Utilization based installation on a computing system

• US 7,802,083 B2
• Filed: 12/20/2006
• Issued: 09/21/2010
• Est. Priority Date: 12/20/2006
• Status: Active Grant

• Current Assignee: Intel Corporation
• Original Assignee: Intel Corporation
• Inventors: Gurumoorthy, Nagasubramanian, Rhodehamel, Michael, Kumar, Arvind
• Primary Examiner(s): ELAMIN, ABDELMONIEM I
• Application Number:
  US11/613,317
• Publication Number:
  US 20080154805A1
• Time in Patent Office: 1,371 Days
• Field of Search: 713/1, 713/2, 709/223, 709/224, 717/120, 717/170, 717176-178
• US Class Current: 713/1
• 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 install a software on the computer system, the instruction including information related to estimated amount of time needed to download and install the software, whether restart of the computer system is to be required after the installation, and a level indicative of criticality of the software;

downloading, by the computing system, the software to be installed at a first time based at least in part on a first prediction by the computer system that the computer system'"'"'s utilization at the first time is to be below a first utilization level;

installing, by the computer system, the software at a second time based at least in part on a second prediction by the computer system that the computer system'"'"'s utilization at the second time is to be below a second utilization level, and the first and the second predictions are adaptively performed based on a rule set including aggregated usage patterns of the computer system.

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 wherein the determining of the selected time is further based on the computer system'"'"'s current utilization.

8. The method of claim 1 wherein the determining of the selected time is further based on the received installation instruction including information related to estimated amount of time needed to download and install the software, whether restart of the computer system is to be required after the installation, and a level indicative of criticality of the software.

9. 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.

10. 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.

11. 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.

12. An apparatus comprising:

a processor; and

an installation module operated by the processor and adapted to;

receive an instruction to install a software on a computer system, the instruction including information related to estimated amount of time needed to download and install the software, whether restart of the computer system is to be required after the installation, and a level indicative of criticality of the software;

download the software to be installed at a first time based at least in part on a first prediction by the apparatus that the utilization of the computer system at the first time is to be below a first utilization level;

installing the software at a second time based at least in part on a second prediction by the apparatus that the computer system'"'"'s utilization at the second time is to be below a second utilization level, and the first and second predictions are adaptively performed, said adaptive prediction including modeling the computer system'"'"'s utilization by a learning algorithm.

13. The apparatus of claim 12, 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.

14. The apparatus of claim 13, 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.

15. The apparatus of claim 12 wherein the selected time is further determined based on the received installation instruction including information related to estimated amount of time needed to download and install the software, whether restart of the computer system is to be required after the installation, and a level indicative of criticality of the software.

16. The apparatus of claim 12 wherein the selected time is further determined based on whether the installation involves patching a file in use.

17. The apparatus of claim 12 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.

18. An article of manufacture comprising:

a tangible computer-accessible storage medium; and

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

receive an instruction to install a software on a computer system, the instruction including information related to estimated amount of time needed to download and install the software, whether restart of the computer system is to be required after the installation, and a level indicative of criticality of the software;

download the software to be installed at a first time based at least in part on a first prediction by the apparatus that the utilization of the computer system at the first time is to be below a first utilization level;

install the software on the apparatus at a second time based at least in part on a second prediction by the apparatus that the computer system'"'"'s utilization at the second time is to be below a second utilization level, and the first and the second predictions are 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.

19. The article of claim 18 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.

20. The article of claim 18 wherein the selected time is further determined based on the received installation instruction including information related to estimated amount of time needed to download and install the software, whether restart of the computer system is to be required after the installation, and a level indicative of criticality of the software.

21. 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 install a software, the instruction including information related to estimated amount of time needed to download and install the software, whether restart of a computer system is to be required after the installation, and a level indicative of criticality of the software;

download the software to be installed at a first time based at least in part on a first prediction by the apparatus that the utilization of the computer system at the first time is to be below a first utilization level;

install the software at a second time based at least in part on a second prediction by the apparatus that the computer system'"'"'s utilization at the second time is to be below a second utilization level, and the first and the second predictions are 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.

22. The system of claim 21 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.

23. The system of claim 21 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.

24. The system of claim 21 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.