UTILIZATION BASED INSTALLATION ON A COMPUTING SYSTEM

• US 20080154805A1
• Filed: 12/20/2006
• Published: 06/26/2008
• Est. Priority Date: 12/20/2006
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Kumar, Arvind, Gurumoorthy, Nagasubramanian, Rhodehamel, Michael
• Granted Patent: US 7,802,083 B2
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06F 8/65 Updates security arrangements therefor G06F21/57
• CPC Class Codes: G06F 9/505 considering the load

Abstract

Methods, apparatuses, articles, and systems for performing an installation by a client system at a time when the client system is predicted to be below a level, are disclosed. The installation may be a software or a patch. In various embodiments, the methods, apparatus et al may include performance of the adaptive prediction, and the adaptive prediction may be performed by a learning algorithm. In other embodiments, the methods et al may also develop a model of the client system'"'"'s utilization by observing and recording metrics of hardware and software utilization over time.

Claims

1. A method comprising:

receiving, by a computer system, an instruction to perform an installation; and

performing, by the computer system, the installation at a selected time, the selected time determined based at least in part on a time when the computer system'"'"'s utilization is predicted to be below a utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the utilization level is adaptively performed.

2. The method of claim 1, wherein the adaptive prediction comprises modeling the computer system'"'"'s utilization by a learning algorithm.

3. The method of claim 2 wherein the model includes at least one usage pattern selected from the group consisting of a range of times when the computer system is on, a range of times when the computer system is off, a range of times when the computer system is in deep sleep, a range of times when the computer system is in soft sleep, a range of times when the central processor unit utilization is above a level, a range of times when the central processor unit utilization is between a range, a range of times when the central processor unit utilization is below a level, a range of times when the computer system memory utilization is above a level, a range of times when the computer system memory utilization is between a range, a range of times when the computer system memory utilization is below a level, a range of times when the computer system network utilization is above a level, a range of times when the computer system network utilization is between a range, a range of times when the computer system network utilization is below a level, a range of times when user interactivity is above a level, a range of times when user interactivity is between a range, and a range of times when user interactivity is below a level.

4. The method of claim 2, further comprising the learning algorithm collecting data relating to at least one metric of the computer system'"'"'s utilization to model the computer system'"'"'s utilization.

5. The method of claim 4 wherein the at least one metric is selected from the group consisting of incidents of power up, incidents of power down, incidents of soft sleep, incidents of deep sleep, incidents of reboot, central processing unit utilization, hard disk utilization, network utilization, and memory utilization.

6. The method of claim 4 further comprising the learning algorithm learning for an initial learning period of a pre-set length of time.

7. The method of claim 1, further comprising specifying the utilization level of the computer system.

8. The method of claim 1 further comprising:

receiving, by the computer system, an instruction to download a software installation; and

performing, by the computer system, the download at another selected time, the other selected time determined based at least in part on a time when the computer system'"'"'s utilization is predicted to be below the same or another utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the same or another utilization level is adaptively performed.

9. The method of claim 1 wherein the determining of the selected time is further based on the computer system'"'"'s current utilization.

10. The method of claim 1 wherein the receiving of an instruction comprises receiving an instruction to perform an installation accompanied by at least one installation parameter selected from the group consisting of whether a reboot is required, whether performing the installation is critical, and an approximate time to perform the installation.

11. The method of claim 10 wherein the determining of the selected time is further based on the at least one installation parameter.

12. The method of claim 1 wherein the determining of the selected time is further based on whether the installation involves patching a file in use by the computer system.

13. The method of claim 1 further comprising:

scheduling, by the computer system, a wake up of the computer system at the selected time if the computer is predicted to be in an off state, a soft sleep state, or a deep sleep state at the selected time.

14. The method of claim 1 further comprising:

setting, by the computer system, states of a management microcontroller of the computer system, the management microcontroller being separate and distinct from a central processing unit of the computer system, to enable the management microcontroller to wake the central processing unit up at the selected time, the selected time being when the central processing unit is predicted to be in an off state, a soft sleep state, or a deep sleep state.

15. An apparatus comprising:

a processor; and

an installation module operated by the processor and adapted to;

receive an instruction to perform an installation; and

perform the installation at a selected time, the selected time determined based at least in part on a time when a computer system'"'"'s utilization is predicted to be below a utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the utilization level is adaptively performed, said adaptive prediction including modeling the computer system'"'"'s utilization by a learning algorithm.

16. The apparatus of claim 15, wherein the learning algorithm is adapted to collect data relating to at least one metric of the computer system'"'"'s utilization to model the computer system'"'"'s utilization.

17. The apparatus of claim 16, wherein the at least one metric is selected from the group consisting of incidents of power up, incidents of power down, incidents of soft sleep, incidents of deep sleep, incidents of reboot, central processing unit utilization, hard disk utilization, network utilization, and memory utilization.

