SOFTWARE VIRTUAL MACHINE FOR CONTENT DELIVERY

• US 20120324448A1
• Filed: 06/15/2012
• Published: 12/20/2012
• Est. Priority Date: 06/16/2011
• Status: Active Grant

• Current Assignee: Argyle Data, Inc. (Mavenir Group Holdings Ltd.)
• Original Assignee: uCIRRUS Corp.
• Inventors: Huetter, Raymond J., Yamarti, Alka
• Granted Patent: US 8,645,958 B2
• Time in Patent Office: Days
• US Class Current: 718/1
• CPC Class Codes: G06F 2209/5017 Task decomposition
• CPC Class Codes: G06F 9/5066 Algorithms for mapping a plurality of inter-dependent sub-tasks onto a plurality of physical CPUs mappping at compile time, see G06F8/451

Abstract

In general, this disclosure is directed to a software virtual machine that provides high-performance transactional data acceleration optimized for multi-core computing platforms. The virtual machine utilizes an underlying parallelization engine that seeks to maximize the efficiencies of multi-core computing platforms to provide a highly scalable, high performance (lowest latency), virtual machine. In some embodiments, the virtual machine may be viewed as an in-memory virtual machine with an ability in its operational state to self organize and self seek, in real time, available memory work boundaries to automatically optimize maximum available throughput for data processing acceleration and content delivery of massive amounts of data.

Claims

1. A device comprising:

a multi-core hardware processor having a plurality of execution cores;

an in-memory database comprising data stored within a plurality of memory partitions, wherein each of the memory partitions is associated with a different one of the execution cores;

a content delivery engine that configures a plurality of private data channels to each deliver unique data to a corresponding one of a plurality of data consumers; and

a parallelization engine that deploys a plurality of tasks to concurrently execute on the cores to concurrently perform transactions on the in-memory database in response to queries from the content delivery engine.

2. The device of claim 1, further comprising:

a plurality of virtual machines that each operate in accordance with a virtual instruction set, wherein each of the virtual machines executes on a different one of the execution cores; and

a statement execution task that executes on one of the virtual machines to apply an assignment algorithm to the sub-transactions to identify the respective execution cores associated with the respective memory partitions that store data for the sub-transactions,

wherein the statement execution task spawns and deploys, for each of the sub-transactions, a sub-execution task to execute the sub-transaction on the identified one of the execution cores for the sub-transaction.

3. The device of claim 1,

wherein each of the virtual machines comprises a scheduler for the corresponding execution core, and

wherein each of the schedulers maintains a separate run list data structure that stores references to tasks scheduled for execution by the corresponding one of the plurality of execution cores.

4. The device of claim 1,

wherein the content delivery engine presents an application programming interface (API) that allows data consumers to specify a continuous query that directs the content delivery engine to continuously apply the continuous query to data newly received by the device.

5. The device of claim 1,

wherein the content delivery engine receives a continuous query that directs the content delivery engine to continuously apply the continuous query to data newly received by the device.

6. The device of claim 5, wherein the continuous query specifies a future token.

7. The device of claim 6,

wherein the content delivery engine receives a first transaction that requests first data and a second transaction that requests second data, and

wherein the content delivery engine computes an aggregate transaction that requests the first data and the second data and outputs the aggregate transaction to another device.

8. The device of claim 6,

wherein the content delivery engine receives an aggregate transaction requesting first data and second data and directs the parallelization engine to execute an aggregate transaction to aggregate the first data and second data, and

wherein the content delivery engine outputs the first data and the second data aggregated by the parallelization engine in response to the aggregate transaction.

9. The device of claim 8,

wherein the plurality of tasks decomposes the aggregate transaction into a plurality of sub-transactions, concurrently execute the sub-transactions to retrieve the first data and the second data from the in-memory database, and return the first data and second data to the content delivery engine.

10. A method comprising:

configuring, with a content delivery engine, a plurality of private data channels to each deliver unique data to a corresponding one of a plurality of data consumers; and

deploying, with a parallelization engine executing on a multi-core hardware processor having a plurality of execution cores, a plurality of tasks to concurrently execute on the cores to concurrently perform transactions on an in-memory database in response to queries from the content delivery engine, wherein the in-memory database comprises data stored within a plurality of memory partitions, wherein each of the memory partitions is associated with a different one of the execution cores.

11. The method of claim 10, further comprising:

executing a plurality of virtual machines that each operate in accordance with a virtual instruction set, wherein each of the virtual machines executes on a different one of the execution cores of the multi-core hardware processor; and

applying, with a statement execution task executing on one of the virtual machines, an assignment algorithm to the sub-transactions to identify the respective execution cores associated with the respective memory partitions that store data for the sub-transactions,

wherein the statement execution task spawns and deploys, for each of the sub-transactions, a sub-execution task to execute the sub-transaction on the identified one of the execution cores for the sub-transaction.

12. The method of claim 10,

wherein each of the virtual machines comprises a scheduler for the corresponding execution core, and

wherein each of the schedulers maintains a separate run list data structure that stores references to tasks scheduled for execution by the corresponding one of the plurality of execution cores.

13. The method of claim 10,

wherein the content delivery engine presents an application programming interface (API) that allows data consumers to specify a continuous query that directs the content delivery engine to continuously apply the continuous query to newly received data.

14. The method of claim 10, further comprising:

receiving, with the content delivery engine, a continuous query that directs the content delivery engine to continuously apply the continuous query to data newly received by the device.

15. The method of claim 14, wherein the continuous query specifies a future token.

16. The method of claim 15, further comprising:

receiving, with the content delivery engine, a first transaction that requests first data and a second transaction that requests second data;

computing, with the content delivery engine, an aggregate transaction that requests the first data and the second data; and

outputting the aggregate transaction to another device.

17. The method of claim 15,

receiving, with the content delivery engine, an aggregate transaction requesting first data and second data;

directing the parallelization engine to execute an aggregate transaction to aggregate the first data and second data; and

outputting, with the content delivery engine, the first data and the second data aggregated by the parallelization engine in response to the aggregate transaction.

18. The method of claim 17,

wherein the plurality of tasks decomposes the aggregate transaction into a plurality of sub-transactions, concurrently execute the sub-transactions to retrieve the first data and the second data from the in-memory database, and return the first data and second data to the content delivery engine.

19. A computer-readable storage device comprising instructions that, when executed, cause a multi-core hardware processor having a plurality of execution cores to:

configure, with a content delivery engine, a plurality of private data channels to each deliver unique data to a corresponding one of a plurality of data consumers; and

deploy, with a parallelization engine, a plurality of tasks to concurrently execute on the cores to concurrently perform transactions on an in-memory database in response to queries from the content delivery engine, wherein the in-memory database comprises data stored within a plurality of memory partitions, wherein each of the memory partitions is associated with a different one of the execution cores.

20. The computer-readable storage device of claim 19, wherein the instructions, when executed, further cause the multi-core hardware processor to:

execute a plurality of virtual machines that each operate in accordance with a virtual instruction set, wherein each of the virtual machines executes on a different one of the plurality of execution cores; and

execute a statement execution task on one of the virtual machines to apply an assignment algorithm to the sub-transactions to identify the respective execution cores associated with the respective memory partitions that store data for the sub-transactions,

wherein the statement execution task spawns and deploys, for each of the sub-transactions, a sub-execution task to execute the sub-transaction on the identified one of the execution cores for the sub-transaction.