Method and system for preloading resources

• US 8,539,038 B2
• Filed: 02/17/2011
• Issued: 09/17/2013
• Est. Priority Date: 12/12/2001
• Status: Active Grant

• Current Assignee: Valve Corporation
• Original Assignee: Valve Corporation
• Inventors: Jones, Paul David, Newcombe, Christopher Richard, Ellis, Richard Donald, Birum, Derrick Jason, Thompson, Mikel Howard
• Primary Examiner(s): Nguyen, Thuong
• Application Number:
  US13/030,103
• Publication Number:
  US 20110145362A1
• Time in Patent Office: 943 Days
• Field of Search: 709/203, 709/219, 709/232, 711/137, 711/158, 707/3
• US Class Current: 709/217
• CPC Class Codes: H04L 2463/101 applying security measures for digital rights management data processing systems or methods, specially adapted for commerce, e.g. marketing, shopping, billing, auctions or e-commerce G06Q30/00
• CPC Class Codes: H04L 2463/121 Timestamp cryptographic mechanisms or cryptographic arrangements involving time stamps H04L9/3297
• CPC Class Codes: H04L 41/0896 Bandwidth or capacity management, i.e. automatically increasing or decreasing capacities flow or congestion control using dynamic resource allocation, e.g. in-call renegotiation, H04L47/76
• CPC Class Codes: H04L 61/2514 between local and global IP addresses
• CPC Class Codes: H04L 63/0428 wherein the data content is protected, e.g. by encrypting or encapsulating the payload
• CPC Class Codes: H04L 63/08 for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 63/0807 using tickets, e.g. Kerberos cryptographic mechanisms or cryptographic arrangements for entity authentication using tickets or tokens H04L9/3213
• CPC Class Codes: H04L 63/0846 using time-dependent-passwords, e.g. periodically changing passwords
• CPC Class Codes: H04L 63/123 received data contents, e.g. message integrity
• CPC Class Codes: H04L 63/168 above the transport layer
• CPC Class Codes: H04L 67/01 Protocols
• CPC Class Codes: H04L 67/02 based on web technology, e.g. hypertext transfer protocol [HTTP]
• CPC Class Codes: H04L 67/06 specially adapted for file transfer, e.g. file transfer protocol [FTP]
• CPC Class Codes: H04L 67/1001 for accessing one among a plurality of replicated servers
• CPC Class Codes: H04L 67/1008 based on parameters of servers, e.g. available memory or workload monitoring of computer activity G06F11/30
• CPC Class Codes: H04L 67/289 Intermediate processing functionally located close to the data consumer application, e.g. in same machine, in same home or in same sub-network
• CPC Class Codes: H04L 67/306 User profiles
• CPC Class Codes: H04L 67/34 involving the movement of software or configuration parameters network booting or remote initial program loading [RIPL] G06F9/4416
• CPC Class Codes: H04L 67/51 Discovery or management thereof, e.g. service location protocol [SLP] or web services
• CPC Class Codes: H04L 67/53 using third party service providers
• CPC Class Codes: H04L 67/55 : Push-based network services
• CPC Class Codes: H04L 67/564 : Enhancement of application control based on intercepted application data
• CPC Class Codes: H04L 67/5681 : Pre-fetching or pre-delivering data based on network characteristics
• CPC Class Codes: H04L 67/62 : Establishing a time schedule for servicing the requests
• CPC Class Codes: H04L 69/329 : in the application layer [OSI layer 7]
• CPC Class Codes: H04L 9/40 : Network security protocols

Abstract

A method and system for preloading data is provided. Resources are preloaded on a client before they are needed by an application. Application hints and static resources lists may be used to indicate resources are needed. The application may begin execution before receiving all of the resources associated with the application. Preloading may be done during execution of an application, or when the application is not executing. Preloading allows an application to run smoothly without consuming more resources than are needed by an application. A prediction graph may be generated that is used in predicting the resources that are likely to be needed by the application. An analyzer may also be used to simulate the system and adjust parameters used in creating the prediction graph.

Claims

1. A method for preloading resources for an application on a computing device, comprising:

determining a resource list to preload onto the computing device, wherein determining the list of resources, further comprises;

determining a plurality of N-order Markov Chains based on a plurality of application hints associated with a previous execution of the application;

combining the plurality of N-order Markov Chains into a prediction graph based on at least one of a subsequence matching algorithm; and

processing each common subsequence of the N-order Markov Chains to reduce the number of nodes and edges in the prediction graph and to constrain a size of the prediction graph based on identified common subsequences of the N-order Markov chains, the prediction graph being useable to generate the list of resources; and

employing the resource list to preload at least one resource onto the computing device before the application accesses the at least one resource;

in response to receiving an application hint associated with the application, dynamically changing the resource list; and

preloading the computing device with the resources based on the changed resource list.

2. The method of claim 1, wherein if the resources are located on another computing device, downloading the resources from the other computing device, and storing the downloaded resources locally on the computing device, and obtaining the resources locally if the resources are stored on the computing device.

3. The method of claim 1, wherein the application is configured to execute with a subset of the resources associated with the application.

4. The method of claim 1, further comprising adding within the resource list another resource to preload based on the application hint, the other resource to preload being added to the resource list at a location within the resource list based on a priority associated with the resource.

5. The method of claim 1, wherein the application hint is based on at least one of a current state of the application, a next state of the application, a heuristic, or a next game level associated with the application.

6. The method of claim 1, further comprising modifying a priority of at least one resource within the list of resources based in part on the application hint.

