribute dynamically a respective level of one or more of the electronic resources to facilitate user application combinations, with a respective one of the user application combinations comprising a respective application context ID and a respective user ID.

5. The method as defined in claim 1, wherein the sending signals step comprises

sending signals, by the one or more computers, to a graphical user interface of an administrator display screen, based at least in part on the allocation data, to facilitate decision-making and/or feedback on a dynamic distribution of the electronic resources to facilitate the user ID application and/or task to each of a respective third plurality of the user IDs.

6. The method as defined in claim 1, further comprising:

wherein the run-time data comprises a detected user action and system state change in response to the detected user action for a third plurality of the user IDs;

calculating, by the one or more computers, response time data for each of the third plurality of the respective user IDs, the response time data for a respective one of the user IDs based at least in part on the detected user action and system state change in response to the detected user action for the respective user ID;

determining, by the one or more computers, current application response data for each of the third plurality of the user IDs, wherein the current application response data for a respective one of the third plurality of the user IDs comprises performing a calculation on the application response data for all applications used by the respective user ID or for all detected user actions within an application, over a period of time; and

comparing, by the one or more computers, the application response data for a respective one of the third plurality of the user IDs to prior application response data for the respective one of the third plurality of the user IDs.

7. The method as defined in claim 6, further comprising:

wherein the signals are sent or made available on the allocation data when selected changes in application response data occur.

8. The method as defined in claim 1, wherein the second rule set comprises a set of relative priorities for respective of the user IDs based on one or more selected from the group of user role, department, function, geography, and which market served, associated with the respective user ID.

9. The method as defined in claim 1, wherein the second rule set comprises a set of respective applications that are to receive a higher relative priority compared to other of the applications.

10. The method as defined in claim 1, wherein the second rule set comprises:

a set of relative priorities for user application combinations, with a respective one of the user application combinations comprising a respective application context ID and a respective one of the user IDs, so that selected applications performed by respective of the user ID'"'"'s are provided priority for the electronic resources relative to other of the user IDs.

11. The method as defined in claim 10, wherein the second rule set comprises a set of relative priorities for respective of the user IDs based on a specific task to be carried out by selected ones of the user IDs in a given time frame.

12. The method as defined in claim 1, wherein the run-time data is normalized from the run-time instantiations of objects occurring on the monitored computer for the respective user ID, with the objects comprising one or more selected from the group of methods, object events, dynamic GUI objects, wherein the object events and dynamic GUI objects comprise at least one selected from the group of operating system events, application events, and web browser events generated or affecting directly or indirectly the respective monitored computer and network events.

13. The method as defined in claim 12, wherein an agent associated with one of the monitored computers, comprising a set of programming code stored on a computer-readable medium, configured for executing by the one or more computers, at run-time, performs the step of normalizing the run-time data associated with the respective monitored computer.

14. The method as defined in claim 1, wherein an agent, comprising a set of programming code stored on a computer-readable medium, configured for executing by the one or more computers, at run-time, wherein the agent performs the steps:

monitoring, by the one or more computers, a monitored computer of a respective one of the monitored computers to obtain the run-time data;

detecting, by the one or more computers, whether a pattern in the run-time data corresponds to a rule in the first rule set that designates a respective one of the application context IDs; and

determining, by the one or more computers, whether a rule in the second rule set applies based at least in part on respective application context ID.

15. The method as defined in claim 14, wherein the agent further performs the step:

generating and sending, by the one or more computers, actuation requests to change priorities and/or levels of the electronic resources provided or providable to or associated with the monitored computer which one or more electronic resources are locally controllable by the agent.

16. The method as defined in claim 14, wherein the agent further performs the step:

generating and sending, by the one or more computers and one or more electronic networks, actuation requests to change priorities and/or levels of the electronic resources provided or providable to monitored computer, to an actuation manager program running on one of the one or more other of the computers, which actuation manager program oversees control of electronic resources outside of control of the respective agent.

17. The method as defined in claim 1, wherein the generating allocation data step comprises:

developing, by one or more computers, a matrix of the respective user IDs versus the electronic resources used by the respective user IDs; and

allocating, the electronic resources, by one or more computers, change the matrix based at least in part on the second rule set.

18. The method as defined in claim 1, further comprising:

generating and sending or making accessible, by the one or more computers, for an administrator graphical user interface, data for each of a plurality of the user IDs, the data for a respective one of the user IDs providing an indication of a level of the electronic resources allocated to the respective user ID.

19. The method as defined in claim 1, further comprising:

generating and sending or making accessible data, by the one or more computers, for a user graphical user interface for a respective one of the user IDs showing an indication of an electronic resource level allocated to the respective one user ID.

20. The method as defined in claim 1, further comprising:

generating and sending or making accessible data, by the one or more computers, for a user graphical user interface for a respective one of the user IDs providing an electronic ability to generate and send an electronic resource actuation request and/or a complaint.

