METHOD FOR ENABLING AN APPLICATION TO RUN ON A CLOUD COMPUTING SYSTEM

• US 20140245319A1
• Filed: 02/27/2014
• Published: 08/28/2014
• Est. Priority Date: 02/27/2013
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: GreenButton Ltd. (Microsoft Corporation)
• Inventors: FELLOWS, David Emerson
• Application Number:
  US14/192,483
• Publication Number:
  US 20140245319A1
• Time in Patent Office: Days
• US Class Current: 718/104
• CPC Class Codes: G06F 8/45 Exploiting coarse grain parallelism in compilation, i.e. parallelism between groups of instructions
• CPC Class Codes: G06F 9/5072 Grid computing

Abstract

A method for enabling an application to run on a cloud computing system so that jobs that may be computed without having to modify the application. The method includes the step of programming a task processor that relates the parameters of each task of the job to the arguments that need to be passed to an application executable on a compute node in the cloud computing system that is used to process the task. The task processor runs on any compute node in the cloud computing system.

A method for computing jobs on a cloud computing system. The method includes the steps of: splitting the job into one or more tasks; transmitting a task to a compute node within the cloud computing system; identifying the job type of the task transmitted to the compute note; and using a task processor to call an executable process using suitable arguments based on the parameters of the task.

Claims

1. A computer implemented method for enabling an application to run on a cloud computing system so that jobs that may be computed by the application can be computed on the cloud computing system without having to modify the application, and wherein the jobs consist of one or more tasks with each task having parameters that define the scope of the task, including the step of:

a. using a local computer to program a task processor that relates the parameters of each task to the arguments that need to be passed to an application executable on a compute node in the cloud computing system that is used to process the task,

wherein the task processor runs on any compute node in the cloud computing system.

2. The method as claimed in claim 1, further including the step of:

a. using a local computer to program a splitting algorithm adapted to split the jobs into tasks that can then be processed by compute nodes in the cloud computing system,

wherein the cloud computing system is runs the splitting algorithm.

3. The method as claimed in claim 2, wherein the method includes the step of uploading the application, splitting algorithm and task processor to the cloud computing system from the local computer.

4. The method as claimed in claim 2, wherein the cloud computer system includes an external API host runs the splitting algorithm and manage the application on the cloud computing system.

5. The method as claimed in claim 2, wherein the compute nodes in the cloud computer system include a middleware layer that is adapted to provide a consistent interface for the task processor independent from the underlying structure of the compute node.

6. The method as claimed in claim 1, wherein the job is a rendering job and the parameters that define the scope of the tasks include frame numbers.

7. A computer implemented method for computing jobs on a cloud computing system, wherein the jobs are of a job type and the cloud computing system is adapted to compute jobs of the job type, and wherein the jobs are associated with an application, including the steps of:

a. splitting the job into one or more tasks, wherein each task is of the job type and includes parameters defining the scope of the task;

b. transmitting a task to a compute node within the cloud computing system;

c. identifying the job type of the task transmitted to the compute note; and

d. using a task processor on the compute node to call an executable process on the compute node based on the identified job type using suitable arguments based on the parameters of the task.

8. The method as claimed in claim 7, including the step of using a splitting algorithm to split the job.

9. The method as claimed in claim 8, including the step of submitting the job from a user local computer to the cloud computing system.

10. The method as claimed in claim 7, including the step of using the application on a local computer to split the job.

11. The method as claimed in claim 10, including the step of submitting the one or more tasks from a user local computer to the cloud computing system.

12. The method as claimed in claim 9, wherein the cloud computing system is adapted to identify the job type of the job after it has been submitted to the cloud computing system from a user local computer.

13. The method as claimed in claim 8, wherein the splitting algorithm is adapted for jobs of the job type.

14. The method as claimed in claim 7, wherein the job is a workload from the application.

15. The method as claimed in claim 7, wherein the task processor is adapted for tasks of the job type.

16. The method as claimed in claim 7, wherein the compute node includes a middleware layer that is adapted to provide a consistent interface for the task processor independent from the underlying structure of the compute node.

17. The method as claimed in claim 7, including the step of provisioning a plurality of compute nodes within the cloud computing system.

18. The method as claimed in claim 17, wherein the step of provisioning the plurality of compute nodes includes downloading the task processor from a storage facility on the cloud computing system to each of the plurality of compute nodes.

19. The method as claimed in claim 18, including the step of allocating tasks between the plurality of compute nodes according to a prioritisation logic.

20. The method as claimed in claim 7, including the step of downloading the application from a storage facility on the cloud computing system to the compute node.

21. The method as claimed in claim 7, including the step of processing the transmitted task on the compute node producing one or more task outputs.

22. The method as claimed in claim 21, including the step of compiling or further processing the task outputs for each of the plurality of tasks after they have been processed to produce a job output.

23. The method as claimed in claim 7, wherein the job is a rendering job and the parameters that define the scope of the tasks include frame numbers.