7. A computing device for preloading resources for an application, comprising:

a network interface component that is configured to communicate data over a network;

a memory that is configured to store at least instructions;

a processor, in communication with the network interface component and the memory, that executes the instructions to enable actions, including;

determining a resource list to preload onto the computing device, wherein determining the list of resources, further comprises;

determining a plurality of N-order Markov Chains based on a plurality of application hints associated with a previous execution of the application;

combining the plurality of N-order Markov Chains into a prediction graph based on at least one of a subsequence matching algorithm; and

processing each common subsequence of the N-order Markov Chains to reduce the number of nodes and edges in the prediction graph and to constrain a size of the prediction graph based on identified common subsequences of the N-order Markov chains, the prediction graph being useable to generate the list of resources; and

employing the resource list to preload at least one resource onto the computing device before the application accesses the at least one resource;

in response to receiving an application hint associated with the application, dynamically changing the resource list; and

preloading the computing device with the resources based on the changed resource list.

8. The computing device of claim 7, wherein the actions further include if the resources are located on another computing device, downloading the resources from the other computing device, and storing the downloaded resources locally on the computing device, and obtaining the resources locally if the resources are stored on the computing device.

9. The computing device of claim 7, wherein the actions further include identifying which resource on the resource list to be loaded next into the memory local to the computing device.

10. The computing device of claim 7, wherein the actions further include adding within the resource list another resource to preload based on the application hint, the other resource to preload being added to the resource list at a location within the resource list based on a priority associated with the resource.

11. The computing device of claim 7, wherein the application is configured to execute with a subset of the resources associated with the application.

12. The computing device of claim 7, wherein the resource includes at least one of a dynamic link library, an executable file, a data file, a profile file, or a configuration file.

13. The computing device of claim 7, wherein the actions further comprise modifying information associated with at least one resource local to the computing device such that a likelihood of purging the at least one local resource from the computing device is reduced.

14. The computing device of claim 7, wherein the application hint is one of a plurality of received application hints, and wherein revising the list of resources is based at least in part on an ordering of the plurality of application hints.

15. The computing device of claim 7, the actions further comprise modifying a priority of at least one resource within the list of resources based in part on the application hint.

16. The computing device of claim 7, wherein the application hint is based on at least one of a current state of the application, a next state of the application, a heuristic, or a next game level associated with the application.

17. A processor readable non-transitive storage media that includes instructions for preloading resources for an application, wherein a processor that executes the instructions enables actions, comprising:

determining a resource list to preload onto the computing device, wherein determining the list of resources, further comprises;

determining a plurality of N-order Markov Chains based on a plurality of application hints associated with a previous execution of the application;

combining the plurality of N-order Markov Chains into a prediction graph based on at least one of a subsequence matching algorithm; and

processing each common subsequence of the N-order Markov Chains to reduce the number of nodes and edges in the prediction graph and to constrain a size of the prediction graph based on identified common subsequences of the N-order Markov chains, the prediction graph being useable to generate the list of resources; and

employing the resource list to preload at least one resource onto the computing device before the application accesses the at least one resource;

in response to receiving an application hint associated with the application, dynamically changing the resource list; and

preloading the computing device with the resources based on the changed resource list.

18. The media of claim 17, wherein if the resources are located on another computing device, downloading the resources from the other computing device, and storing the downloaded resources locally on the computing device, and obtaining the resources locally if the resources are stored on the computing device.

19. The media of claim 17, wherein the application is configured to execute with a subset of the resources associated with the application.

20. The media of claim 17, wherein the actions further comprise adding within the resource list another resource to preload based on the application hint, the other resource to preload being added to the resource list at a location within the resource list based on a priority associated with the resource.

21. The media of claim 17, wherein the application hint is based on at least one of a current state of the application, a next state of the application, a heuristic, or a next game level associated with the application.

22. The media of claim 17, wherein the actions further comprise modifying a priority of at least one resource within the list of resources based in part on the application hint.

23. A system for preloading resources for an application over a network, comprising:

a computing device, including;

a network interface component that is configured to communicate data over the network;

a memory that is configured to store at least instructions;

a processor, in communication with the network interface component and the memory, that executes the instructions to enable actions, including;

determining a resource list to preload onto the computing device, wherein determining the list of resources, further comprises;

determining a plurality of N-order Markov Chains based on a plurality of application hints associated with a previous execution of the application;

combining the plurality of N-order Markov Chains into a prediction graph based on at least one of a subsequence matching algorithm; and

processing each common subsequence of the N-order Markov Chains to reduce the number of nodes and edges in the prediction graph and to constrain a size of the prediction graph based on identified common subsequences of the N-order Markov chains, the prediction graph being useable to generate the list of resources; and

employing the resource list to preload at least one resource onto the computing device before the application accesses the at least one resource;

in response to receiving an application hint associated with the application, dynamically changing the resource list; and

preloading the computing device with the resources based on the changed resource list; and

another computing device, comprising;

another network interface component that is configured to communicate data;

another memory that is configured to store at least instructions;

another processor, in communication with the network interface component and the memory, that is configured to execute the instructions to enable actions, including;

providing the computing device with access to preload at least one resource on the resource list; and

downloading the at least one resource to the computing device.

24. The system of claim 23, wherein the actions further include if the resources are located on the other computing device, downloading and storing the downloaded resources locally on the computing device, and obtaining the resources locally if the resources are stored on the computing device.

25. The system of claim 23, wherein the actions further include identifying which resource on the resource list to be loaded next into the memory local to the computing device.

26. The system of claim 23, wherein the actions further include adding within the resource list another resource to preload based on the application hint, the other resource to preload being added to the resource list at a location within the resource list based on a priority associated with the resource.

27. The system of claim 23, wherein the application is configured to execute with a subset of the resources associated with the application.

28. The system of claim 23, the actions further comprise modifying a priority of at least one resource within the list of resources based in part on the application hint.

29. The system of claim 23, wherein the application hint is based on at least one of a current state of the application, a next state of the application, a heuristic, or a next game level associated with the application.