18. The apparatus of claim 15 wherein the installation module is further adapted to:

receive an instruction to download a software installation; and

perform the download at another selected time, the other selected time determined based at least in part on a time when the computer system'"'"'s utilization is predicted to be below the same or another utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the same or another utilization level is adaptively performed.

19. The apparatus of claim 15 wherein the selected time is further determined based on at least one installation parameter received with the installation instruction.

20. The apparatus of claim 15 wherein the selected time is further determined based on whether the installation involves patching a file in use.

21. The apparatus of claim 15 wherein the installation module is further adapted to schedule a wake up of the computer system at the selected time if the computer is predicted to be in an off state, a soft sleep state, or a deep sleep state at the selected time.

22. An article of manufacture comprising:

a storage medium; and

a plurality of programming instructions stored on the storage medium, and designed to program an apparatus to implement an installation module to;

receive an instruction to perform an installation; and

perform the installation at a selected time, the selected time determined based at least in part on a time when a computer system'"'"'s utilization is predicted to be below a utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the utilization level is adaptively performed, said adaptive prediction including modeling the computer system'"'"'s utilization by a learning algorithm, the learning algorithm adapted to collect data relating to at least one metric of the computer system'"'"'s utilization to model the computer system'"'"'s utilization.

23. The article of claim 22 wherein the installation module is further designed to:

receive an instruction to download a software installation; and

perform the download at another selected time, the other selected time determined based at least in part on a time when the computer system'"'"'s utilization is predicted to be below the same or another utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the same or another utilization level is adaptively performed.

24. The article of claim 22 wherein the model includes at least one usage pattern selected from the group consisting of a range of times when the computer system is on, a range of times when the computer system is off, a range of times when the computer system is in deep sleep, a range of times when the computer system is in soft sleep, a range of times when the central processor unit utilization is above a level, a range of times when the central processor unit utilization is between a range, a range of times when the central processor unit utilization is below a level, a range of times when the computer system memory utilization is above a level, a range of times when the computer system memory utilization is between a range, a range of times when the computer system memory utilization is below a level, a range of times when the computer system network utilization is above a level, a range of times when the computer system network utilization is between a range, and a range of times when the computer system network utilization is below a level.

25. The article of claim 22 wherein the selected time is further determined based on at least one installation parameter received with the installation instruction, said at least one installation parameter selected from the group consisting of whether a reboot is required, whether performing the installation is critical, and the approximate time to perform the installation.

26. A system comprising:

a processor;

a mass storage device coupled to the processor, the mass storage device storing a plurality of programming instructions designed to implement an installation module, the installation module adapted to;

receive an instruction to perform an installation; and

perform the installation at a selected time, the selected time determined based at least in part on a time when a computer system'"'"'s utilization is predicted to be below a utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the utilization level is adaptively performed, said adaptive prediction including modeling the computer system'"'"'s utilization by a learning algorithm, the learning algorithm adapted to collect data relating to at least one metric of the computer system'"'"'s utilization to model the computer system'"'"'s utilization; and

a management microcontroller coupled to the processor, the management microcontroller adapted to receive from the installation module states to enable the management microcontroller to wake the processor up at the selected time.

27. The system of claim 26 wherein the installation module is further designed to:

receive an instruction to download a software installation; and

perform the download at another selected time, the other selected time determined based at least in part on a time when the computer system'"'"'s utilization is predicted to be below the same or another utilization level, and the predicting of the time when the computer system'"'"'s utilization will be below the same or another utilization level is adaptively performed.

28. The system of claim 26 wherein the at least one metric is selected from the group consisting of incidents of power up, incidents of power down, incidents of soft sleep, incidents of deep sleep, incidents of reboot, central processing unit utilization, hard disk utilization, network utilization, and memory utilization.

29. The system of claim 26 wherein the model includes at least one usage pattern selected from the group consisting of a range of times when the computer system is on, a range of times when the computer system is off, a range of times when the computer system is in deep sleep, a range of times when the computer system is in soft sleep, a range of times when the central processor unit utilization is above a level, a range of times when the central processor unit utilization is between a range, a range of times when the central processor unit utilization is below a level, a range of times when the computer system memory utilization is above a level, a range of times when the computer system memory utilization is between a range, a range of times when the computer system memory utilization is below a level, a range of times when the computer system network utilization is above a level, a range of times when the computer system network utilization is between a range, and a range of times when the computer system network utilization is below a level.

30. The system of claim 26 wherein the installation module is further designed to set states of the management microcontroller to enable the management microcontroller to wake the processor up at the selected time, the selected time being when the processor is predicted to be in an off state, a soft sleep state, or a deep sleep state.