21. The method as defined in claim 1, further comprising:

determining, by the one or more computers, a label or other indication for a relative level of importance or priority, based at least in part on the application context ID determined for a respective one of the user IDs; and

generating and sending or making accessible, by the one or more computers, data for a user graphical user interface for a respective one of the user IDs, for the label or other indication for the relative importance or priority of current activity, based at least in part on the run-time data, performed by the respective user ID.

22. The method as defined in claim 1, wherein the generating allocation data step comprises:

generating data to redistribute dynamically, by the one or more computers, the electronic resources allocated to a respective application server that serves a particular one of the applications based at least in part on a number of the user IDs currently accessing the application server and running the particular one application and/or based at least in part on a number of the user IDs currently accessing a different application server.

23. The method as defined in claim 1, further comprising:

generating, by the one or more computers, user ID profile data for each of a third plurality of users, the user ID profile data for a respective one of the third plurality of the user IDs based at least in part on the respective application context IDs detected from the run-time data for the respective one user ID aggregated over a period of time; and

making this user ID profile data accessible electronically, by the one or more computers.

24. The method as defined in claim 1, wherein the generating allocation data step further comprises:

determining a relative allocation level for two or more of the electronic resources selected from the group of CPU cycle allocation, disk input/output bandwidth allocation and memory allocation, to obtain a level of application performance; and

generating adjustments in the allocation levels of the electronic resources, by the one or more computers, to optimize overall performance of a respective one of the monitored computers.

25. The method as defined in claim 1, further comprising sending, by the one or more computers, the allocation data to a plurality of nested-sub-controllers that each control an allocation of the electronic resources to a different respective infrastructure electronic component.

26. The method as defined in claim 1, further comprising:

detecting, by the one or more computers, a pattern comprising one or more selected from the group of an application, a combination of applications, and a combination of an application used by a specified one of the user IDs;

determining a prediction rule for the determined pattern, by the one or more computers, from a prediction rule set, wherein the respective prediction rule in the prediction rule set is based at least in part on a past history of usage of the electronic resources when the pattern is detected;

determining, by the one or more computers, a prediction of electronic resources that may be used at a future time or during a window of time when the pattern is detected based at least in part on the prediction rule set; and

using the prediction of electronic resources, by the one or more computers, in one selected from the group of generating of the allocation data for the electronic resources and providing data or making the data accessible electronically that indicates a need for additional electronic resources.

27. The method as defined in claim 26, wherein use of the prediction of electronic resources in generating the allocation data or providing of the data indicating a need for additional electronic resources is performed only when a predicted level of electronic resources exceeds a current level of the electronic resources or exceeds the current level of the electronic resources by an amount.

28. The method as defined in claim 26, comprising:

generating by agents the allocation data taking into account at least in part the prediction of electronic resources to increase or to decrease a level of the electronic resources allocated to be allocated, wherein a respective one of the agents comprises a set of programming code stored on a computer-readable medium, configured for executing at run-time, on a monitored infrastructure component.

29. The method as defined in claim 26, comprising:

aggregating, by the one or more computers, predictions of electronic resources that may be used at the future time or in the window of time, for shared electronic resource pools, to obtain a predicted shared resource total;

generating and sending or making accessible, by the one or more computers, data for an administrator graphical user interface showing an indication of electronic resource needs based at least in part on the predicted shared resource totals.

30. The method as defined in claim 26, further comprising:

accessing, by the one or more computers, memory storing correlations from a past history of user ID interactions with applications correlated with electronic resource usage over time associated with use of the respective applications or application patterns; and

generating, by the one or more computers, the prediction rule set from these correlations, by the one or more computers, for detected applications based on one or more parameters, wherein a respective one of the rules in the prediction rule set comprises a predicted level of one or more of the electronic resources when a respective application or application pattern is detected.

31. The method as defined in claim 1, further comprising:

filtering out, by the one or more computers, data that does not correspond to a pattern of run-time data corresponding a rule in the first rule set.

32. The method as defined in claim 31, wherein the filtering step is performed by agents, wherein a respective one of the agents comprises a set of programming code stored on a computer-readable medium, configured for executing at run-time, on a monitored infrastructure component.

33. The method as defined in claim 1, further comprising:

controlling, by the one or more computers, software agents disposed to monitor a plurality of virtual machines, to distribute over time the run-time data from the plurality of the virtual machines so that times for receipt of the run-time data from the different respective virtual machines is distributed over time.

34. The method as defined in claim 1, further comprising:

setting, using the one or more computers, a respective resource cap or caps for one or more of the electronic resources, to be allocated to respective ones of the monitored electronic computers or systems;

obtaining a level of the electronic resources currently being used directly or indirectly by one or more of the monitored computers or systems; and

changing, using the one or more computers, one or more of the caps, based at least in part on the level of the electronic resources currently being used directly or indirectly by the one or more of the monitored computers or systems.

35. The method as defined in claim 1, further comprising generating one or more rules of the first rule set and/or the second rule set and/or a single comprehensive rule set that comprises a combination of the first and the second rule sets:

identifying, by the one or more computers, respective identification strings in the run-time data;

reordering, by the one or more computers, the elements in one or more of the strings based at least in part on one or more rules in a tokenizing rule set to obtain reordered identification strings;

sorting, by the one or more computers, the reordered identification strings to create hierarchal groups of adjacent related strings;

receiving and/or determining, by the one or more computers, a selection of one or more of the reordered identification strings; and

creating a rule in at least one selected from the group of the first rule set or the second rule set or the single comprehensive rules, based at least in part on the selected one or more of the reordered identification strings.

36. The method as defined in claim 1, further comprising:

identifying, by the one or more computers, respective identification strings in the run-time data;

hierarchically grouping, by the one or more computers, the identification strings using a hierarchy algorithm;

generating presentation data, by the one or more computers; and

sending or making accessible the presentation data, by the one or more computers, for selection of certain of the identification strings for use in generating one or more rules for the first rule set and/or for the second rule set and/or for a single comprehensive rule set that comprises a combination of the first and the second rule sets.

37. A method comprising:

determining or obtaining or receiving, by one or more computers, a first rule set comprising a plurality of patterns of run-time data used to distinguish among a plurality of applications and/or locations within applications running on monitored computers associated with respective user IDs and to provide respective application context IDs that designate respective applications incorporating application locations within applications;

receiving or obtaining or being presented, by the one or more computers, with run-time data for a first plurality of the user IDs, wherein the runtime data for a respective one of the user IDs comprises and/or is derived from run-time instantiations of objects occurring on the monitored computer for the respective user ID, with the objects comprising one or more selected from the group of methods, system objects, object events, dynamic GUI objects, network component objects, wherein the object events comprise at least one selected from the group of operating system events, application events, and web browser events and network events generated or affecting directly or indirectly the respective monitored computer;

determining, by the one or more computers, respective application context IDs for applications and/or respective locations within applications running on the respective user IDs of a second plurality of the user IDs, based at least in part on the first rule set;

detecting, by the one or more computers, a pattern comprising one or more selected from the group of an application, a combination of applications, and a combination of an application used by a specified one of the user IDs;

determining a prediction rule for the determined pattern, by the one or more computers, from a prediction rule set, wherein the respective prediction rule in the prediction rule set is based at least in part on a past history of usage of the electronic resources when the pattern is detected;

determining, by the one or more computers, prediction data for of electronic resources that may be used at a future time or during a window of time when the pattern is detected based at least in part on the prediction rule set; and

using the prediction data for electronic resources, by the one or more computers, in one selected from the group of generating of the allocation data for the electronic resources and providing data or making the data accessible electronically that indicates a need for additional electronic resources.

38. The method as defined in claim 37, wherein an agent, comprising a set of programming code stored on a computer-readable medium, configured for executing by the one or more computers, at run-time, wherein the agent performs the steps:

monitoring, by the one or more computers, a monitored computer of a respective one of the user IDs to obtain the run-time data; and

detecting, by the one or more computers, whether a pattern determined from the run-time data corresponds to a prediction rule in the prediction rule set.

39. The method as defined in claim 38, wherein the agent further performs the step:

generating and sending, by the one or more computers, actuation requests to change selected priorities or levels of the electronic resources provided or providable to or associated with the monitored computer which one or more electronic resources are locally controllable by the agent.

40. The method as defined in claim 38, wherein the agent further performs the step:

generating and sending, by the one or more computers and one or more electronic networks, actuation requests to change selected priorities or levels of the electronic resources provided or providable to a respective monitored computer, to an actuation manager program running on one of the one or more other of the computers, which actuation manager program oversees control of electronic resources outside of control of the respective agent.

41. The method as defined in claim 37,

receiving or obtaining, by the one or more computers, slowdown data indicating a slow down of a first one of the monitored computers;

wherein the prediction data for electronic resources further includes resource shift data, and

further comprising generating, by the one or more computers, the allocation data to increase the electronic resources to the first monitored computer and to decrease the electronic resources to one or more other of the monitored computers on a temporary basis and then to return the electronic resources to those monitored computers to previous levels.

42. A method, comprising:

receiving or obtaining, by the one or more computers, slowdown data indicating a slow down of a first one of the monitored computers;

determining, by the one or more computers, a resource shift rule applicable at a time of receiving or obtaining the slowdown data, the resource shift rule for taking electronic resources from one or more other monitored computers, the resource shift rule comprising a shift pattern comprising one or more selected from the group of a time of day, a day of the month, a company department, an application, wherein the respective resource shift rule in the resource shift rule set is based at least in part on a past history of usage of the electronic resources when the shift pattern is detected;

determining, by the one or more computers, using the resource shift rule the one or more other of the monitored computers from which to take the electronic resources; and

generating automatically allocation data, by the one or more computers, for shifting additional of the electronic resources to the first monitored computer, to be taken at least in part from the one or more other monitored computers only on a temporary basis and then returning the electronic resources allocated to the first monitored computer and the other monitored computers to previous levels before the shifting.

43. The method as defined in claim 42, further comprising:

monitoring, by one or more computers, a plurality of monitored computers; and

detecting from a plurality of the monitored computers, by the one or more computers, respective data patterns, with each data pattern comprising one or more selected from the group of an application context ID, a combination of application context IDs, and a combination of an application used by a specified user ID,

wherein the generating automatically allocation data for shifting additional electronic resources is performed only when predetermined data patterns from the plurality of the data patterns are detected.

44. A method comprising:

creating or receiving, by one or more computers, a language syntax to define at least in part;

a plurality of rules for assigning token types to a stream of messages to obtain a stream of contextualized token instances;

a plurality of production rules for detecting deterministic event instances from the stream of contextualized token instances;

a plurality of state type rules for detecting state type instances, wherein a state type rule comprises one or more deterministic event types with respective probability weights, wherein a probability weight is the likelihood that, detecting the deterministic event type that results in the system, the respective state type is executed; and

a plurality of respective pattern rules to define respective Markov chains, with each of the pattern rules comprising a set of state types and transition probability weights between the state types;

receiving or obtaining or being presented, by the one or more computers, with the run-time data comprising a stream of messages for systems of a first plurality of the user IDs, wherein the runtime data for a respective one of the user IDs comprises and/or is derived from run-time instantiations of objects occurring on the monitored computer for the respective user ID, with the objects comprising one or more selected from the group of methods, system objects, object events, dynamic GUI objects, network component objects, wherein the object events comprise at least one selected from the group of operating system events, application events, and web browser events and network events generated or affecting directly or indirectly the respective monitored computer;

parsing, by the one or more computers, to apply the language syntax to the stream of messages to extract a stream of the contextualized token instances from the stream of messages based at least in part on the language syntax;

detecting, by the one or more computers, from the stream of the token instances, a stream of the deterministic user event instances, based at least in part on the language syntax;

selecting, by the one or more computers, a plurality of the Markov chains containing one or more of the state type instances that are implied from the stream of messages, based at least in part on the respective probability weights of the state types from the deterministic user event instances that were detected;

computing, by the one or more computers, a probability that the observed system is executing the respective pattern type for each of the respective Markov chains selected; and

determining, by the one or more computers, based at least in part on one or more of the selected Markov chains and next state transitions in the respective Markov chains selected and/or one or more of predicted states in the respective Markov chains selected, predicted prioritization data for electronic resources for the observed system and/or a predicted level of electronic resources that may be needed by the system generating the messages.

45. The method as defined in claim 44, further comprising:

accessing, by the one or more computers, a database containing a plurality of the respective Markov chains; and

associating, by the one or more computers, a priority and/or a level of electronic resources to be allocated for the Markov chain that was selected.

46. The method as defined in claim 44, further comprising:

wherein the determining step determines a predicted level of electronic resources that may be needed by the system generating the messages, and

generating data, by the one or more computers, for display based at least in part on the predicted level of electronic resources for the system generating the messages.

47. The method as defined in claim 44, further comprising:

wherein the determining step determines a predicted level of electronic resources that may be needed by the system generating the messages, and

generating data, by the one or more computers, to adjust available electronic resources in the system generating the messages, based at least in part on the predicted level of electronic resources for the system generating the messages.

48. The method as defined in claim 44, further comprising:

receiving or obtaining, by the one or more computers, a ranking rule set for ranking applications and/or user IDs;

generating ranking data providing a ranking of a plurality of the user IDs, by the one or more computers, based at least in part on the ranking rule set; and

sending or making accessible, by the one or more computers, the ranking data.

49. The method as defined in claim 48, further comprising:

generating, by the one or more computers, based at least in part on the ranking data for a second plurality of the systems of user IDs, a predicted level of electronic resources that may be needed by the second plurality of the systems of user IDs;

obtaining, by the one or more computers, a level of electronic resources allocated to the second plurality of the systems of the user IDs;

comparing, by the one or more computers, the level of electronic resources allocated to the second plurality of the systems of the user IDs with the predicted level of the electronic resources that may be needed by a second plurality of the systems of user IDs to obtain comparison data; and

generating and sending or making accessible electronic resource change data, by the one or more computers, based at least in part on the comparison data.